Non-invasive glucose monitoring -
But are they close to hitting the shelves? Diabetics would certainly hope so. Getting glucose measurements without drawing blood would make managing their condition much easier, whether they need continuous glucose monitoring or not. So, before decidingdrawing, read this comprehensive guide to learn how non-invasive blood glucose monitoring works, its effectiveness, and more.
For diabetic patients, monitoring blood glucose levels is part of their daily routine. Unfortunately, determining the blood glucose value at home using the standard devices may result in certain side effects, such as:. This condition can make both blood glucose monitoring and diabetes treatment more difficult for those who are insulin-dependent.
The two most common types of permanent blood sugar monitoring devices are:. Unfortunately, device cost, health insurance status, and socioeconomic backgrounds create a firm barrier between most diabetics and these devices. As a result, glucose meters are more widespread because they are relatively affordable, simple to use, and provide fairly accurate blood sugar readings.
There are several types of noninvasive blood glucose monitoring systems available that take widely different approaches. Optical methods provide glucose measurements by analyzing how a light beam is absorbed or scattered through tissue.
There are several avenues such devices use to get their readings, but their core principle comes from the notion that glucose levels change the way light is reflected from human skin.
Think of this type of non-invasive glucose monitor as a smartwatch you wear on your wrist. These measure blood glucose levels by analyzing a sample taken in a non-invasive manner, such as sweat, tears, urine, or saliva.
The measurement process with these devices is different depending on the specific approach they take for detection. Here are a few possible examples:. The most common minimally invasive blood glucose monitoring device is the CGM.
It involves getting a small glucose sensor inserted under the skin, either through a patch containing a microneedle or an implantable device. The CGM measures the blood glucose from the interstitial fluid, which is found between the cells, in real-time.
Patients can view their blood glucose levels, get warnings through a smartphone app if their levels are too low or high, and get charts to monitor their overall diabetes management.
However, the sensors in CGM devices need to be changed once every days, depending on the specifications of the product. Implantable sensors may last longer.
Wearable devices today can offer you essential wellness information, from heart rate to blood pressure and even blood oxygen levels.
So far, the results have been mixed regarding blood sugar for both devices designed for personal use and clinical settings.
John L. Smith, a well-known consultant and chemist, has been watching this industry for decades and documenting the attempts at non-invasive devices in a book called "The Pursuit of Noninvasive Glucose" , which he updates every few years. One important note made by the author is that companies tend to over-promise the abilities of their new devices before they gather the data to back up their claims.
These ideas were confirmed by a Diabetes Technology Society analysis published in , which also warned that many of these claims are hyperbolic. But even with clearance and the clinical studies behind them, these devices still have a major problem showing accurate glucose concentrations in real time because of time delays.
There is a lag between the glucose levels in the blood and other samples used by non-invasive systems. For example, a CGM sensor that analyzes the interstitial fluid could show glucose levels with a 5 to minute delay when compared to finger-pricking methods.
The blood-saliva lag could be as long as 40 minutes , while for tear fluid, it can be around 15 minutes. For a healthy person, these delays are not significant, but people with a diabetes diagnosis may still need to use glucose meters to get real-time glucose concentrations.
A major benefit of these devices is that they help people take a more proactive approach to their diabetes management. For example, a CGM smartphone app could alert a person that their glucose concentration is too high or too low, in which case the patient could take immediate action before they began to feel the symptoms,.
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NID is limited. Figure 3 b summarises the study results colour coded by technique. The included studies compared NID or MID to different reference standards. Seven studies used capillary blood as the only reference standard, six compared MID or NID to capillary and venous blood and two studies had venous blood as the only reference standard.
Studies using venous blood as the reference standard indicated a higher sensitivity than studies using capillary blood as the reference standard venous capillary Yet, a significant difference in specificity could not be observed venous The likelihood-ratio test confirmed the result χ 2 : 9.
The corresponding SROC is displayed in Fig. As venous reference standard test, YSI Yellow springs instrument, YSI Inc, OH, USA was used in all studies except one Bay et al. Accu-Chek Roche , StatStrip Xpress Nova Biomedical , OneTouch Ultra 2 meter Onetouch. Pooled sensitivity was significantly higher in trials using venous blood as the reference standard, whereas the influence on pooled specificity was not significant.
The pooled sensitivity was significantly higher in studies investigating a larger study cohort. Most studies included a limited number of participants 2 studies investigated only 12 participants [ 27 , 28 ] and one study only 14 participants [ 20 ].
Yet, the cohort of the largest study included participants [ 21 ]. We investigated whether there is an association of study size with observed diagnostic accuracy.
low number of participants or single-centre: There was no significant effect on pooled specificity. Corresponding SROC is given in Fig. There was also a high variability in the number of paired measurements. One study relied on only 99 paired measurements [ 32 ], while the highest number of paired measurements was 16, [ 19 ].
small number of measurements: In many studies, in addition to sensitivity and specificity of hypoglycaemia detection, further parameters of accuracy were reported.
Ten studies described accuracy in terms of mean absolute relative difference MARD , which is a parameter that shows overall device accuracy over the whole glycaemic range. high MARD: An insignificant difference of mean specificity was also observed low MARD: Corresponding SROC is given in supplement 4.
In this context, also other parameters of accuracy, like the correlation coefficient and percentage of measurements in zones A and B of the Clarke Error Grid Analysis, showed a similar relationship with pooled sensitivity and specificity.
Covariates relating to the study setting were analysed. Here 1 artificial adjustment of blood glucose, 2 funding by manufacturers and 3 age of the study showed an influence on diagnostic accuracy.
no insulin administration: no-manufacturer-funding: Newer studies revealed a non-significantly higher sensitivity new studies: old studies: The location of the study hospital vs.
Interestingly, no association of participant characteristics including mean age, gender, proportion of participants with type 1 diabetes and BMI with pooled sensitivity and specificity was observed.
Two studies also included participants that did not have diabetes Lee et al. The sensitivity was notably lower in studies including patients without diabetes pooled sensitivity: Only pooling data of studies applying the threshold recommended by the American Diabetes Association resulted in a slightly higher pooled sensitivity Three studies investigated diagnostic accuracy for different thresholds simultaneously.
Inclusion of these data in additional meta-regression analyses showed that, as expected, higher cut-off values were associated with increased sensitivity and decreased specificity. A corresponding forest plot is given in supplement 5.
To investigate whether the findings of this systematic review are robust, sensitivity analyses were undertaken. As occasionally the quality of included studies was unsatisfactory, the influence of studies of poor quality on the results was analysed: Exclusion of studies with high risk of bias according to the QUADAS-2 tool did not have a notable influence on sensitivity.
Additionally, the performance of different devices was analysed. Ten out of the 15 studies reported on sensor stability.
All in all, the device failure rate is reported as high throughout the studies. In the study of Adolfsson et al. However, as this study investigated the diagnostic accuracy of CGMS Gold Medtronic in the context of diving, this may underestimate the actual stability in a normal setting.
Yet also, Hathout et al. Reasons for the high rate of device malfunction are not always discussed, but calibration and transmission failures are reported. Furthermore, side effects and adverse events of different devices were analysed. Six out of the 15 studies reported on side effects. Two studies reported the occurrence of no side effects or adverse events [ 24 , 29 ], whereas the rate of reported side effects was high in the other studies.
The highest number of side effects was seen by Hathout et. The studies from Christiansen et al. and Bode et al. Most of the side effects were instances of mild irritation, bleeding or discomfort. However, two more notable side effects were reported by Christiansen et al.
Those two events are rated as mild in severity due to small size and biocompatibility. Second, a device could not be removed in local anaesthesia as planned but general anaesthesia was required.
This event was adjudicated as serious [ 19 ]. In this presented work, some devices seemed to be more accurate than others. In this meta-analysis, Eversense Senseonics, Inc. Calibration is needed twice daily.
On the other hand, the sensor cannot be placed by the patients themselves but by a healthcare professional. The placement is more invasive than the procedure for other MID, and the rate of side effects of Eversense was higher and more serious compared to other MID.
The second highest accuracy in detection of hypoglycaemic events was seen in Dexcom G4 Platinum DexCom Inc, USA. However, contemplating the results of this meta-analysis, diagnostic accuracy showed a great variation sensitivity ranged from The sensor stability seems to be satisfactory and the rate of side effects seems to be low.
In this work, we provide a comprehensive review and meta-analysis on the diagnostic accuracy of MID and NID for hypoglycaemia detection in patients with type 1 diabetes and type 2 diabetes.
Fifteen studies with a total of participants evaluating the diagnostic accuracy of hypoglycaemia detection of MID and NID were included. The mean sensitivity was There was remarkable heterogeneity among the included studies.
Meta-regression analyses revealed an association of type of reference standard test venous vs. capillary blood , number of participants, reported overall performance, artificial manipulation of blood glucose and funding by manufacturers with device performance in hypoglycaemia detection.
Pooled sensitivity was significantly higher in studies funded by device manufactures. Different reasons might contribute to this association. The study design might have been more rigorous in trials funded by manufacturers.
This concept is supported by the fact that the sample size was generally higher and venous blood was used more often as the reference standard in those studies. On the other hand, in manufacturer-funded studies, trial protocols might have been chosen that tend to overestimate device performance.
And indeed, induced hypoglycaemia by insulin administration was more commonly performed in these studies. Additionally, we found that there is a notable rate of side effects and adverse events in one case even a serious side effect.
Furthermore, the sensor stability was reported as relatively poor throughout the studies. While this work, to the best of our knowledge, for the first time reviews systematically the accuracy of MID and NID in detection of hypoglycaemia, a recent non-systematic review also sees limitations in the diagnostic accuracy of MID and NID and raises concerns regarding the frequency of false-positive alarms [ 44 ].
Interestingly, Howsmon et al. praise the high sensor accuracy and alarm sensitivity of CGM systems in their non-systematic review [ 45 ]. A reason for this discordant conclusion might be the fact that the authors make the assumption that an improved sensor accuracy in the hypoglycaemic range can be translated into providing more accurate hypoglycaemic alarms, which might not always follow.
Notably, the authors of the UK recommendation on one particular, currently very popular device FreeStyle Libre are aware of these limitations as they recommend to validate hypoglycaemic values measured with FreeStyle Libre via finger-prick blood glucose testing [ 46 ].
Even though the present review reveals that an accurate detection of hypoglycaemic events can likely not be achieved with MID and NID, a recent meta-analysis has found that patients using MID spend less time in hypoglycaemia than patients using SMBG [ 47 ].
This finding could be due to reduced detection of hypoglycemic events; however, other reasons may lead to a reduction of time spent in hypoglycaemia, for example because users may be able to recognise a trend towards hypoglycaemia and take precautionary steps accordingly.
Interestingly, Koziel et al. found in their non-systematic review that this reduction of time in hypoglycaemia does not correlate with device accuracy in terms of MARD. However, in keeping with our findings, they reported a significant relationship between MARD and the detection of hypoglycaemic events [ 48 ].
The aim of MID and NID is the accurate and user-friendly monitoring of glucose levels. The results of this review indicate that most devices are not yet able to detect hypoglycaemia with sufficient accuracy. In 1 year of using an average MID or NID, according to the results of this meta-analysis, a patient with type 1 diabetes is expected to experience about 17 false-positive alarms and about 32 false-negative measurements.
Underlying this estimate is an incidence of two episodes of symptomatic hypoglycaemia per week per patient [ 1 , 2 ]. The high number of false-positive alarms especially during the night may lead to user frustration, alarm fatigue and cessation of device use. Even worse, subsequent alarms may not be taken seriously and true hypoglycaemic events may be missed.
The number of false-negative events is equally concerning, as a missed hypoglycaemic episode may be a life-threatening event. This is especially problematic when MID and NID do not confirm hypoglycaemia in the presence of related symptoms, especially during rapid changes in glucose levels [ 49 ].
This increases the risk of delayed hypoglycaemia detection. Therefore, based on the available data, MID and NID do not appear to be sufficiently accurate to replace SMBG for the detection of hypoglycaemic episodes on its own.
Values measured via MID or NID in or near the hypoglycaemic range should be double-checked with another method e. capillary blood. As we also observed a lack of robust high-quality studies, larger and methodologically optimised works are needed to assess the accuracy of hypoglycaemia detection of MID and NID.
The risk of bias was specifically high in terms of patient selection. Future studies should take care of including the relevant population e. people unaware of hypoglycaemia should not be excluded.
Investigating the comparative diagnostic accuracy among MID and NID is highly challenging [ 50 ]. This systematic review was not designed to provide a complete overview on adverse events and device failure. However, our data are indicative of a high number of adverse events and system failures, and this is likely to be an underestimate as harms may be underreported [ 52 ].
Therefore, further studies investigating the actual number and severity of side effects, and analysis of the sensor stability as well as reasons for system failure are mandatory.
This systematic review provides the first comprehensive review of the current evidence on the diagnostic accuracy of MID and NID for the detection of hypoglycaemia. However, some limitations need to be considered: It is generally challenging to investigate the diagnostic accuracy of MID or NID.
Therefore, the quality of articles in this field of research often appears imperfect. Frequently, the incomplete reporting in the included studies impeded the assessment of their methodological quality.
In particular, there was uncertainty with regard to the index test and the patient selection. This might lead to an overestimation of the accuracy of hypoglycaemia detection of NID and MID by the present systematic review. However, meta-regression analyses have only revealed an insignificant trend regarding an influence of the year of publication on diagnostic accuracy.
The present data show that MID and NID are not sufficiently accurate for detecting hypoglycaemia in type 1 diabetes and type 2 diabetes in routine use. The indicated diagnostic accuracy was associated with a variety of factors including the type of reference standard test, study size, general device performance, artificial manipulation of blood glucose and study funding source.
Additionally, we saw a notable rate of side effects and adverse events and a limited sensor stability. The incidence and impact of hypoglycemia in type 1 and type 2 diabetes.
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By Victoria Songa senior reporter focusing on wearables, Flucose tech, and monitoriing with 11 years of experience. Before coming to NNon-invasive Verge, she Interval training workouts for Gizmodo and PC Magazine. Recently, Bloomberg ran a story that set the health tech sphere abuzz. Citing insider knowledge, it claimed Apple had reached a major milestone in noninvasive blood glucose monitoring that could revolutionize diabetes treatment as we know it. Like other kinds of emerging health tech, noninvasive blood glucose monitoring has both technical and regulatory hurdles to clear. As it turns out, that may not even be the most realistic or helpful use for the technology in the first place.
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