Analysis of time in range depending on the method of assessing the glucose level

Diabetes prevalence is increasing globally especially in the Asian subcontinent. It is expected that by the year 2030 there may be close to 400 million people with diabetes. All of the research in the past 25 years has clearly documented the effectiveness of improving glucose control in reducing long-term complications of diabetes, both microvascular and macrovascular. The improvement in glucose control usually requires continuous intensive diabetes management, particularly in insulin-requiring patients, which must include home self-monitoring of blood glucose (SMBG). Despite the convincing evidence, the role of SMBG in diabetes management is still being debated even though its availability in the past 35 years has revolutionised diabetes care, especially at home. The International Diabetes Federation (IDF) recently published guidelines for SMBG use in non-insulin-treated diabetic patients, recommending that SMBG should be used only when patients and/or their clinicians possess the ability, willingness and knowledge to incorporate SMBG and therapy adjustment into their diabetes care plan. The IDF also recommends that structured SMBG be performed with the choice of applying different defined blood glucose testing algorithms to patients' individual diabetes care plans. These defined blood glucose testing algorithms give SMBG a medically meaningful structure to collect high quality glucose information and are called structured SMBG. Former SMBG studies have demonstrated SMBG to be beneficial when patients receive feedback regarding the impact of their behaviours on SMBG results. Other studies which did not link SMBG results to these principal behaviours have shown no SMBG benefit. A new wave of clinical studies performed after the release of the IDF guideline have recently been published and have proved the success of the new application of SMBG. The reasons for this ongoing debate may in part be due to rising healthcare costs globally, lack of convincing data in non-insulin-requiring patients with type 2 diabetes in randomised controlled clinical trials and multiple controversial meta-analyses performed on several studies. Sometimes the decisions are extended to insulin-requiring patients, even those with type 1 diabetes. For example, last year in the state of Washington in the USA, legislators were going to stop reimbursing glucose test strips for children with type 1 diabetes. After much debate with committee members (who were not diabetologists and or endocrinologists) and law makers, not only SMBG but even in some cases continuous glucose monitoring (CGM) is now reimbursed. The issue was simply educating non-understanding but well-meaning people whose main concern is saving money. In the end, no one, even those not familiar with paediatric type 1 diabetes, can disagree about the need for SMBG in this age group. It seems to us that we should instead be spending our time and effort in advancing the field and improving diabetes management for patients through newer technologies like CGM and closed-loop systems. As discussed in the section on CGM (Chapter 2) there is ample data from both non-randomised and randomised clinical trials showing the efficacy in reducing time spent in hypoglycaemia and hyperglycaemia along with improvement in glucose control without introducing any additional medication. We hope that the future will be spent in advancing the care rather than useless meta-analyses or going back in time. It is worthwhile to review existing evidence about SMBG to learn, transfer and apply knowledge about the core requirement for good diabetes management, glucose information. Fendler W, Hogendorf A, Szadkowska A, Młynarski W Department of Pediatrics, Oncology, Hematology and Diabetology, Medical University of Lodz, Poland Pediatr Endocrinol Diabetes Metab 2011; 17 : 57–63 Background: SMBG is one of the major components of diabetes management. Aims: To evaluate the potential for miscoding of a personal glucometer, to define a target population among paediatric patients with diabetes for a non-coding glucometer and to assess the accuracy of the Contour TS non-coding system. Methods: Potential for miscoding during SMBG was evaluated by means of an anonymous questionnaire, with worst and best case scenarios evaluated depending on the response pattern. Testing of the Contour TS system was performed according to the national committee for clinical laboratory standards guidelines. Results: The estimated frequency of individuals prone to non-coding ranged from 68.21% [95% confidence interval (CI) 60.70%–75.72%] to 7.95% (95% CI 3.86%–12.31%) for the worse and best case scenarios, respectively. Factors associated with increased likelihood of non-coding were a smaller number of tests per day, a greater number of individuals involved in testing and self-testing by the patient. The Contour TS device showed intra- and inter-assay accuracy of –95%, a linear association with laboratory measurements (R2 = 0.99, p < 0.0001) and small bias of –1.12% (95% CI –3.27% to 1.02%). Clarke error grid analysis showed 4% of values within the benign error zone (B) with the other measurements yielding an acceptably accurate result (zone A). Conclusions: The Contour TS system showed sufficient accuracy to be safely used in the monitoring of paediatric patients with diabetes. Patients from families with a high throughput of test-strips or multiple individuals involved in SMBG using the same meter are candidates for clinical use of such devices due to an increased risk of calibration errors. Comment: This study further highlights the role of making SMBG simpler and easier so that patients can monitor the glucose more effectively. The current study used the Contour TS system which does not require coding by the patient and thus removes the barrier of mis-coding of SMBG. We personally think that all meters going forward must be non-coding meters. Nerhus K 1 , Rustad P 2 , Sandberg S 1,3 1 Norwegian Centre for Quality Improvement of Primary Care Laboratories, Department of Public Health and Primary Health Care, University of Bergen, Bergen, Norway, 2 Norwegian Clinical Chemistry EQA-Program, Fürst Medical Laboratory, Oslo, Norway, 3 Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway Diabetes Technol Ther 2011; 13: 883–92 Background: Analytical quality of SMBG can be affected by environmental conditions. Aims: To determine the influence of a shift in the ambient temperature immediately before measurement and taking measurements in the lower and upper part of the operating temperature range. Methods: Different SMBG systems (n = 9) available on the Norwegian market were tested with heparinised venous blood (4.8 and 19.0 mmol/l). To test the effect of a shift in ambient temperature, the glucometer and strips were equilibrated for 1 h at 5 °C or 30 °C before the meter and strips were moved to room temperature, and measurements were performed after 0, 5, 10, 15 and 30 min. To test the lower and upper temperature range, measurements were performed at 10 °C and at 39 °C after 1 h for temperature equilibration of the glucometer and strips. All the measurements were compared with measurements performed simultaneously on a meter and strips kept the whole time at room temperature. Results: Six of nine SMBG systems overestimated and/or underestimated results by more than 5% after moving meters and strips from 5 °C or 30 °C to room temperature immediately before the measurements. Two systems underestimated the results at 10 °C. One system overestimated and another underestimated the results by more than 5% at 39 °C. Conclusions: A rapid shift in the ambient temperature affects analytical performance. Therefore patients need to wait at least 15 min for temperature equilibration of affected meters and strips before measuring blood glucose. Comment: This study highlights the importance of ambient temperature on analytical performance of SMBG. The study shows that rapid shift in ambient temperature may affect the accuracy and bias in SMBG measurement and highlights the need for 15 min temperature equilibration. In addition to what has been highlighted in the study, future studies also need to assess the accuracy of existing meters (especially the one using glucose oxidase) at higher altitudes (10,000 feet or higher). It is known that many of these meters do not perform well at high altitudes. McAndrew LM 1,2 , Horowitz CR 3 , Lancaster KJ 4 , Quigley KS 2,5,6 , Pogach LM 1,2 , Mora PA 7 , Leventhal H 8 1 War Related Illness and Injury Study Center and REAP Center for Healthcare Knowledge Management, Department of Veterans Affairs, New Jersey Health Care System, East Orange, NJ, USA, 2 University of Medicine and Dentistry of New Jersey, Newark, NJ, USA, 3 Department of Health Evidence and Policy, Mount Sinai School of Medicine,New York, NY, USA, 4 Department of Nutrition, Food Studies and Public Health, New York University, New York, NY, USA, 5 Department of Veterans Affairs, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA, USA, 6 Department of Psychology, Northeastern University, Boston, MA, USA, 7 Psychology Department, University of Texas at Arlington, Arlington, TX, USA, and 8 Institute for Health, Health Care Policy and Research, Rutgers University, New Brunswick, NJ, USA J Diabetes 2011; 3 : 147–52; Comment in J Diabetes 2011; 3 : 93–4 Background: It is unknown whether SMBG can motivate adherence to dietary recommendations. Aims: To evaluate if patients who used more SMBG would also report lower fat and greater fruit and vegetable consumption. Methods: This was a cross-sectional study of primarily minority individuals living with diabetes in East Harlem, New York (n = 401). Fat intake and fruit and vegetable consumption were measured with the Block Fruit/Vegetable/Fiber and Fat Screeners. Results: Greater frequency of SMBG was associated with lower fat intake [r(s) = –0.15; p < 0.01], but not fruit and vegetable consumption. The effects of SMBG were not moderated by insulin use. A significant interaction was found between frequency of SMBG and changing one's diet in response to SMBG on total fat intake. Conclusions: The frequency of SMBG was associated with lower fat intake. The data suggest that participants who use SMBG to guide their diet do not have to monitor multiple times a day to benefit. Comment: This study further highlights the importance of SMBG in daily lifestyle changes. Subjects with higher frequency of SMBG consumed less fat, in part related to overall education and seeing the impact from making dietary changes on SMBG levels. Kuo CY 1,2 , Hsu CT 3 , Ho CS 3 , Su TE 3 , Wu MH 4 , Wang CJ 2,5 1 Department of Clinical Laboratory, Tai-An Hospital, Taichung, Taiwan, 2 Institute of Biochemistry and Biotechnology, Chung Shan Medical University, Taichung, Taiwan, 3 Department of Core Technical Research, Bionime Corporation, Taichung, Taiwan, 4 Department of Laboratory Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan, 5 Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan Diabetes Technol Ther 2011; 13 : 596–600 Background: SMBG systems should at least meet the minimal requirement of the World Health Organization's ISO 15197:2003. For tight glycaemic control, a tighter accuracy requirement is needed. Methods: Seven SMBG systems were evaluated for accuracy and precision: Bionime Rightest™ GM550 (Bionime Corp., Dali City, Taiwan), Accu-Chek® Performa (Roche Diagnostics, Indianapolis, IN, USA), OneTouch® Ultra®2 (LifeScan Inc., Milpitas, CA, USA), MediSense® Optium™ Xceed (Abbott Diabetes Care Inc., Alameda, CA, USA), Medisafe (TERUMO Corp., Tokyo, Japan), Fora® TD4227 (Taidac Technology Corp., Wugu Township, Taiwan) and Ascensia Contour® (Bayer HealthCare LLC, Mishawaka, IN, USA). The 107 participants were 23–91 years old. The analytical results of seven SMBG systems were compared with those of plasma analysed with the hexokinase method (Olympus AU640, Olympus America Inc., Center Valley, PA, USA). Results: The imprecision of the seven blood glucose meters ranged from 1.1% to 4.7%. Three of the seven blood glucose meters (42.9%) fulfilled the minimum accuracy criterion of ISO 15197:2003. The mean absolute relative error value for each blood glucose meter was calculated and ranged from 6.5% to 12.0%. Conclusions: More than 40% of evaluated SMBG systems meet the minimal accuracy criterion requirement of ISO 15197:2003. However, considering a tighter criterion for accuracy of ±15%, only the Bionime Rightest GM550 meets this requirement. Manufacturers have to try to improve accuracy and precision and to ensure the good quality of blood glucose meters and test strips. Comment: This study further highlights the need for more accurate SMBG systems. Their data concluded that more than 40% of the evaluated SMBG systems meet the minimum ISO criteria. Since patients use blood glucose information for adjusting their insulin dose and/or treating hypoglycaemia, the accuracy of the glucose meters has to be consistent and improved. Hortensius J 1 , Slingerland RJ 2 , Kleefstra N 1,3,4 , Logtenberg SJ 1 , Groenier KH 5 , Houweling ST 3,6 , Bilo HJ 1,4 1 Diabetes Centre, Isala Clinics, Zwolle, The Netherlands, 2 Department of Clinical Chemistry, Isala Clinics, Zwolle, The Netherlands, 3 Medical Research Group, Langerhans, The Netherlands, 4 Department of Internal Medicine, University Medical Center, Groningen, The Netherlands, 5 Department of General Practice, University of Groningen, Groningen, The Netherlands, and 6 General Practice Sleeuwijk, Sleeuwijk, The Netherlands Diabetes Care 2011; 34 : 556–60 Background: There is no agreement regarding the use of the first or second drop of blood for glucose monitoring. Aims: To investigate whether capillary glucose concentrations, as measured in the first and second drops of blood, differed ≥10% compared with a control glucose concentration in different situations. Methods: Capillary glucose concentrations were measured in two consecutive drops of blood in 123 patients with diabetes in the following circumstances: without washing hands, after exposing the hands to fruit, after washing the fruit-exposed hands, and during application of different amounts of external pressure around the finger. The results were compared with control measurements. Results: Not washing hands led to a difference of ≥10% in glucose concentration in the first and in the second drops of blood in 11% and 4% of the participants, respectively. In fruit exposed fingers, these differences were found in 88% and 11% of the participants, respectively. Different external pressures led to ≥10% differences in glucose concentrations in 5%–13% of the participants. Conclusions: Washing hands with soap and water, drying them, and using the first drop of blood for SMBG is recommended. If washing hands is not possible, it is acceptable to use the second drop of blood after wiping away the first drop. External pressure may lead to unreliable readings. Comment: Over the years we have probably under-emphasised the importance of technique with SMBG. One has to wonder how much iatrogenic hypoglycaemia has occurred due to unintended exposure to glucose on the hands, and how often CGM devices are due to technique with SMBG use. 1,2 , 3 , 4 , 5 , 6 , 7 , 8 , 8 , 8 1 University of CA, USA, 2 Diabetes CA, USA, 3 University of CA, USA, 4 University Health System, USA, 5 America Diabetes USA, 6 Health IN, USA, 7 USA, and 8 Diagnostics, Indianapolis, IN, USA Diabetes Care 2011; 34 : To assess the effectiveness of structured blood glucose testing in controlled patients with type 2 diabetes without insulin Methods: A study patients with type 2 diabetes (n = and glycaemic control from 34 care in the were randomised to an control with care or a structured testing with care and at least use of structured SMBG. patients and were to use a to glucose consecutive The was measured at Results: The analysis = = showed greater in mean in the compared with the = p = analysis = = showed even greater mean in the compared with the p < more patients a at the first compared with patients, of the p < and patients significant < 0.0001) in Conclusions: use of structured SMBG glycaemic control and more changes in patients with type 2 diabetes without Comment: It is with an healthcare using a structured glucose testing can improve glucose control in non-insulin-treated Potential are of this can be in a with the more time in a care to be , 5 , 6 , 1,4 1 Department of and Laboratory Medicine, University of 2 Department of and University of 3 Department of Pediatrics, University of 4 Laboratory 5 Corporation, MA, USA, and 6 Department of Health University of J Diabetes Technol 4 : Aims: To analytical error in and glucose meters by in and Methods: The of and were tested and in with each glucose meter and with a plasma glucose method at glucose was by consecutive analysis (n = at glucose levels. analysis was used to the bias associated with the and in Results: Three meters demonstrated bias that was glucose bias on the and the of bias was on small significant on meters. of and of increased the bias and was by of total analytical the glucose meter devices of total analytical error in glucose measurement ranged from to the Conclusions: The of glucose meters to significant analytical is the analytical Comment: with to for SMBG and the research is it does not that many people the This has impact on as new guidelines for greater of are the impact on SMBG accuracy be particularly by the device should be a 1 , 1 , 2 , 3 1 Health and 2 Health, and 3 of and Department of Internal Medicine, : Aims: To evaluate the of SMBG in patients with type 2 diabetes with in Methods: In a study a of diabetes was used to from a published study of SMBG in patients with type 2 diabetes, with and with no of a time was from the of a healthcare and clinical were at Results: or times daily SMBG was associated with in which led to increased and and of diabetes complications compared with no SMBG in type 2 diabetes patients on costs increased by and in patients SMBG or times daily compared with those not using SMBG. were well at and per year respectively. Conclusions: SMBG is to be by standards in patients on in the Comment: This analysis is on a showing of and The concern of is that this analysis is not a randomised controlled and at best the data are about the efficacy of this population using this analysis is probably on controversial 1,2 , 3 , S 3 , 1 Health Research, University School of Medicine, Indianapolis, IN, USA, 2 Institute for Health Care, Indianapolis, IN, USA, 3 of University School of Medicine, Indianapolis, IN, USA, and 4 Diabetes and Center, University School of Medicine, Indianapolis, IN, USA Diabetes 2011; : To assess the and of a glucose monitoring system for with type 1 diabetes and their Methods: Patients with type 1 diabetes who been for at least 1 year in the used the system for 6 3 to and with the glucose monitoring as well as how use of the system affect quality of and diabetes management. Results: about the a number of significant that affected use of the all that the in monitoring testing was The use of the did not the quality of their of with their their of diabetes, or their glycaemic control within the time of the Conclusions: This that glucose monitoring can be used in an population to and in self-monitoring Comment: for both SMBG and CGM is clearly the future for with type 1 diabetes. The will be to how to best use this to improve in this To the is new to how best to use more studies will be to this P 1 , 2 , ST 3 , 2 , 2 , 4 , Bilo 5 , Bilo HJ 1 Department of Internal Medicine, Hospital, The Netherlands, 2 Department of Clinical Chemistry, Isala Clinics, Zwolle, The Netherlands, 3 Department of Isala Clinics, Zwolle, The Netherlands, 4 Centre for The Netherlands, 5 Department of Internal Medicine, Isala Clinics, Zwolle, The Netherlands, and 6 Department of Internal Medicine, University Medical Centre, Groningen, The Netherlands One 5 : Background: Patients with diabetes part in and thus blood glucose meters are studies bias in blood glucose measurements using different at high To evaluate if glucose are more by the lower pressure at than glucose Methods: measurements at of nine glucose glucose were compared with glucose measurement on a at and with a laboratory glucose blood of different glucose were were at a bias from glucose and from glucose mmol/l). Results: significant difference was between measurements at and for glucose or glucose as a Two glucose did not meet performance criteria. Conclusions: high all tested glucose did not influence of All for two glucose performed within criteria. are glucose concentration at high high one glucose best precision and Comment: testing for SMBG meters may be In glucose test strips should be at high altitudes. not in this it should be that patients do not this into when or 1 , J 2 , 2 , 1 , 3 , 4 , J 3 , 5 , J 6 , 7 , 7 , 8 1 Department of Health University of Health Center, USA, 2 and Health Department of Medical and University of USA, 3 Department of and Health University of USA, 4 Diabetes Care Center, Medical USA, 5 Department of University of USA, 6 Medicine Technology Center USA, 7 Department of Medicine, University of USA, and 8 Health Research USA J Aims: To assess the and of using as part of an existing system for between patients with diabetes and a care Methods: In glucose meter and two to feedback on glycaemic control were Results: glucose meter with and data feedback were the system to an and feedback system for value in the system as an to the and Conclusions: diabetes management systems may one to improve the quality of diabetes Comment: best to in a care for type 2 diabetes is still It is that there will be different of that will for different patient the use of the as a means of that device like the of in diabetes management. the now seems to be how to is a It may be that will be the only for many the of time this population 1 , 2 1 Centre for Policy Research, of Health Health Policy and Research, University of and 2 Institute for Medicine, and Health University Medical Centre 2011; : Background: the of and in Patients with 2 Diabetes a in the risk of in patients with type 2 diabetes who performed SMBG. Aims: To evaluate if these are by a that time Methods: The bias in the study was and demonstrated that it is to the effect of SMBG on Results: In the study, patients were as exposed to SMBG for their whole time if performed SMBG for at least 1 year during the study the time between and the after 1 year of self-monitoring was performed is for patients with SMBG. Patients to at least 1 year to be as exposed to this and were from The total of in the SMBG is at least of at risk After of as the relative risk from to Conclusions: The effect of SMBG on in the study is by time Comment: The study is one of the trials in the of SMBG. Study is probably a major for between the studies. Kleefstra N 1,2 , Hortensius J 1 , Logtenberg SJ 1 , 3 , Groenier K 4 , Houweling ST 2,5 , 6 , 2 , Bilo HJ 1 Diabetes Centre, Isala Clinics, Zwolle, The Netherlands, 2 Medical Research Langerhans, Zwolle, The Netherlands, 3 Department of Clinical Chemistry, Isala Clinics, Zwolle, The Netherlands, 4 Department of General Practice, University of Groningen, Groningen, The Netherlands, 5 General Practice Sleeuwijk, Sleeuwijk, The Netherlands, 6 Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands J : Background: It is not if SMBG glycaemic control in patients with type 2 diabetes. To investigate the effects of SMBG in patients with type 2 diabetes who were in glycaemic Methods: Patients years with an of using one to two blood glucose were in the Patients (n = were to receive SMBG to care or to with care for 1 A glucose value and glucose values were measured The efficacy was quality of and were using the Health the the Diabetes and the Results: in between was (95% CI to p = There were no significant changes between on the type or for the which was lower in the difference (95% CI to Conclusions: type 2 diabetes patients some of their the use of SMBG in these patients is and its use should be Comment: This is another SMBG study in non-insulin-treated type 2 the of not patients what to do with the information is one has to wonder if this at some to have some of the it is to that the in care is to more education and time than is in many of these studies. A 1 , 1 , 1 , S 2 , S 3 , A 1 , A 1 , 1 , 4 , 3 , 3 , 1 1 Department of and School of Medicine, 2 Department of School of Medicine, 3 Department of Biochemistry, School of Medicine, and 4 Department of School of Medicine, Diabetes Technol Ther : Background: glucose meters may not be accurate to Aims: of the accuracy and the capillary and venous of different Xceed (Abbott Diabetes Care, Alameda, CA, USA), Contour TS (Bayer Diabetes Care, (Roche Milpitas, CA, and Inc., in an Methods: The insulin hypoglycaemia test was performed for of mean age was measured from venous blood and capillary before and after of insulin were analysed in the laboratory by the hexokinase In tests for method and precision were also performed by the venous Results: All to hypoglycaemia to some was in error zone and was in the error zone Xceed and Contour TS and were than the other two according to error grid analysis or International for criteria. The in tests were consistent with the clinical The capillary and venous of and were than the other Not all are accurate in blood glucose levels. The patients and the should be of these of the and give more to the hypoglycaemia than the values with the These results that there is a need for the of accuracy standards of at blood glucose levels. Comment: We have the that meters are from especially at levels. patients can state a time when an The are not only for the of hypoglycaemia but also as we forward with CGM about how this about this the with the for use of the meters our clinical with patient error us that accuracy a for glucose meters at levels. has research from Inc., Corporation, Inc., and is a for Diagnostics, Diabetes Care and

Insulin glargine U100/lixisenatide and insulin degludec/liraglutide are fixed-ratio combinations containing basal insulin and a glucagon-like peptide-1 receptor agonist capable of reducing both fasting and postprandial blood glucose levels with a single formulation. This study aimed to compare the time in range (TIR) and the time below range (TBR) level 1 using professional continuous glucose monitoring and to establish criteria for the differential use of the fixed-ratio combinations. Thirty-six outpatients with type 2 diabetes mellitus (24 men and 12 women; average age, 62.1 years) were randomly assigned to the groups. At 0 and 18 weeks, a device was worn to compare the TIR and TBR level 1. Thecorrelation between the C-peptide index at baseline and TIR at 18 weeks was assessed. The TIR and TBR level 1 showed no significant differences between the two groups. Both groups showed significant positive correlations between the C-peptide index and the TIR (P = 0.002, r = 0.679; P = 0.002, r = 0.681, respectively). The changes in glycemic variability, therapeutic indices, and body mass index were not significantly different among the groups (P > 0.05). The receiver operating curve analysis revealed that the cut-off values of the C-peptide index to achieve TIR of >70% at 18 weeks were 1.258 (sensitivity, 77.8%; specificity, 100%) and 1.099 (sensitivity, 57.1%; specificity, 90.9%) in the insulin glargine U100/lixisenatide and insulin degludec/liraglutide groups, respectively. A TIR of >70% was achieved for both fixed-ratio combinations without significant differences.

BackgroundTelemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers.ObjectiveDuring the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period.MethodsThis prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit.ResultsAmong 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; P<.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (P=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; P=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; P<.001) than in those with a GMI of <7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters.ConclusionsThese findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.

Update on technologies, medicines and treatments.

Flash Glucose Monitoring System facilitates sustainable improvements in glycemic control in patients with type 1 diabetes: A 12-month follow-up study in real life

Objective: To investigate the association between treatment induced change in continuous glucose monitored (CGM) time in range (TIR) and microalbuminuria in persons with type 1 diabetes (T1D) treated with sensor-augmented pumps (SAP). Methods: In an open labelled controlled trial, 60 participants with T1D treated with multiple daily injections (MDI) and history of albuminuria were randomised to MDI- or SAP-therapy for 1 year. During the study, the SAP group wore a CGM (Enlite®, Medtronic). The MDI group wore blinded CGMs (Ipro2®, Medtronic) for 6 days at baseline and study end. Anthropometrics, CGM data, blood and urine samples were collected at 0, 1, 3, 6, 9, and 12-month visits. Based on CGM data collected between each visit, the percentage of time spent within 3.9-10.0 mmol/l (%TIR) was calculated. Urine albumin creatinine ratio (UACR) was measured in 3 samples at all visits. Only CGM data for the SAP group were included in the analysis. A linear mixed model with a participant-specific random intercept was used to analyse the effect of %TIR during 1 year after start of SAP on UACR, adjusting for changes in mean arterial pressure (MAP), HbA1c and body mass index. Results: Twenty-six participants were assigned to the SAP group and had a baseline median (interquartile range) age of 51.5 (45.9; 62.1) years, HbA1c of 66 (58; 77) mmol/mol, UACR of 78.8 (37; 205) mg/g and 51Cr-EDTA-GFR 85.0 (73.0; 96.5) ml/min/1.73m2. From baseline to study end, the mean improvement (95% CI) in %TIR was 11.3 (6.0; 16.6) %, HbA1C was -14.4 (-17.5; -11.2) mmol/mol and UACR was -13 (-33; -22) %; all p&amp;lt;0.001. HbA1c decreased with 4.7 mmol/mol per 10% increase in %TIR (p&amp;lt;0.001). The UACR decreased with 13% per 10% increase in %TIR (p=0.038), with 15% per 10 mmol/mol decrease in HbA1c (p=0.071), and with 31 % per 10 mmHg decrease in MAP (p&amp;lt;0.001). Conclusion: In this longitudinal study, treatment induced increase in CGM-derived %TIR were significantly associated with decrease in albuminuria in T1D. Disclosure A. Ranjan: None. S. Rosenlund: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. T.W. Hansen: None. P. Rossing: Advisory Panel; Self; Sanofi. Consultant; Self; Astellas Pharma Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S. Speaker’s Bureau; Self; Eli Lilly and Company. Stock/Shareholder; Self; Novo Nordisk A/S. S. Andersen: None. K. Nørgaard: Advisory Panel; Self; Abbott, Medtronic. Research Support; Self; Novo Nordisk A/S. Speaker’s Bureau; Self; Medtronic, Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk Inc. Funding Novo Nordisk Foundation (NNF17SA0031406); Medtronic A/S

Karaz is a mobile health solution designed to leverage real-time monitoring, monetary incentives, and gamification to improve health outcomes in people with diabetes (PWD). Data generated by wearables are all displayed on a unified interface for users and healthcare professionals. Karaz app rewards users for engaging in health-promoting behaviors by offering points redeemable for monetary value. To evaluate the changes in continuous glucose monitoring (CGM) metrics, level of physical activity, and sleep duration over three months among PWD who used Karaz app for ≥ 3 months. Data from 384 Karaz app users who had diabetes, were wearing CGM, and used Karaz app for ≥ 3 months were retrospectively analyzed. Logistic regression analysis was performed to identify potential predictors of improvement in time in range (TIR) after using Karaz app. The average age of the study participants was 24 years, 57% women, and 78% had type 2 diabetes. In PWD who had baseline TIR ≤ 60%, CGM metrics improved from baseline to month 3: TIR (43.1 to 45.89%, p < 0.01), time above range (TAR) > 250 mg/dL (24.66 to 22.5%, p < 0.01), TAR > 180 mg/dL (51.83 to 48.79%, p < 0.01), glycemia risk index (GRI) (87 to 84%, p < 0.01), and time in tight range (TITR) 70–140 mg/dL (25.54 to 27.84%, p < 0.01). Whereas, in PWD with baseline TIR > 60%, glucose levels changed from baseline to month 3 as follows: TIR (74.88 to 73.53%, p < 0.01), TAR > 250 (4.03 to 4.81%, p = 0.03), TAR > 180 (20.19 to 21.24%, p = 0.07), TITR (52.63 to 52.04%, p = 0.29). Compared to PWD with baseline TIR > 70%, those with baseline TIR 40–70% and TIR < 40% were 10 and 8 times, respectively, as likely to experience a significant improvement in TIR, by > 5%, after adjusting for age, gender, baseline TBR, and daily steps. Use of Karaz app, with monetary incentives and gamification, was associated with significant improvements in CGM metrics in PWD who had suboptimal glucose control at baseline. This highlights the promising role of innovative digital solutions and management approaches in improving health outcomes in PWD.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23762-w.

Background: Coronavirus (COVID-19) disease portends a poor prognosis in patients with type 1 diabetes (T1D) . As a consequence, the booster dose of Covid-vaccination should be prioritized in these patients. Nonetheless, concerns exist about vaccine-induced dysglycemia. Objectives: Aim of this study was to assess the short-term effects of booster dose of Covid-vaccination on glycaemic control assessed by flash glucose monitoring (FGM) in people with T1D. Methods: In this observational cross-sectional study we investigated changes in daily insulin requirement (IR) and glycaemic control between 7 days before and 7 days after the third dose of vaccination with BioNTech Pfizer among 30 individuals with T1D on multiple daily insulin injections wearing a flash glucose monitoring (FGM) device. The following parameters of glycaemic variability were analysed: mean glucose, time in range (TIR) , time above range (TAR) , time below range (TBR) and coefficient of variation (CV) . Results: No significant differences were found for mean glycemia, TIR, TAR and TBR over the course of the vaccination from 7 days prior to receiving the third-dose vaccination until 7 days after. Nonetheless, CV and IR were significantly higher (CV, p-value = 0.001; IR, p-value = 0.05) in the week after the vaccination compared to the week earlier. The median value of CV was 35.5% [33-37] before and 36.9% [34-39] after the booster dose, whereas the median value of IR changed from 0.55 UI/Kg/day to 0.61 UI/Kg/day. Conclusions: Our study suggests that the booster dose of Covid-vaccination impact on glycaemic variability and insulin requirement in people with T1D, probably due to the pro-inflammatory cytokines and immune responses. While this observation should be investigated in larger studies, potential glycaemic aberrations in response to Covid-immunization should be considered by health care professionals and glucose monitoring intensified within the days around the vaccination. Disclosure S.Pieralice: None. L.Monte: None. G.Defeudis: None. G.Tabacco: None. N.Napoli: Advisory Panel; Novo Nordisk, Consultant; Lilly Diabetes. P.Pozzilli: Advisory Panel; Dompé, Consultant; Abbott, Speaker’s Bureau; Dompé, Lilly Diabetes, Medtronic, Sanofi.

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70-180 mg/dL] 54% vs. 49%) and hypoglycemic range <70 mg/dL (7% vs. 5%), less time in hyperglycemic range >180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged <30 years. We found no significant differences between genders. Multivariable regression models revealed the highest Time In Range (TIR) and lowest time with sensor glucose >250 mg/dL, CV and daytime-night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

Background Continuous glucose monitoring (CGM) enables the assessment of blood glucose trends and patterns, as well as the significant variability in excursions in glucose in individuals having type 1 diabetes mellitus (T1DM). Aim The purpose of this work was to compare patients using CGM devices and patients on self-monitoring of blood glucose (SMBG) regarding time in range (TIR) and assess the association between glycated hemoglobin (HbA1c) and TIR in patients with T1DM using CGM devices and patients on SMBG. Patients and methods This observational, prospective cohort work had been conducted on 60 participants aged from 4 to 16 years old, both sexes, with T1DM. Patients had been assigned into two groups equally: group I: using CGM device (free style libre) and group II: with SMBG on multiple daily insulin treatment. Results TIR was significantly higher in group I compared with group II (P&lt;0.05). HbA1c was significantly greater in group II compared with group I (P&lt;0.05). TIR was significantly correlated with HbA1c in both groups (P&lt;0.001). Lipodystrophy at injection sites in group II was significantly correlated with HbA1c (P=0.037). High-density lipoproteins cholesterol was significantly greater in group I than group II (P&lt;0.05). Alanine transaminase, aspartate aminotransferase, urea, creatinine, triglycerides, total cholesterol, mean blood glucose, and time above range were significantly greater in group II than group I (P&lt;0.05). Conclusion CGM contrasted to SMBG has shown an improvement in TIR and a reduction in HbA1c in patients with type 1 diabetes. CGM is revolutionizing diabetes management.

Continuous glucose monitoring (CGM) metrics are increasingly used to study the relationship between glycemic control and diabetic complications; however, the correlated glucose measurements and unequal follow-up times in real-world CGM data necessitate more advanced analytical approaches to yield unbiased estimates. This retrospective case-cohort study aimed to estimate longitudinal changes in time in range (TIR) on progression to diabetic retinopathy (DR) among patients with type 1 diabetes (T1D). This was a retrospective case-cohort study among patients with T1D, using CGM devices. We analyzed linked CGM and electronic health record data from 161 patients with T1D, with long-term follow-up for incident DR diagnoses. TIR was defined as time spent in sensor glucose between 70 and 180 mg/dL. Multilevel mixed-effects parametric survival models and Markov models were constructed to obtain effects of TIR (eg, hazard ratios [HRs]) and lifetime trajectories of developing retinopathy and blindness, respectively. A retrospective case cohort of 161 patients with T1D (mean duration 13.7 years) included 71 cases (baseline glycated hemoglobin A1c [HbA1c] of 8.2%) and 90 controls (baseline HbA1c of 7.3%). A 10% increase in TIR was associated with a modestly lower risk of retinopathy progression (HR 0.88; 95% CI, 0.78-0.98) and an estimated prevention of 39 cases of blindness per 1000 individuals over time (TIR 70% vs 40%). Economic simulation modeling suggested $2581 lower costs per person and a gain of 0.13 quality-adjusted life years (QALYs). These findings can guide real-world CGM studies and support diabetes simulation models to predict future treatment outcomes from CGM metrics.

Aims/IntroductionThis study aimed to evaluate the association between time in range (TIR) obtained from continuous glucose monitoring and the prevalence and degree of painful diabetic neuropathy.Materials and MethodsA total of 364 individuals with diabetic peripheral neuropathy were enrolled in this study. Sensor‐based flash glucose monitoring systems were used to monitor the participants’ glucose levels, and the glycemic variability metrics were calculated, including the TIR, glucose coefficient of variation, standard deviation and the mean amplitude of glycemic excursions. The participants were asked to record any form of pain during the 2 weeks of monitoring, and score the pain every day on a numerical rating scale. Based on the numerical rating scale, the patients were divided into the pain‐free group, mild pain group and moderate/severe pain group.ResultsOverall, 51.92% (189/364) of the participants were diagnosed with painful diabetic neuropathy. Compared with the pain‐free group, the level of TIR decreased significantly in the mild pain and moderate/severe pain groups (P < 0.05). The prevalence of mild pain and moderate/severe pain decreased with increasing TIR quartiles (all P < 0.05). Multiple linear regression analysis showed that TIR was significantly negatively correlated with the numerical rating scale score after adjustment for glycated hemoglobin, glycemic variability indicators and other risk factors (P < 0.05). Logistic regression analysis showed that a decreasing level of TIR was significantly associated with an increasing risk of any pain and moderate/severe pain (P < 0.05).ConclusionsTIR is correlated with painful diabetic neuropathy and is underscored as a valuable clinical evaluation measure.

INTRODUCTION: The rising incidence of pediatric diabetes underscores the need for accurate glucose monitoring tools to facilitate early detection. Although both Blood Glucose Meters (BGM) and Continuous Glucose Monitoring (CGM) are widely used, their comparative effectiveness in predicting diabetes onset in children remains contested. This study evaluated and compared the predictive utility of BGM and CGM in high-risk pediatric populations. METHODS: A nine-month prospective observational study (January–September 2024) was conducted at the Health Polytechnic of the Ministry of Health, Sorong, involving 76 children aged 10–18 years diagnosed with Acanthosis Nigricans and positive FINDRISC scores. Participants were allocated to either BGM (every 3 days) or CGM (15-minute intervals). Key outcome measures included Time in Range (TIR), mean glucose levels, frequencies of hypo- and hyperglycaemic episodes, adherence rates, and Mean Absolute Relative Difference (MARD). RESULTS: CGM significantly outperformed BGM across all parameters: higher TIR (78.9% vs. 63.4%, p&lt;0.001), lower average glucose levels (145.3 vs. 162.7 mg/dL, p=0.003), fewer hypoglycaemic (1.1 vs. 2.8, p=0.015) and hyperglycaemic events (2.6 vs. 4.5, p=0.002), and superior adherence (88.7% vs. 71.3%, p&lt;0.001). CGM also showed a lower MARD (7.2% vs. 10.8%, p=0.004), indicating greater accuracy. CONCLUSIONS: CGM offers more reliable and comprehensive glucose monitoring than BGM for predicting diabetes onset in children at risk, supported by better glycaemic control, higher adherence, and improved accuracy. These findings endorse CGM as the preferred approach for early diabetes detection in pediatric populations.

Introduction: To evaluate time in tight range (TITR) 70-140 mg/dL (3.9-7.8 mmol/L), its correlation with standard continuous glucose monitoring (CGM) metrics and the clinical variables that possibly have a substantial impact on its value, in a large cohort of pediatric subjects using different treatment strategies. Materials and Methods: A total of 854 children and adolescents with type 1 diabetes were consecutively recruited in this real world, dual center, cross-sectional study. Participants were categorized into four treatment groups (multiple daily injections [MDI] + real-time CGM, MDI + intermittently scanned CGM, sensor augmented pump, and hybrid closed loop [HCL]). Demographical and clinical data, including CGM data, were collected and analyzed. Results: The overall study population exhibited an average TITR of 36.4% ± 12.8%. HCL users showed higher TITR levels compared to the other treatment groups (P < 0.001). A time in range (TIR) cut-off value of 71.9% identified subjects achieving a TITR ≥50% (area under curve [AUC] 0.98; 95% confidence interval 0.97-0.99, P < 0.001), and a strong positive correlation between these two metrics was observed (r = 0.95, P < 0.001). An increase in TIR of 1% was associated with 1.84 (R2 Nagelkerke = 0.35, P < 0.001) increased likelihood of achieving TITR ≥50%. Use of HCL systems (B = 7.78; P < 0.001), disease duration (B = -0.26, P = 0.006), coefficient of variation (B = -0.30, P = 0.004), and glycated hemoglobin (B = -8.82; P < 0.001) emerged as significant predictors of TITR levels. Conclusions: Our study highlights that most children and adolescents with type 1 diabetes present TITR levels below 50%, except those using HCL. Tailored interventions and strategies should be implemented to increase TITR.

Introduction: Type 1 diabetes is an autoimmune condition resulting in insulin deficiency that requires daily insulin therapy and self-monitoring of blood glucose. Continuous glucose monitoring (CGM) systems allow for measurement of interstitial fluid glucose levels in a continuous fashion to identify variations and trends that are not feasible with conventional self-monitoring. Hemoglobin A1C (HbA1C) is the method used to assess adequate glycemic control and relates to future risk of developing complications. Current evidence has shown improvement in HbA1C with concomitant use of CGM in adults over 25 years of age with Type 1 diabetes, whereas studies in children and adolescents have failed to show this. However, it is important to note the limitations in HbA1C use as it is a marker of average blood glucose over 3 months but does not reflect glycemic variability. More recent data has suggested that factors such as time in range (TIR), which can be determined with CGM use, are also associated with decrease risk of diabetes complications.Methods: The goal of our study was to analyze the change in HbA1C levels after using a CGM (DEXCOM G4, G5, G6) over a 6-month period in pediatric patients with Type I diabetes. Two HBA1c levels 3 months apart from 92 patients were collected before using a CGM and two while using a CGM. Results were compared by using a dependent samples t-test. IBM SPSS 25.0 was used for data analysis.Results: Preliminary analysis indicates the average change in HBA1C among the patients (N=92) before (-0.08 ± 1.16) and while using the CGM (0.12 ± 1.00) was not significantly different (t (79) = -1.27, p = 0.21). The average change in HBA1C was also not significantly different (p>0.05) among the patients before and while using the CGM for gender (males and females), age groups (0-7 years, 8-14 years, and 15-24 years), and generations of DEXCOM used (G4, G5, and G6).Conclusion: As has been shown in other studies, we did not find a significant change in HbA1c after CGM use for 6 months in our patients. While HbA1C is a reflection of blood sugars over a 3-month period, it does not provide information about glycemic excursions. Metrics derived from CGM use, such as TIR, can provide actionable information which we did not address in our study. There have been reports of the association between TIR and long-term complications of diabetes. Most data comes from studies in adults and pediatric data is lacking. We propose that future studies must look into CGM metrics such as TIR to better define glycemic control in pediatric patients with diabetes mellitus.