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BACKGROUND: Continuous glucose monitoring can improve glycemic control for hospitalized patients with diabetes, according to current evidence. However, there is a lack of consensus-established recommendations for the management of hospitalized patients with diabetes using flash continuous glucose monitoring system (fCGM) in Latin America. Therefore, this expert consensus exercise aimed to establish guidelines on the implementation of fCGM in the management of hospitalized patients with diabetes in Latin America. METHODS: The modified Delphi method was applied on a panel of nine specialists, establishing consensus at 80%. A twenty-two-question instrument was developed to establish recommendations on the use of fCGM in hospitalized patients living with diabetes. RESULTS: Based on consensus, experts recommend the use of fCGM in hospitalized patients with diabetes starting at admission or whenever hyperglycemia (> 180 mg/dl) is confirmed and continue monitoring throughout the entire hospital stay. The recommended frequency of fCGM scans varies depending on the patient's age and diabetes type: ten scans per day for pediatric patients with type 1 and 2 diabetes, adult patients with type 1 diabetes and pregnant patients, and seven scans for adult patients with type 2 diabetes. Different hospital services can benefit from fCGM, including the emergency room, internal medicine departments, intensive care units, surgery rooms, and surgery wards. CONCLUSIONS: The use of fCGM is recommended for patients with diabetes starting at the time of admission in hospitals in Latin America, whenever the necessary resources (devices, education, personnel) are available.

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Introduction: There are multiple mechanisms by which HbA1c values can be altered in chronic kidney disease (CKD), which limits its usefulness as a strategy to assess glycemic control in this population. Methods: Concordance and agreement study between two diagnostic tests: HbA1c and glucose management indicator (GMI) measured by intermittently scanned continuous glucose monitoring (isCGM), based in a prospective cohort of patients with diabetes, CKD (glomerular filtration rate between 15 and 60 ml/min/1.73 m²), and anemia. The isCGM was performed for 3 months, and the GMI was compared with the HbA1c levels taken at the end of isCGM. Agreement was evaluated using Bland-Altman graph analysis and Lin's concordance correlation coefficient (CCC). The concordance of the measures with good glycemic control (<7%) was also evaluated. Results: A total of 74 patients were enrolled (median age 68.5 years, 51.3% female, 64.9% with CKD stage 3, hemoglobin 11.1 ± 1.2 g/l). The Bland-Altman analysis shows a mean difference between GMI and HbA1c of 0.757 ± 0.687% (95% limits of agreement: -0.590 and 2.105). Difference was greater as the values of GMI and HbA1c increased. The agreement was poor [CCC 0.477; 95% confidence interval (CI): 0.360-0.594], as well as the concordance of values with good glycemic control according to GMI versus HbA1c (67.5% versus 29.7%, p < 0.001) (Kappa 0.2430; 95% CI: 0.16-0.32). Conclusion: The HbA1c overestimates the GMI values with highly variable ranges of difference, which prevents a precise correction factor. isCGM probably is a safer option for monitoring and decision-making in this population, especially in patients treated with insulin where the risk of hypoglycemia is greater.

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OBJECTIVE: To establish recommendations through the consensus of a Latin American experts panel on the use of the flash glucose monitoring system (fCGM) in people living with type 2 diabetes mellitus (T2DM) regarding the benefits and challenges of using the fCGM. METHODS: An executive committee of experts was created, comprised by a panel of fifteen physicians, including endocrinologists and internal medicine physicians, with expertise in management of adult patients with T2DM. The experts were from various countries: Colombia, Chile, Peru, Mexico, Argentina, and Brazil. The modified Delphi method was used, considering a consensus level of at least 80% of the participants. A seventeen-item instrument was developed to establish recommendations on the use of fCGM in patients with T2DM in Latin American. RESULTS: The number of glucose scans recommended per day with the fCGM for patients managed with oral antidiabetic drugs or basal insulin was a median of 6 scans per day, and for those managed with multiple insulin doses, a median of 10 scans per day was recommended. Additionally, a holistic and individualized management approach was recommended, taking into account new treatment directions and identifying patients who would benefit from the use of the fCGM. CONCLUSION: Continuous use of the fCGM is recommended for people living with T2DM, regardless of their type of treatment. These metrics must be evaluated individually for each patient profile.

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AIM: To evaluate the use of intermittently scanned continuous glucose monitoring (isCGM) in patients with liver cirrhosis (LC). METHODS: Observational study including 30 outpatients with LC (Child-Pugh B/C): 10 without diabetes (DM) (G1), 10 with newly diagnosed DM by oral glucose tolerance test (G2), and 10 with a previous DM diagnosis (G3). isCGM (FreeStyle Libre Pro) was used for 56 days (four sensors/patient). Blood tests were performed at baseline and after 28 and 56 days. RESULTS: No differences were found in the baseline characteristics, except for higher age in G3. There were significant differences between G1, G2 and G3 in glucose management indicator (GMI) (5.28 ± 0.17, 6.03 ± 0.59, 6.86 ± 1.08%, P < .001), HbA1c (4.82 ± 0.39, 5.34 ± 1.26, 6.97 ± 1.47%, P < .001), average glucose (82.79 ± 7.06, 113.39 ± 24.32, 149.14 ± 45.31mg/dL, P < .001), time in range (TIR) (70.89 ± 9.76, 80.2 ± 13.55, 57.96 ± 17.96%, P = .006), and glucose variability (26.1 ± 5.0, 28.21 ± 5.39, 35.31 ± 6.85%, P = .004). There was discordance between GMI and HbA1c when all groups were considered together, with a mean difference of 0.35% (95% SD 0.17, 0.63). In G1, the mean difference was 0.46% (95% SD 0.19, 0.73) and in G2 0.69% (95% SD 0.45, 1.33). GMI and HbA1c were concordant in G3, with a mean difference of -0.10 % (95% SD [-0.59, 0.38]). CONCLUSION: Disagreements were found between the GMI and HbA1c levels in patients with LC. isCGM was able to detect abnormalities in glycemic control that would not be detected by monitoring with HbA1c, suggesting that isCGM can be useful in assessing glycemic control in patients with LC.

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BACKGROUND: Diabetes Mellitus (DM) is an important chronic disease that occurs worldwide. AIMS: This study aims to investigate how the use of the FreeStyle® Libre system in Unified Health System (SUS) patients impacts diabetes parameters in patients who receive education on proper insulin administration and the use of the continuous monitoring device, as well as how this affects patients without any concomitant multidisciplinary support in Sergipe, Brazil. METHODS: We conducted a prospective randomized study in a diabetes clinic in Sergipe, Brazil, using the flash method FreeStyle® Libre (Abbott). The participants were divided into two groups: one receiving diabetes education on CGM (continuous glucose monitoring), while the other did not. Before the intervention, the patient's treatment motivation and quality of life were assessed using a questionnaire, and baseline levels of glycated hemoglobin were measured using high-performance liquid chromatography (HPLC) and the point of care AlereTM Afinion with boronate fixation. We compared first- and second-phase data with respect to glycated hemoglobin, mean interstitial blood glucose, time on and above target for hypoglycemic and hyperglycemic events, and mean hypoglycemic duration. RESULTS: In group A, which received the diabetes education intervention, there was a significant reduction in average HbA1c levels from 8.6% to 7.9% after 3 months (p = 0.001). However, there was no significant difference in average glycemic values. Time above target decreased significantly from 50.62% to 29.43% (p = 0.0001), while time below target decreased from 22.90% to 20.21% (p = 0.002). There was no significant change in the number of hypoglycemic events, but the duration of hypoglycemia decreased significantly from 130.35 min to 121.18 min after 3 months (p = 0.0001). In Group B, there was no significant difference in mean HbA1c levels before (7.07%) and after (7.28%) sensor installation. This group maintained lower HbA1c levels compared to the other group. Average blood glucose levels also remained similar before (148.37 mg/dL) and after (154.65 mg/dL) the intervention. Although the time above the target glucose level increased significantly from 35.94% to 48.17%, the time at target decreased from 50.40% to 37.97%. No significant changes were observed in the time below target, the number of hypoglycemic events, or the duration of hypoglycemia. CONCLUSIONS: Our findings indicate that utilizing continuous glucose monitoring technology can enhance glycemic control, particularly in motivated, educated, low-income patients dependent on the SUS. To achieve positive results with FreeStyle Libre, it is imperative to allocate resources for multidisciplinary support.

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Introduction: Continuous glucose monitoring (CGM) has shown favorable outcomes in patients with type 2 diabetes (T2D) who are on insulin therapy. However, the efficacy of CGM in managing glucose levels in noninsulin-treated people with T2D remains controversial. Methods: PubMed, Cochrane, and Embase were searched for randomized controlled trials (RCTs) comparing CGM to self-monitoring of blood glucose (SMBG) in people with T2D not using insulin. We computed weighted mean differences (WMDs) and standard mean differences (SMD) for continuous outcomes, with 95% confidence intervals (CIs). Heterogeneity was assessed using I2 statistics. Statistical analyses were performed using R version 4.2.3. Results: We included six RCTs comprising 407 noninsulin-treated people with T2D of whom 228 were randomized to CGM. Diabetes duration ranged from 5.4 to 13.9 years. The mean age was 57.9 years and the mean body mass index was 30.8 kg/m2. Four trials used real-time CGM (rt-CGM) and two intermittent scanning CGM (is-CGM). Compared with SMBG, CGM significantly reduced the glycated hemoglobin level (WMD -0.31%; 95% CI -0.42 to -0.21; I2 = 0%), glucose level (WMD -11.16 mg/dL; 95% CI -19.94 to -2.39; I2 = 0%), time in hypoglycemia level 2 (WMD -0.28%; 95% CI -0.52 to -0.03; I2 = 91%), glucose time >180 mg/dL (WMD -7.75%; 95% CI -12.04 to -3.45; I2 = 0%), and the standard deviation of glucose variation (WMD -4.00 mg/dL; 95% CI -6.86 to -1.14; I2 = 0%). CGM also increased time in range (WMD 8.63%; 95% CI 4.54-12.71; I2 = 0%) and treatment satisfaction (SMD 0.79; 95% CI 0.54-1.05; I2 = 0%). Conclusion: In this meta-analysis, rt-CGM and is-CGM were associated with improvement in glycemic control in people with T2D not using insulin when compared to SMBG.

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Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

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Objetivo: analizar la correlación entre el tiempo en rango y la hemoglobina glicosilada de personas que viven con diabetes mellitus y realizan la monitorización continua de la glucemia o el automonitoreo de la glucemia capilar Método: revisión sistemática de etiología y riesgo basada en las directrices del JBI e informada según los Preferred Reporting Items for Systematic Reviews and Meta-Analyses, abarcando seis bases de datos y la literatura gris. La muestra incluyó 16 estudios y la calidad metodológica fue evaluada utilizando las herramientas del JBI. Protocolo registrado en Open Science Framework, disponible en https://doi.org/10.17605/OSF.IO/NKMZB. Resultados: tiempo en rango (70-180 mg/dl) mostró una correlación negativa con la hemoglobina glicosilada, mientras que el tiempo por encima del rango (>180 mg/dl) mostró una correlación positiva. Los coeficientes de correlación variaron entre -0,310 y -0,869 para el tiempo en rango, y entre 0,66 y 0,934 para el tiempo por encima del rango. Un estudio se realizó en una población que hacía el automonitoreo. Conclusión: hay una correlación estadísticamente significativa entre el tiempo en rango y el tiempo por encima del rango con la hemoglobina glicosilada. Cuanto mayor sea la proporción en el rango glucémico adecuado, más cerca o por debajo del 7% estará la hemoglobina glicosilada. Se necesitan más estudios que evalúen esta métrica con datos del automonitoreo de la glucemia.

Objective: to analyze the correlation between time on target and glycated hemoglobin in people living with diabetes mellitus and carrying out continuous blood glucose monitoring or self-monitoring of capillary blood glucose. Method: systematic review of etiology and risk based on JBI guidelines and reported according to Preferred Reporting Items for Systematic Reviews and Meta- Analyses, covering six databases and grey literature. The sample included 16 studies and methodological quality was assessed using JBI tools. Protocol registered in the Open Science Framework, available at https://doi.org/10.17605/OSF.IO/NKMZB. Results: time on target (70-180 mg/dl) showed a negative correlation with glycated hemoglobin, while time above target (>180 mg/dl) showed a positive correlation. Correlation coefficients ranged between -0.310 and -0.869 for time on target, and between 0.66 and 0.934 for time above target. A study was carried out on a population that performed self-monitoring. Conclusion: there is a statistically significant correlation between time on target and time above target with glycated hemoglobin. The higher the proportion in the adequate glycemic range, the closer to or less than 7% the glycated hemoglobin will be. More studies are needed to evaluate this metric with data from self-monitoring of blood glucose.

Objetivo: analisar a correlação entre o tempo no alvo e a hemoglobina glicada de pessoas que vivem com diabetes mellitus e realizam a monitorização contínua da glicemia ou a automonitorização da glicemia capilar. Método: revisão sistemática de etiologia e de risco pautada nas diretrizes do JBI e reportada conforme Preferred Reporting Items for Systematic Reviews and Meta-Analyses, abrangendo seis bases de dados e a literatura cinzenta. A amostra incluiu 16 estudos e a qualidade metodológica foi avaliada utilizando as ferramentas do JBI. Registrado protocolo no Open Science Framework, disponível em https://doi.org/10.17605/OSF.IO/NKMZB. Resultados: tempo no alvo (70-180 mg/dl) apresentou correlação negativa com a hemoglobina glicada, enquanto o tempo acima do alvo (>180 mg/dl) mostrou correlação positiva. Os coeficientes de correlação variaram entre -0,310 e -0,869 para o tempo no alvo, e entre 0,66 e 0,934 para o tempo acima do alvo. Um estudo foi efetuado com população que realizava a automonitorização. Conclusão: há correlação estatisticamente significativa entre o tempo no alvo e o tempo acima do alvo com a hemoglobina glicada. Quanto maior a proporção na faixa glicêmica adequada, mais próxima ou inferior a 7% estará a hemoglobina glicada. São necessários mais estudos que avaliem essa métrica com dados da automonitorização da glicemia.

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Objective: The Diabetes Device Confidence Scale (DDCS) is a new scale designed to evaluate school nurse confidence with diabetes devices. We hypothesized that DDCS score would be associated with related constructs of school nurse diabetes knowledge, experience, and training. Research Design and Methods: In a cross-sectional study, we co-administered the DDCS and Diabetes Knowledge Test 2 (DKT2) questionnaires to school nurses in Pennsylvania. We summarized DDCS scores (range 1-5) descriptively. We evaluated the relationship between DKT2 percent score and DDCS mean score with the Spearman correlation coefficient. Simple linear regression examined school nurse characteristics as predictors of DDCS score. Results: A total of 271 completed surveys were received. Mean DDCS score was 3.16±0.94, indicating moderate confidence with devices overall. School nurses frequently reported low confidence in items representing specific skills, including suspending insulin delivery (40%), giving a manual bolus (42%), knowing when to calibrate a continuous glucose monitor (48%), changing an insulin pump site (54%), and setting a temporary basal rate (58%). Mean DKT2 score was 89.5±0.1%, which was weakly but not significantly correlated with DDCS score (r=0.12, p=0.06). Formal device training (p<0.001), assisting ≥5 students with diabetes devices in the past 5 years (p<0.01), and a student caseload between 1000-1500 students (p<0.001) were associated with higher mean DDCS score. Conclusions: DDCS score is related to prior training and experience, providing evidence for the scale's convergent validity. The DDCS may be a useful tool for assessing school nurse readiness to use devices and identify areas to enhance knowledge and practical skills.

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Objective: Using continuous glucose monitoring (CGM), we examined patterns in glycemia during school hours for children with type 1 diabetes, exploring differences between school and non-school time. Methods: We conducted a retrospective analysis of CGM metrics in children 7-12 years (n=217, diabetes duration 3.5±2.5 years, hemoglobin A1c 7.5±0.8%). Metrics were obtained for weekday school hours (8 AM to 3 PM) during four weeks in fall 2019. Two comparison settings included weekend (fall 2019) and weekday (spring 2020) data when children had transitioned to virtual school due to COVID-19. We used multilevel mixed models to examine factors associated with time in range (TIR) and compare glycemia between in-school, weekends, and virtual school. Results: Though CGM metrics were clinically similar across settings, TIR was statistically higher, and time above range (TAR), mean glucose, and standard deviation (SD) lower, for weekends and virtual school (p<0.001). Hour and setting exhibited a significant interaction for several metrics (p<0.001). TIR in-school improved from a mean of 40.9% at the start of the school day to 58.0% later in school, with a corresponding decrease in TAR. TIR decreased on weekends (60.8 to 50.7%) and virtual school (62.2 to 47.8%) during the same interval. Mean glucose exhibited a similar pattern, though there was little change in SD. Younger age (p=0.006), lower hemoglobin A1c (p<0.001), and insulin pump use (p=0.02) were associated with higher TIR in-school. Conclusion: Although TIR was higher for weekends and virtual school, glycemic metrics improve while in-school, possibly related to beneficial school day routines.

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BACKGROUND: There is evidence in the literature that the use of sensor-augmented insulin pumps in patients with high-complexity diabetes improves metabolic control. However, there is no long-term information on clinical outcomes such as hospitalization or admission to the emergency room. This study describes outcomes for metabolic control, incidence of hospitalizations, and emergency room visits in a specific population using this technology. OBJECTIVE: We aimed to assess long-term glycemic and clinical outcomes after the use of continuous subcutaneous insulin infusion and continuous glucose monitoring in people with diabetes. METHODS: A retrospective cohort study was carried out in patients with diabetes previously treated with an intensive insulin regimen at a specialized diabetes treatment center who required a sensor-augmented insulin pump due to nonoptimal glycemic control. Glycated hemoglobin, severe hypoglycemic episodes, nonsevere hypoglycemic episodes, perception of hypoglycemia, and the incidence of emergency room visits and hospitalizations before and after treatment were evaluated. RESULTS: Between January 2013 and August 2020, 74 patients with a median age of 36 (IQR 27-46) years were included in the study with a median 4 (IQR 2-7) years of follow-up. We found a statistically significant reduction in glycated hemoglobin (8.35% vs 7%; P<.001), nonsevere hypoglycemic episodes (71/74, 96% vs 62/74, 84%; P=.01), emergency room visits (42/73, 58% vs 4/62, 6%; P<.001), and hospitalizations (36/72, 50% vs 10/72, 14%; P<.001) after use of continuous subcutaneous insulin infusion. CONCLUSIONS: The use of a sensor-augmented insulin pump associated with a strict follow-up program for patients with high-complexity diabetes led to a significant and sustained reduction in glycated hemoglobin and hypoglycemic episodes, as well as in the rate of emergency room visits and hospitalizations. These results encourage the adoption of this technology in patients who do not achieve metabolic control with optimal management of diabetes.

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Background/Objective: Wolfram syndrome (WS) is a rare genetic disorder, in which patients develop early-onset diabetes mellitus (DM), optic nerve atrophy, and neurodegeneration, which has no specific treatment available. Here, we report 2 brothers treated with an insulin pump to manage the alterations of the glycemic levels due to the DM. Case Report: We present the case of 2 siblings diagnosed with Wolfram syndrome 1, they presented with typical endocrinological and neurodegenerative early manifestations, one brother was treated with a sensor-augmented insulin infusion system, and the other with an insulin pump. Both reached a better metabolic state and had improved quality of life. Discussion: The management of WS is still a challenge; however, the use of a sensor-augmented insulin infusion system and the information that it provides may offer better care to patients who require frequent monitoring and adjustments in their treatment. It has been reported that the neurodegenerative progression of WS is also associated with high glucose peaks; therefore, it is necessary to control it, even when it is hard due to the difficult-to-manage DM. There is only 1 previous case report of WS with insulin pump that describes the benefits of continuous subcutaneous insulin infusion and tight metabolic control during pregnancy. Conclusion: The use of insulin pumps may be an effective treatment for DM in WS patients, mainly in terms of improving the prognosis of difficult-to-manage DM.

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ABSTRACT Objectives: To evaluate the alternate use of flash glucose monitoring (FGM) with self-monitoring blood glucose (SMBG), in patients with type 1 diabetes (T1D). Materials and methods: Two weeks of open FGM (P2), both preceded (P1) and followed by 2 weeks (P3) of SMBG with a blinded FGM system. Mean absolute relative difference (MARD) was calculated by (-FGMi − SMBGi-) / SMBGi, where it was a paired data sample. Results: In total, 34 patients were evaluated. Time in range (TIR) did not change between P1 and P2. In 12 patients (35.3%), TIR increased from 40% at P1 to 52% at P2 (p = 0.002) and in 22 (64.7%), TIR decreased or did not change. FGM use resulted in decreased % time spent in hypoglycemia (<70 mg/dL) (6.5% vs. 5.0%; p = 0.005), increased % time spent in hyperglycemia (>180 mg/dL) (44.5% to 51%; p = 0.046) with no significant change in % TIR. The proportion of patients who reached sensor-estimated glycated hemoglobin (eA1c) < 7% decreased from 23.5% at P1 to 12.9% at P2, p = 0.028. For the whole sample, the MARD between the two methods was 15.5% (95% CI 14.5-16.6%). For normal glucose range, hyperglycemic levels and hypoglycemic levels MARD were 16.0% (95% CI 15.0-17.0%), 13.3% (95% CI 11.5-15.2%) and 23.4% [95% CI 20.5-26.3%)], respectively. Conclusion: FGM after usual SMBG decreased the % time spent in hypoglycemia concomitant with an undesired increase in % time spent in hyperglycemia. Lower accuracy of FGM regarding hypoglycemia levels could result in overcorrection of hypoglycemia.

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INTRODUCTION: Continuous subcutaneous insulin infusion (CSII) has emerged as a potential solution for diabetes management during the pandemic, as it reduces the need for in-person visits and allows for remote monitoring of patients. Telemedicine has also become increasingly important in the management of diabetes during the pandemic, as it allows healthcare providers to provide remote consultations and support. Here, we discuss the implications of this approach for diabetes management beyond the pandemic, including the potential for increased access to care and improved patient outcomes. METHODS: We performed a longitudinal observational study between 1 March 2020 and 31 December 2020 to evaluate glycemic parameters in diabetic patients with CSII in a telehealth service. Glycemic parameters were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability control. RESULTS: A total of 36 patients were included in the study, with 29 having type 1 diabetes and 6 having type 2 diabetes. The study found that the proportion of patients achieving target glucose variability and GMI remained unchanged during follow-up. However, in patients with type 2 diabetes, the time in target range increased from 70% to 80%, and the time in hyperglycemia decreased from 2% to 0%. CONCLUSIONS: The results of this study suggest that telemedicine is a strategy for maintaining glycemic control in patients using CSII. However, the lack of access to the internet and adequate telemonitoring devices make it difficult to use on a large scale in emerging countries like ours.

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INTRODUCTION: There are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established. METHODS: Study of diagnostic tests. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+ app, in diabetic patients managed with insulin therapy. RESULTS: Twenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+ app (81.92% vs. 68.67%, p=0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip+ and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p=0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p=0.0273), being higher with FreeStyle libre. CONCLUSIONS: The use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+ app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.

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AIM: We studied real-world performance of MiniMed (MM) 780G system users from Argentina, Brazil, Colombia and Chile (geographical analysis), and the effect of each technology iteration of the MM system on glycaemic control (technology iteration analysis). MATERIALS AND METHODS: CareLink data from August 2020 to September 2022 were extracted. Endpoints included continuous glucose monitoring metrics. For the geographical analysis, aggregated endpoints for MM780G system users were calculated. For the technology iteration analysis, MM780G system user outcomes were compared with outcomes when the same individuals were still using the MM640G or MM670G system. RESULTS: On average, 1025 MM780G system users from the geographical analysis were followed for 136 (SD 135) days, spent 91.5 (14.3)% in advanced hybrid closed loop, showed a glucose management indicator (GMI) of 6.7 (0.3)%, a time in range between 70 and 180 mg/dl (TIR) of 76.5 (9.0)%, and a time below range 70 mg/dl (TBR) of 2.7 (2.1)%. The percentage of users reaching targets of GMI <7%, TIR >70% and TBR <4% was 80.8%, 78.1% and 80.1%, respectively. The technology iteration analysis on users transitioning from MM640G to MM780G system (N = 381) showed 0.4% decrease in GMI (7.1% to 6.7%, p < .0001), 10.7% increase in TIR (65.9% to 76.6%, p < .0001), while TBR remained. The percentage of insulin delivered automatically increased as well (47.5%-57.7%, p < .0001). Users transitioning from MM670G system (N = 78) showed a similar but less pronounced pattern. CONCLUSIONS: Real-world Latin American MM780G users on average showed good glucose control, achieving international targets. Glycaemic control increased with every technology iteration of the MM system, providing more automation each time.

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OBJECTIVE: HbA1C is the "gold standard" parameter to evaluate glycemic control in diabetes; however, its correlation with mean glucose is not always perfect. The objective of this study was to correlate continuous glucose monitoring (CGM)-derived hemoglobin glycation index (HGI) with microvascular complications. METHODS: We conducted a cross-sectional study including permanent users of CGM with type 1 diabetes mellitus or latent autoimmune diabetes of the adult. HGI was estimated, and presence of microvascular complications was compared in subgroups with high or low HGI. A logistic regression analysis to assess the contribution of high HGI to chronic kidney disease (CKD) was performed. RESULTS: In total, 52 participants who were aged 39.7 ± 14.7 years, with 73.1% women and 15.5 years (IQR, 7.5-29 years) since diagnosis, were included; 32.7% recorded diabetic retinopathy, 25% CKD, and 19.2% neuropathy. The median HbA1C was 7.6% (60 mmol/mol) and glucose management indicator (GMI) 7.0% (53 mmol/mol). The average HGI was 0.55% ± 0.66%. The measured HbA1C was higher in the group with high HGI (8.1% [65 mmol/mol] vs 6.9% [52 mmol/mol]; P < .001), whereas GMI (7.0% [53 mmol/mol] vs 7.0% [53 mmol/mol]; P = .495) and mean glucose were similar in both groups (153 mg/dL vs 153 mg/dL; P = .564). In the high HGI group, higher occurrence of CKD (P = .016) and neuropathy were observed (P = .025). High HGI was associated with increased risk of CKD (odds ratio [OR]: 5.05; 95% CI: 1.02-24.8; P = .04) after adjusting for time since diagnosis (OR: 1.09; 95% CI: 1.02-1.16; P = .008). CONCLUSION: High HGI measured by CGM may be a useful marker for increased risk of microvascular diabetic complications.

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Objective: To evaluate the alternate use of flash glucose monitoring (FGM) with self-monitoring blood glucose (SMBG), in patients with type 1 diabetes (T1D). Materials and methods: Two weeks of open FGM (P2), both preceded (P1) and followed by 2 weeks (P3) of SMBG with a blinded FGM system. Mean absolute relative difference (MARD) was calculated by (|FGMi - SMBGi|) / SMBGi, where it was a paired data sample. Results: In total, 34 patients were evaluated. Time in range (TIR) did not change between P1 and P2. In 12 patients (35.3%), TIR increased from 40% at P1 to 52% at P2 (p = 0.002) and in 22 (64.7%), TIR decreased or did not change. FGM use resulted in decreased % time spent in hypoglycemia (<70 mg/dL) (6.5% vs. 5.0%; p = 0.005), increased % time spent in hyperglycemia (>180 mg/dL) (44.5% to 51%; p = 0.046) with no significant change in % TIR. The proportion of patients who reached sensor-estimated glycated hemoglobin (eA1c) < 7% decreased from 23.5% at P1 to 12.9% at P2, p = 0.028. For the whole sample, the MARD between the two methods was 15.5% (95% CI 14.5-16.6%). For normal glucose range, hyperglycemic levels and hypoglycemic levels MARD were 16.0% (95% CI 15.0-17.0%), 13.3% (95% CI 11.5-15.2%) and 23.4% [95% CI 20.5-26.3%)], respectively. Conclusion: FGM after usual SMBG decreased the % time spent in hypoglycemia concomitant with an undesired increase in % time spent in hyperglycemia. Lower accuracy of FGM regarding hypoglycemia levels could result in overcorrection of hypoglycemia.

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OBJETIVO: Evaluar el tiempo en rango de glucosa y su asociación con otras medidas del control glicémico establecidas por el consenso internacional del tiempo en rango en usuarios de vida real del sistema flash de monitorización de glucosa FreeStyle LibreTM en Chile. MÉTODOS: Se analizaron los datos provenientes de la base de datos Freestyle Libre™ entre diciembre de 2014 y enero de 2022. Las lecturas se dividieron en 10 grupos (deciles) del mismo tamaño (cada decil contenía aproximadamente 498 usuarios) en función del tiempo en rango. Para cada decil se calculó la media de determinaciones diarias, el promedio de glucosa, la HbA1c, la desviación estándar de glucosa, el coeficiente de variación de la glucosa, el tiempo en rango, el tiempo de glucosa (porcentaje) por encima de 250 mg/dL (TA250), el tiempo de glucosa (porcentaje) por encima de 180 mg/dL (TA180), el tiempo por debajo (porcentaje) de 70 mg/dL (TB70) y el tiempo por debajo (porcentaje) de 54 mg/dL (TB54). RESULTADOS: Desde diciembre de 2014 hasta enero de 2022 hubo 4984 lectores. El grupo con el mayor tiempo en rango mostró significativamente una menor glucosa promedio que el grupo con el tiempo en rango más bajo (decil 1: media 248,3 mg/dL, decil 10: media 113,2 mg/L, diferencia ­135,1 mg/dL, p<0.05). Asimismo, el mayor tiempo en rango se asoció con una menor desviación estándar (decil 1: media 93,7mg/dL, decil 10: media 26,7mg/L, diferencia: -67,0 mg/ dL, p<0,05), menor coeficiente de variación (decil 1: media 37,8%, decil 10: media 23,3%, diferencia: -14,5%, p<0,05), menor TA250 (decil 1: media 46,5%, decil 10: media 0,2%, diferencia: -46,3%, p<0.05), menor TA180 (decil 1: media 73,9%, decil 10: media 3,8%, diferencia: -70,1%, p<0.05), menor TB70 (decil 5: mediana 6,13%, decil 10: mediana 1,70%, diferencia: -4,43%, p<0.05) y menor TB54 (decil 5: mediana 1,79%, decil 10: mediana 0,12%, diferencia: -1,67%, p<0.05). El mayor tiempo en rango se asoció también significativamente con más determinaciones diarias (decil 1: media 11,4, decil 10: media 16,6, diferencia: 5,2, p<0,05). La frecuencia media de las determinaciones entre todos los lectores fue de 14,7 determinaciones diarias. CONCLUSIONES: En los pacientes con diabetes en Chile, el empleo del sistema flash de monitorización demuestra la asociación entre el mayor tiempo en rango, la reducción de la variabilidad de la glucosa y un menor riesgo de hiperglucemias e hipoglicemias y también con un mayor compromiso.

OBJECTIVE: To evaluate glucose time in range and its association with other metrics of glucose control established by the International Consensus on TIR amongst real-life patients using the Flash Glucose Monitoring system FreeStyle LibreTM in Chile. METHODS: Data from the Freestyle Libre™ database between December 2014 and January 2022 were analyzed. Readers were divided into 10 groups (deciles) of the same size (each decile had approximately 498 users) according to time in range. For each decile of time in range, the mean of daily scans, average glucose, estimated HbA1c, glucose standard deviation, glucose coefficient of variation, time in range, glucose time (percentage) above 250 mg/dL (TA250), and glucose time (percentage) above 180 mg/dL (TA180), and the median of glucose time (percentage) below 70 mg/dL (TB70) and glucose time (percentage) below 54 mg/dL (TB54), were calculated. RESULTS: From December 2014 to January 2022, there were 4984 readers. The group with the highest TIR showed significantly lower average glucose than the group with the lowest TIR (decile 1: mean 248.3 mg/dL, decile 10: mean 113.2 mg/L, difference: ­135.1 mg/dL, p<0.05). In addition, more time in range was associated with a lower glucose standard deviation (decile 1: mean 93.7 mg/dL, decile 10: mean 26.7 mg/L, difference: -67.0 mg/dL, p<0.05), lower glucose coefficient of variation (decile 1: mean 37.8%, decile 10: mean 23.3%, difference: -14.5%, p<0.05), lower TA250 (decile 1: mean 46.5%, decile 10: mean 0.2%, difference: -46.3%, p<0.05),lower TA180 (decile 1: mean 73.9%, decile 10: mean 3.8%, difference: -70.1%, p<0.05), lower TB70 (decile 5: median 6.13%, decile 10: median 1.70%, difference: -4.43%, p<0.05) and lower TB54 (decile 5: median 1.79%, decile 10: median 0.12%, difference: -1.67%, p<0.05). Greater TIR was also associated with significantly more daily scans (decile 1: mean 11.4, decile 10: mean 16.6, difference: 5.2, p<0.05). Mean scan frequency amongst all readers was 14.7 daily scans. CONCLUSIONS: In patients with diabetes from Chile, the use of the flash glucose monitoring system demonstrates the association between greater TIR, reduced glucose variability, and reduced risk of hyperglycemia and hypoglycemia, and also its association with greater engagement.

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BACKGROUND: Diabetes mellitus (DM) has glucose variability that is of such relevance that the appearance of vascular complications in patients with DM has been attributed to hyperglycemic and dysglycemic events. It is known that T1D patients mainly have glycemic variability with a specific oscillatory pattern with specific circadian characteristics for each patient. However, it has not yet been determined whether an oscillation pattern represents the variability of glycemic in T2D. This is why our objective is to determine the characteristics of glycemic oscillations in T2D and generate a robust predictive model. RESULTS: Showed that glycosylated hemoglobin, glycemia, and body mass index were all higher in patients with T2D than in controls (all p < 0.05). In addition, time in hyperglycemia and euglycemia was markedly higher and lower in the T2D group (p < 0.05), without significant differences for time in hypoglycemia. Standard deviation, coefficient of variation, and total power of glycemia were significantly higher in the T2D group than Control group (all p < 0.05). The oscillatory patterns were significantly different between groups (p = 0.032): the control group was mainly distributed at 2-3 and 6 days, whereas the T2D group showed a more homogeneous distribution across 2-3-to-6 days. CONCLUSIONS: The predictive model of glycemia showed that it is possible to accurately predict hyper- and hypoglycemia events. Thus, T2D patients exhibit specific oscillatory patterns of glycemic control, which are possible to predict. These findings may help to improve the treatment of DM by considering the individual oscillatory patterns of patients.

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