Time-in-range derived from self-measured blood glucose in people with type 2 diabetes advancing to iGlarLixi: A participant-level pooled analysis of three phase 3 LixiLan randomized controlled trials.

AIM: To evaluate the efficacy of a fixed-ratio combination of insulin glargine 100 U/mL plus lixisenatide (iGlarLixi) in people with type 2 diabetes (T2D) using derived time-in-range (dTIR). METHODS: Participant-level data from LixiLan-L, LixiLan-O and LixiLan-G were pooled and dTIR (70-180 mg/dL), derived time-above-range (> 180 mg/dL) and derived time-below-range (dTBR; < 70 mg/dL) were calculated from participant seven-point self-monitored blood glucose profiles. RESULTS: This pooled analysis included data from 2420 participants receiving iGlarLixi (n = 1093), iGlar (n = 836), Lixi (n = 234) or a glucagon-like peptide-1 receptor agonist (GLP-1 RA) (n = 257). Numerically greater improvements in least square (LS) means dTIR were seen from baseline to end of treatment (EOT) with iGlarLixi (25.7%) versus iGlar (15.8%), Lixi (11.7%) or GLP-1 RA (16.2%). At EOT, the mean (standard deviation) dTBR was 0.71% ± 3.4%, 0.61% ± 3.2%, 0.08% ± 1.0% and 0.0% ± 0.0% for iGlarLixi, iGlar, Lixi and GLP-1 RA, respectively. In a subgroup analysis, participants aged younger than 65 years (n = 1690) and 65 years or older (n = 713) showed numerically greater improvements in LS means dTIR from baseline to EOT with iGlarLixi versus iGlar, Lixi or GLP-1 RA. CONCLUSIONS: iGlarLixi achieved improvements in dTIR, with low dTBR values, providing further evidence to inform clinical outcomes with the use of iGlarLixi.

Performance of the MiniMed 780G system on mitigating menstrual cycle-dependent glycaemic variability.

AIM: To map the glycaemic variabilities and insulin requirements across different phases of the menstrual cycle and assess the efficacy and performance of the MiniMed 780G system on mitigating glycaemic variabilities during phases of the menstrual cycle. MATERIALS AND METHODS: A pilot study recruiting 15 adolescent and young adult females with type 1 diabetes was conducted. Only females with regular spontaneous menstruation were enrolled in the current study. Phases of each menstrual cycle were determined as either follicular phase or luteal phase. The study analysed continuous glucose monitoring metrics during two study periods: the open loop period (OLP) and the advanced hybrid closed-loop (AHCL) period; each period lasted 3 consecutive months. RESULTS: During the OLP, the mean time in range (TIR) significantly decreased during the luteal phase compared with the follicular phase (65.13% ± 3.07% vs. 70.73% ± 2.05%) (P < .01). The mean time above range significantly increased from 21.07% ± 2.58% during the follicular phase to 24.87% ± 2.97% during the luteal phase (P < .01). After initiating the AHCL period, TIR was comparable during both phases of the menstrual cycle (P = .72), without increasing the time spent below 70 mg/dL (P > .05). Regarding insulin delivery during the AHCL period, the percentage of Auto basal and Auto correction delivered by the algorithm increased by 13.55% and 30.6%, respectively (P < .01), during the luteal phase. CONCLUSIONS: The fully automated adaptive algorithm of the MiniMed 780G system mitigated menstrual cycle-dependent glycaemic variability, successfully attaining the recommended glycaemic outcomes with a TIR greater than 70% throughout the entire menstrual cycle.

IDegLira improves time in range in a cohort of patients with type 2 diabetes: TiREX study.

AIMS: To assess the effects of IDegLira on glucometric indices deriving from intermittently scanned Continuous Glucose Monitoring (isCGM) in patients with type 2 diabetes (T2D). METHODS: Retrospective, observational, cohort, multi-center, "pre - post" study. All adults consecutively identified in the medical records who started treatment with IDegLira, and for whom an isCGM report before and after the initiation of IDegLira was available were included in the study. Time in range (TIR) represented the primary endpoint. Additional glucometric indices, insulin doses and body weight were also assessed. RESULTS: Overall, 87 patients were included by 5 diabetes centers [mean age 70.2 ± 11.0 years, mean duration of T2D 15.5 ± 9.6 years; BMI 29.4 ± 5.4 kg/m2, baseline HbA1c 9.1 ± 2.1%, 33% insulin naïve, 20.7% treated with basal-oral therapy (BOT), and 46% treated with multiple daily injections of insulin (MDI)]. After an average of 1.7 weeks from IDegLira initiation, TIR significantly increased from 56.8 ± 23.5% to 81.3 ± 13.5% (p < 0.0001), TAR decreased from 42.3 ± 24.2% to 17.1 ± 13.6% (p < 0.0001), while TBR remained steadily low (from 1.3 ± 2.3% to 1.4 ± 2.6%; p = 0.62). Estimated HbA1c decreased from 9.1 ± 2.1% to 6.7 ± 0.6% (p < 0.0001) and percentage of patients with a blood glucose coefficient of variation ≥ 36% dropped from 33.2 to 13.8% (p = 0.0005). In patients on MDI, the reduction in the total insulin dose was substantial (from 55.8 ± 31.2 IU to 27.2 ± 12.3 U). CONCLUSIONS: In T2D patients with poor metabolic control, either insulin naïve or treated with BOT or MDI, the introduction of IDegLira produces a significant increase in the time spent in good metabolic control and a marked reduction in glycemic fluctuations.

Clinical relevance of short-term glycemic variability in children and adolescents with type 1 diabetes: a narrative review.

Background and Objective: In recent years, there has been growing interest in glycemic variability within the scientific community, particularly regarding its potential as an independent risk factor for diabetes-related long-term complications. This narrative review aimed to provide a comprehensive overview of short-term glycemic variability in children and adolescents with type 1 diabetes (T1D). Methods: We performed a search of published literature on the PubMed MEDLINE database using the following combination of search terms: "glycemic variability", "pediatric", "type 1 diabetes", and "children". Key Content and Findings: The widespread use of continuous glucose monitoring (CGM) systems has facilitated the characterization and quantification of glycemic fluctuations. Over the years, several metrics for assessing glycemic variability have been developed. Children and adolescents with T1D often experience wide and frequent glycemic excursions due to behavioral and hormonal factors. Several studies suggest a potential link between glycemic variability and an increased risk of diabetes-related complications. Conclusions: Glycemic variability has become an integral aspect of the routine clinical management of youths with T1D, serving as a valuable therapeutic target. However, achieving recommended glycemic targets in this population remains challenging. Further long-term data are needed to definitively establish the role of glycemic variability in the development of complications.

Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes.

BACKGROUND: The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D). METHODS: A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID. Secondary outcomes included time above/below range (TAR/TBR) and total daily insulin (TDI). RESULTS: Compared with PLGS, AID increased TIR on average from 63.2% to 72.6%, decreased TAR from 36.2% to 26.8%, and increased TDI from 70.2 to 76.3 U (all P < .001), without significant change to TBR. Glycemic improvements were more pronounced in those with worse glycemic control during PLGS use (P < .001). CONCLUSIONS: Real-life use of AID led to a rapid and sustained improvement of glycemic control in individuals with T2D.

[Continuous Glucose Monitoring: A Paradigm Shift]. / Monitorización continua de glucosa: cambio de paradigma.

The new method of monitoring patients with diabetes using continuous glucose sensors allows for a more intimate understanding of the patient. Continuous glucose monitoring also enables the immediate adjustment of treatment. This editorial encourages community pharmacists to adapt and evolve, embracing the new paradigm that allows for closer connection with the patient and helps them interpret the data generated.

The Relationship Between Hemoglobin A1c, Time in Range, and Glycemic Management Indicator in Patients With Type 1 and Type 2 Diabetes in a Tertiary Care Hospital in Saudi Arabia.

OBJECTIVES: This study aimed to assess the correlation between glycated hemoglobin A1 (HbA1c), time in range (TIR), and glycemic management indicator (GMI) in patients with both type 1 diabetes (T1D) and type 2 diabetes (T2D) who were using a flash glucose monitoring (FGM) device (FreeStyle Libre; Abbott Diabetic Care, Witney, UK). METHODS: This was a retrospective study that looked at T1D and T2D FreeStyle Libre users' LibreView database in the period between January 2020 to June 2022. The study was conducted at the diabetes department at the King Fahad Medical City (KFMC) in Riyadh, Saudi Arabia, following Institutional Review Board (IRB) approval. Data were collected from the LibreView website, as well as from the electronic privacy information center (EPIC) hospital records. RESULTS: Data were available for 327 patients, mean age of 33.08(±17.1) years old, and 55.7% were females. HbA1c had a statistically significant correlation with both TIR and GMI with coefficient of correlation (r) values of 0.78 (p<0.001) and 0.82 (p<0.001), respectively. A linear regression model between TIR and Hb1Ac was also developed and found to be statistically significant (p<0.001) with an acceptable R2 value (0.60). CONCLUSION: Study findings revealed that the %TIR could be a reliable predictor of Hb1Ac. Thus, Freestyle Libre was able to determine Hb1Ac as close to the lab results as possible. Therefore, it is necessary to encourage diabetes patients to achieve at least 70% TIR in order to keep Hb1Ac within the desired range.

A sensor-augmented pump with a predictive low-glucose suspend system could lead to an optimal time in target range during pregnancy in Japanese women with type 1 diabetes.

Introduction: It is challenging for pregnant women with type 1 diabetes to maintain optimum glucose level to attain good neonatal outcomes. This study evaluated the efficacy of sensor-augmented insulin pump (SAP) with a predictive low-glucose suspend (PLGS) system in pregnant Japanese women with type 1 diabetes. Materials and methods: SAP with PLGS was used in 11 of the 22 women with type 1 diabetes who delivered between 2011 and 2021 at the two medical institutions in Japan. Glucose management, insulin delivery suspension time (IST) and neonatal outcomes were retrospectively studied. Results: In SAP with PLGS cases (n = 11), average glycated hemoglobin levels were < 6.5% throughout the pregnancy, and the time in range (TIR, 63-140 mg/dl) was > 70% in the second and third trimesters. PLGS was safely used without inducing ketoacidosis. Positive correlation was observed between IST and TIR (r = 0.62, p < 0.01). Negative correlation was observed between IST and time below range (TBR) (r = - 0.40, p = 0.02), and IST and time above range (TAR) (r = - 0.45, p = 0.01). Total daily insulin dose was adequately increased without increasing hypoglycemia. There was only one heavy-for-date HFD) infant among the 11 newborns in SAP with PLGS cases. In cases without SAP (n = 11), target glycemic levels were difficult to achieve and there were 5 HFD infants among the 11 newborns. Conclusion: SAP with PLGS was safely and effectively used in pregnant women with type 1 diabetes to achieve target glucose levels without increasing the risk of hypoglycemia, which may have led to good neonatal outcomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13340-024-00716-7.

Relationship Between Glycosylated Hemoglobin Variability and the Severity of Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus.

Background: Glycosylated hemoglobin (HbA1c) variability is a risk factor for cardiovascular complications in patients with Type 2 diabetes mellitus (T2DM), but its relationship with the severity of coronary artery disease (CAD) is unclear. Methods: Patients with T2DM who underwent coronary angiography due to angina were enrolled. HbA1c variability was expressed as coefficient of variation (CV), standard deviation (SD), variability independent of mean (VIM), and time in range (TIR). The severity of CAD was expressed by the number of involved vessels and Gensini score. Multivariate regression models were constructed to test the relationship between HbA1c variability, number of involved vessels, and the Gensini score, followed by linear regression analysis. Results: A total of 147 patients were included. In multivariate analysis, VIM-HbA1c (OR = 2.604; IQR: 1.15, 5.90; r = 0.026) and HbA1cTIR (OR = 0.13; IQR: 0.04, 0.41; r < 0.001) were independent risk factors for the number of involved vessels. After adjustment, HbA1cTIR (OR = 0.01; IQR: 0.002, 0.04; r < 0.001), SD-HbA1c (OR = 4.12, IQR: 1.64, 10.35; r = 0.001), CV-HbA1c (OR = 1.41, IQR: 1.04, 1.92; r = 0.007), and VIM-HbA1c (OR = 3.26; IQR: 1.43, 7.47; r = 0.003) were independent risk factors for the Gensini score. In the linear analysis, the Gensini score was negatively correlated with HbA1cTIR (ß = -0.629; r < 0.001) and positively correlated with SD-HbA1c (ß = 0.271; r = 0.001) and CV-HbA1c (ß = 0.176; r = 0.033). After subgroup analysis, HbA1cTIR was a risk factor for the number of involved vessels. The Gensini score was negatively correlated with HbA1cTIR and positively correlated with SD-HbA1c at subgroups of subjects with a mean HbA1c ≤ 7%. Conclusions: Our analysis indicates that HbA1c variability, especially HbA1cTIR, plays a role for the severity of CAD in patients with T2DM. HbA1c variability may provide additional information and require management even at the glycemic target. Translational Aspects: Studies have shown that HbA1c variability is related to cardiovascular complications. Further, we explore the correlation between HbA1c variability and the severity of CAD. HbA1c variability is a risk factor for coronary stenosis in T2DM. It may be a potential indicator reflecting glycemic control for the prevention and treatment of cardiovascular complications.

Exploring the Continuous Glucose Monitoring in Pediatric Diabetes: Current Practices, Innovative Metrics, and Future Implications.

Continuous glucose monitoring (CGM) systems, including real-time CGM and intermittently scanned CGM, have revolutionized diabetes management, particularly in children and adolescents with type 1 diabetes (T1D). These systems provide detailed insights into glucose variability and detect asymptomatic and nocturnal hypoglycemia, addressing limitations of traditional self-monitoring blood glucose methods. CGM devices measure interstitial glucose concentrations constantly, enabling proactive therapeutic decisions and optimization of glycemic control through stored data analysis. CGM metrics such as time in range, time below range, and coefficient of variation are crucial for managing T1D, with emerging metrics like time in tight range and glycemia risk index showing potential for enhanced glycemic assessment. Recent advancements suggest the utility of CGM systems in monitoring the early stages of T1D and individuals with obesity complicated by pre-diabetes, highlighting its therapeutic versatility. This review discusses the current CGM systems for T1D during the pediatric age, established and emerging metrics, and future applications, emphasizing the critical role of CGM devices in improving glycemic control and clinical outcomes in children and adolescents with diabetes.

Acute Effect of Imeglimin Add-on Therapy on 24-h Glucose Profile and Glycemic Variability in Patients with Type 2 Diabetes Receiving Metformin.

INTRODUCTION: Imeglimin is a novel antidiabetic drug with insulinotropic and insulin-sensitizing effects that targets mitochondrial bioenergetics. We investigated acute effects of add-on therapy with imeglimin to preceding metformin on the 24-h glucose profile and glycemic variability assessed by continuous glucose monitoring (CGM) in patients with type 2 diabetes. METHODS: We studied 30 outpatients with type 2 diabetes inadequately controlled with metformin. CGM was used for 14 days straight during the research period. Imeglimin 2,000 mg/day was started on day 7 after initiating CGM. Several CGM parameters were compared between days 4-6 (prior to imeglimin treatment) and 11-13 (following the initiation of imeglimin treatment). RESULTS: After treatment with imeglimin, 24-h mean glucose was acutely decreased from 161.6 ± 48.0 mg/dL to 138.9 ± 32.2 mg/dL (p < 0.0001), while time in range (i.e., at a glucose level of 70-180 mg/dL) was significantly increased from 69.9 ± 23.9% to 80.6 ± 21.0% (p < 0.0001). Addition of imeglimin to metformin significantly decreased the standard deviation (SD) of 24-h glucose and mean amplitude of glycemic excursions, 2 indexes of glycemic variability. Baseline serum high-density lipoprotein (HDL) cholesterol was negatively correlated with changes in mean 24-h glucose (r = -0.3859, p = 0.0352) and those in SD (r = -0.4015, p = 0.0309). CONCLUSIONS: Imeglimin add-on therapy to metformin acutely lowered 24-h glucose levels and improved glycemic variability in patients with type 2 diabetes on metformin. A higher serum HDL cholesterol at baseline was associated with a better response to acute effects of imeglimin on 24-h glucose levels and glycemic variability.

A comparative study of the relationship between time in range assessed by self-monitoring of blood glucose and continuous glucose monitoring with microalbuminuria outcome, HOMA-IR and HOMA-ß test.

AIMS: To compare the time in range (TIR) obtained from self-monitoring of blood glucose (SMBG) with that obtained from continuous glucose monitoring (CGM), and explore the relationship of TIR with microalbuminuria outcome, HOMA-IR and HOMA-ß test. METHODS: We recruited 400 patients with type 2 diabetes to carry out blood glucose monitoring by both SMBG and CGM for 3 consecutive days. TIR, TAR, TBR and other blood glucose variation indices were calculated respectively through the glucose data achieved from SMBG and CGM. The HOMA-IR and HOMA-ß test was evaluated by an oral glucose tolerance test. Urinary microalbumin-to-creatinine ratio completed in the laboratory. RESULTS: The median (25 %, 75 % quartile) of TIRCGM and TIRSMBG were 74.94(44.90, 88.04) and 70.83(46.88, 87.50) respectively, and there was no significant difference, p = 0.489; For every 1 % increase in TIRCGM, the risk of microalbuminuria decreased by 1.6 % (95%CI:0.973, 0.995, p = 0.006) and for every 1 % increase in TIRSMBG, the risk of microalbuminuria decreased by 1.3 % (95%CI:0.975, 0.999, p = 0.033). Stepwise multiple linear regression analysis showed an independent positive correlation between TIR (including TIRCGM and TIRSBMG) and LnDI30 and LnDI120 levels (p = 0.000). CONCLUSIONS: The TIR calculated by SMBG was highly consistent with that reported by CGM and was significantly associated with the risk of microalbuminuria and the HOMA-ß. Higher TIR quartiles were associated with lower incidence of microalbuminuria as well as higher lever of HOMA-ß. For patients with limited CGM application, SMBG-derived TIR may be an alternative to CGM-derived TIR, to assess blood glucose control.

'We're taught green is good': Perspectives on time in range and time in tight range from youth with type 1 diabetes, and parents of youth with type 1 diabetes.

AIMS: Continuous glucose monitoring (CGM) systems are standard of care for youth with type 1 diabetes with the goal of spending >70% time in range (TIR; 70-180 mg/dL, 3.9-10 mmol/L). We aimed to understand paediatric CGM user experiences with TIR metrics considering recent discussion of shifting to time in tight range (TITR; >50% time between 70 and 140 mg/dL, 3.9 and 7.8 mmol/L). METHODS: Semi-structured interviews and focus groups with adolescents with type 1 diabetes and parents of youth with type 1 diabetes focused on experiences with TIR goals and reactions to TITR. Groups and interviews were audio-recorded, transcribed and analysed using content analysis. RESULTS: Thirty participants (N = 19 parents: age 43.6 ± 5.3 years, 79% female, 47% non-Hispanic White, 20 ± 5 months since child's diagnosis; N = 11 adolescents: age 15.3 ± 2 years, 55% female, 55% non-Hispanic White, 16 ± 3 months since diagnosis) attended. Participants had varying levels of understanding of TIR. Some developed personally preferred glucose ranges. Parents often aimed to surpass 70% TIR. Many described feelings of stress and disappointment when they did not meet a TIR goal. Concerns about TITR included increased stress and burden; risk of hypoglycaemia; and family conflict. Some participants said TITR would not change their daily lives; others said it would improve their diabetes management. Families requested care team support and a clear scientific rationale for TITR. CONCLUSIONS: The wealth of CGM data creates frequent opportunities for assessing diabetes management and carries implications for management burden. Input from people with type 1 diabetes and their families will be critical in considering a shift in glycaemic goals and targets.

Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review.

INTRODUCTION: Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain. METHODS: The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain. RESULTS: A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future. CONCLUSION: By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Continuous glucose monitoring (CGM) devices are used to monitor glucose levels in real time over 24 h. This has changed the way people with diabetes and their health care providers interact. These devices produce a large amount of data that can be analyzed and evaluated using standardized parameters called glucometrics, which include the time a patient's glucose is in range, below range, and above range, and how much it varies over 24 h. Clinicians can use these data to better assess glycemic control and a patient's individual response to treatment. In this article, we summarize evidence from published studies involving adults and children with type 1 diabetes in Spain to look at how the use of these data has affected clinical practice. Studies have shown that cases of severe low blood glucose in children with diabetes were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Data from clinical practice in Spain show that CGM is associated with improved blood glucose markers. Many studies analyzed these data during the COVID-19 pandemic and showed that CGM and analysis of glucometrics were highly useful during this time. New glucometrics and approaches to the analysis of data from CGM could help patients achieve better blood glucose control.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.

BACKGROUND: Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. METHODS: This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. RESULTS: Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. CONCLUSIONS: Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Seasonal fluctuations of CGM metrics in individuals with type 1 diabetes using an intermittently scanned CGM device or sensor augmented pump.

OBJECTIVE: To elucidate the fluctuations in glucose levels measured using CGM-metrics during the four distinct seasons of the year in individuals with type 1 diabetes mellitus (T1DM) using an intermittently scanned CGM (isCGM) device or sensor augmented pump (SAP). RESEARCH DESIGN AND METHODS: This retrospective, single-center study enrolled 93 individuals with T1DM who were equipped with an isCGM device or SAP at Kobe University Hospital. The subjects had a median age of 47.0 years [interquartile range, 37.0-62.0 years], 25 individuals (26.9%) were male, median body mass index was 22.0 kg/m2 [20.8-23.8 kg/m2], and median hemoglobin A1c level was 7.4% [6.9-8.0%]. CGM data were reviewed from January to December 2019, and the mean sensor glucose (SG) value, time above range (TAR), time in range (TIR), time below range (TBR), and standard deviation (SD) of SG were calculated for each season (spring, March-May; summer, June-August; autumn, September-November; winter, December-February). RESULTS: Seasonal fluctuations were detected for mean SG, TAR, TIR, and SD, with TIR being lower and mean SG, TAR, and SD being higher in cold seasons (spring or winter) than in warm seasons (summer or autumn). CONCLUSION: Seasonal fluctuations in CGM metrics should be taken into account in future studies performed to evaluate the favorable impact of CGM on glycemic management in individuals with T1DM.