Clinical and biochemical predictors of large for gestational age during pregnancy in women with type 1 diabetes mellitus - current insight.

Introduction: Glycemic control has a major effect on fetal growth in women with type 1 diabetes mellitus (DM1), worse control leading to increased growth (large for gestational age (LGA) or very large for gestational age (VLGA) fetus). Currently, we aim at strict control (at least HbA1c ≤ 48 mmol/mol) before and during pregnancy. Real Time Continuous Glucose Monitoring (RTCGM) offers new ways to achieve this. We performed a retrospective chart review of pregnancy outcomes in DM1 to assess outcome and CGM-data in well-controlled women with DM1. Methods: Forty-eight pregnancies in 25 women were identified between 2016-2019; 19 women with a singleton pregnancy met the inclusion criterion of HbA1c ≤ 48 mmol/mol in 1st and 3rd trimester; 8 women had insufficient CGM-data.; CGM could be analysed in 11 women. LGA was defined as fetal weight > 90th and VLGA > 97.7th percentile. CGM-data were studied during 4 periods: 4-8 weeks gestation, 18-22 weeks, 30-34 weeks and last 8 days before delivery. Target range 3.5-7.8 mmol/L. Time in range (TIR), time below range (TBR) and time above range (TAR) were calculated. Results: In the 19 women with adequate control, LGA (LGA and VLGA) in 36.8%; simple LGA 15.7%, VLGA 21.1%. There were no congenital malformations. Women with VLGA child were significantly older (p<0.03), had higher BMI (p<0.02) and longer duration of DM1 (p<0.02). In women with normal growth fetus, LGA or VLGA, Time In Range (TIR) was 64.1%, 63.6% and 58.5%; Time Above Range (TAR): 24.4, 34.4 and 39.6%; Time Below Range: 4.0, 2.2 and 2.1%. Conclusion: Our data show that current implementation of advanced technology was associated with mean TIR below recommended level of >70% and TAR above recommended level (<25%). Prevalence of LGA and VLGA was still increased. Adaptation of care and counselling re needed for improvement in outcome with technology. Disclosure H.W. de Valk: None. L. Oosterwijk: None. K. Kaasjager: None.

B. Zaidan: None. R. Malek: None. Background: Successful management of type 1 diabetes mellitus (T1DM) in pregnancy poses challenges to women and their providers due to tight glycemic goals (63-140mg/dL) requiring intensive glucose monitoring and insulin adjustment. Studies have shown that only 10% of pregnant women achieve more than 70% time in range (1). A multicenter randomized clinical trial used hybrid closed loop therapy in women with preexisting T1DM in pregnancy significantly improved maternal glycemic control during pregnancy (2). A case series of 4 pregnant women with T1DM using Tslim with Control-IQ showed time in range glucose 73.4% to 78.7%,78% to 83.6%, and 46.5% to 71.9% between the first and third trimesters, respectively (3). Case description: A 30-year-old patient with T1DM previously using Tslim with Control-IQ identified pregnancy at 6 weeks gestation. The patient was aware that the use of Control-IQ technology is not FDA approved for use in pregnancy and chose to remain on it. The patient’s blood sugars were evaluated every 2-4 weeks depending on stability with frequent setting adjustments. Data evaluated include time in range (TIR), time below range (TBR), and time above range (TAR) and neonatal outcomes. Result: The patient had a full-term pregnancy, delivering at 39 weeks. The pregnancy TIR (65-140mg/dl) in the 1st trimester was 68% with average blood glucose 125mg/dl; TIR in the 2nd trimester was 71% with average blood glucose of 129mg/dl; TIR in the 3rd trimester was 73% with average blood glucose of 119mg/dl. The TAR in the 1st trimester was less than 27%; in the 2nd trimester and 3rd trimester were less than 25%. The TBR in the 1st trimester was less than 3%; in the 2nd trimester was less than 4%; in the 3rd trimester was less than 3%. No episodes of diabetic ketoacidosis, severe hypoglycemia, emergency department visits or hospitalization occurred. The patient delivered a baby girl by C-section with no neonatal complications. The patient did develop post-partum hypertension that resolved. Conclusion: Pregnant women with T1DM need optimal blood glucose control for a healthy maternal and fetal outcome. Consistent with previously described case series (3), the off-label use of Tslim with Control-IQ technology in pregnancy reduced diabetes management burden and improved glycemic control. Reference: 1. Murphy HR. Continuous glucose monitoring targets in type 1 diabetes pregnancy: every 5% time in range matters. Vol. 62, Diabetologia. 2019 2.Lee TTM, Collett C, Bergford S, Hartnell S, Scott EM, Lindsay RS, et al. Automated Insulin Delivery in Women with Pregnancy Complicated by Type 1 Diabetes. N Engl J Med. 2023 3.Wang XS, Dunlop AD, McKeen JA, Feig DS, Donovan LE. Real-world use of Control-IQTM technology automated insulin delivery in pregnancy: A case series with qualitative interviews. Diabetic Medicine. 2023 Jun 1;40(6) Sunday, June 2, 2024

Objective: To investigate the appropriate cut-off point of time in range (TIR) for evaluating glucose control in type 2 diabetes mellitus (T2DM) patients, and analyze the prevalence of abnormal carotid intima-media thickness (CIMT) and diabetic retinopathy (DR) in different TIR categories. Methods: A total of 2 161 subjects with T2DM (1 183 males) were enrolled from hospitalized patients at the Department of Endocrinology and Metabolism of the Sixth People's Hospital Affiliated to Shanghai Jiao Tong University from January 2005 to February 2012. The age of the enrolled participants was (60.4±11.9) years. Each patient underwent continuous glucose monitoring (CGM) for three consecutive days, then TIR (3.9-10.0 mmol/L), time above range (TAR) and time below range (TBR) were calculated. Fundus photography and carotid artery Doppler ultrasound were performed to diagnose DR and abnormal CIMT (defined as CIMT≥1.0 mm), respectively. Multivariate logistic regression models were used to examine the independent association of different TIR groups with CIMT and DR. Results: All subjects were divided into 4 groups according to TIR:≤40%, 41%-70%, 71%-85% and>85%. Significant linear trends in age, diabetes duration, body mass index (BMI), total cholesterol, glycated hemoglobin A1c (HbA1c), TAR and mean glucose (MG) existed among the 4 groups (all P(trend)<0.05). However, there was only a weak correlation between TIR and TBR (<3.9 mmol/L) (r=0.087, P<0.001), and no significant association was observed between TBR (<3 mmol/L) and the TIR categories (P(trend)=0.378). The overall prevalence of abnormal CIMT and DR was 12.1% and 23.8%, respectively. The prevalence of abnormal CIMT in the 4 groups with ascending levels of TIR was 16.9% (59/349), 12.9% (96/746), 11.2% (57/510) and 9.0% (50/556) (P(trend)<0.001), respectively. And the prevalence of DR was 30.7% (107/349), 29.4% (219/746), 20.8% (106/510) and 14.9% (83/556), respectively (P(trend)<0.001). In the binary logistic regression model by adjusting confounding factors, compared with TIR≤ 40%, the risk of abnormal CIMT was reduced by 33.8% (OR=0.662, 95%CI: 0.456-0.963, P=0.031), 40.8% (OR=0.592, 95%CI: 0.390-0.899, P=0.014), and 45.0% (OR=0.550, 95%CI: 0.358-0.846, P=0.006) in the other three groups, respectively. And the risk of DR was reduced by 2.9% (OR=0.971, 95%CI: 0.725-1.301, P=0.844), 33.4%(OR=0.666, 95%CI: 0.479-0.924, P=0.015) and 53.3% (OR=0.467, 95%CI: 0.331-0.657, P<0.001), respectively. Conclusion: Using 40%, 70% and 85% as cut-off point of TIR helps stratify the risk of diabetic complications, and assess the glucose control (Poor: TIR≤40%; Unsatisfactory: TIR≤70%; Satisfactory: TIR>70%; Optimal: TIR>85%) in patients with T2DM.

Women with preexisting diabetes mellitus (PDM) are at increased risk of pregnancy-related complications. To summarize the available supporting evidence for the Endocrine Society guidelines about management of PDM in pregnancy. MEDLINE, EMBASE, Scopus, and other sources through February 2025. Studies were selected by pairs of independent reviewers. Data were extracted by pairs of independent reviewers. We included 17 studies. Meta-analysis showed no significant difference between hybrid closed-loop insulin pump (HCL) and standard of care regarding time in range (TIR), time above range (TAR), and time below range (TBR). HCL had better overnight TIR and TBR. For women with type 2 diabetes mellitus (T2DM), intermittent use of continuous glucose monitoring (CGM) was not associated with a significant change in the risk of large for gestational age (LGA) neonates (2 randomized controlled trials [RCTs], 102 patients). Adding metformin to insulin was associated with a lower risk of LGA (2 RCTs, 1126 patients). Three retrospective studies (1724 patients) suggested increased neonatal complications when delivery was induced before 39 weeks of gestation (particularly before 38 weeks) in women with preexisting type 1 (T1DM) and T2DM, although this evidence was subject to likely confounding. One retrospective study showed no increase in neonatal complications with periconceptional exposure to glucagon-like peptide-1 receptor agonists. We could not identify comparative studies assessing a screening question about the possibility of pregnancy or a carbohydrate restrictive diet. This systematic review addresses various aspects of managing PDM in pregnancy and will support the development of the Endocrine Society guidelines.

BackgroundContinuous glucose monitoring (CGM)-derived time in range (TIR) is closely associated with micro- and macrovascular complications in type 2 diabetes mellitus (T2DM). This study was performed to investigate the relationship between key CGM-derived metrics and specific cognitive domains in patients with T2DM.MethodsOutpatients with T2DM who were otherwise healthy were recruited for this study. A battery of neuropsychological tests was performed to evaluate cognitive function, including memory, executive functioning, visuospatial ability, attention, and language. Participants wore a blinded flash continuous glucose monitoring (FGM) system for 72 h. The key FGM-derived metrics were calculated, including TIR, time below range (TBR), time above range (TAR), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE). Furthermore, the glycemia risk index (GRI) was also calculated by the GRI formula. Binary logistic regression was used to assess risk factors for TBR, and we further analysed the associations between neuropsychological test results and key FGM-derived metrics with multiple linear regressions.ResultsA total of 96 outpatients with T2DM were recruited for this study, with 45.8% experiencing hypoglycemia (TBR< 3.9 mmol/L). Spearman analysis results revealed that a higher TBR< 3.9 mmol/L was correlated with worse performance on the Trail Making Test A (TMTA), Clock Drawing Test (CDT), and cued recall scores (P < 0.05). Logistic regression analysis results indicated that the TMTA (OR = 1.010, P = 0.036) and CDT (OR = 0.429, P = 0.016) scores were significant factors influencing the occurrence of TBR< 3.9 mmol/L. Multiple linear regressions further demonstrated that TBR< 3.9 mmol/L (β = -0.214, P = 0.033), TAR> 13.9 mmol/L (β = -0.216, P = 0.030) and TAR10.1–13.9 mmol/L (β = 0.206, P = 0.042) were significantly correlated with cued recall scores after adjusting for confounding factors. However, TIR, GRI, CV and MAGE showed no significant correlation with the results of neuropsychological tests (P > 0.05).ConclusionsA higher TBR< 3.9 mmol/L and TAR> 13.9 mmol/L were associated with worse cognitive functions (memory, visuospatial ability, and executive functioning). Conversely, a higher TAR of 10.1–13.9 mmol/L was associated with better memory performance in memory tasks.

Growing evidence shows that sarcopenia is more prevalent in patients with type 2 diabetes mellitus (T2DM) than in the normal population. However, currently, data on the relationship between blood glucose fluctuation and sarcopenia in elderly patients with T2DM are still limited. In this study, 280 patients ≥ 60 years with T2DM were divided into sarcopenic group and non-sarcopenic group, according to the diagnostic criteria of the 2019 Asian Working Group for Sarcopenia. They wore MeiQi to acquire the indexes including time in range (TIR), time above range (TAR), time below range (TBR), mean amplitude of glycemic excursion (MAGE), coefficient of Variation (CV), blood glucose standard deviation (SD), largest amplitude of glycemic excursions (LAGE) and mean glucose (MG). The prevalence rate of sarcopenia was statistically analyzed and the different indicators of glucose fluctuation between the two groups were compared. We analyzed the indexes of glucose fluctuation and appendicular skeletal muscle mass index (ASMI), handgrip strength, the time of five times sit to stand test (FTSST) with Spearman's correlation analysis. Logistic regression was used to analyze the influence factors for sarcopenia. The prevalence of sarcopenia was 15.36%. TIR, MG and TAR were correlated with ASMI, handgrip strength, the time of FTSST. MG and TAR were risk factors for sarcopenia, while TIR was the protective factor of sarcopenia. After adjusting mixing factors, logistic regression analysis showed that TIR was an independent protective factor. The result of the Chi-square test showed that the incidence of sarcopenia in different TIR ranges was different: the proportion of patients with sarcopenia was 40.48% (TIR ≤50%), 20.41% (50%<TIR≤70%) and 8.47% (TIR >70%). TIR is associated with sarcopenia in elderly T2DM patients. Furtherly, the incidence rate of sarcopenia decreases with the increase of TIR.

AimsThe comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.MethodsThis was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.ResultsThe results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.ConclusionsT1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.

IntroductionThis study aimed to evaluate the effectiveness and safety of switching from basal bolus insulin treatment (BBIT) to a fixed combination of insulin degludec and liraglutide (IDegLira) in patients with type 2 diabetes mellitus (T2DM) who had preserved insulin secretion but inadequate glucose control. The study also aimed to assess the feasibility of implementing this therapeutic approach in common clinical practice settings.MethodsThis was a non-randomized, open-label, multicenter, prospective, single-arm study involving 234 patients with T2DM who were receiving BBIT. Inclusion criteria were duration of diabetes mellitus > 60 months, stable total daily dose of insulin (TDDI) ranging from > 20 to < 70 IU/day (approx. > 0.3 to < 0.7 IU/kg body weight/day), C-peptide levels > 10% above the lower limit, HbA1c levels > 7% and < 10% (Diabetes Control and Complications Trial), and body mass index > 25 kg/m2. The primary endpoints were changes in glycated hemoglobin (HbA1c) and body weight at week 28 after treatment switching. Secondary endpoints included changes in the 7-point glycemic profile, hypoglycemia frequency, blood pressure, blood lipids, liver enzymes, insulin dose, and a patient questionnaire focusing on treatment satisfaction, concerns and impact on daily activities. A subgroup of 55 patients underwent continuous glucose monitoring (CGM) with the evaluation of CGM-derived parameters, such as time in range (TIR), time above range (TAR), time below range (TBR), hypoglycemia, and glucose variability.ResultsA significant decrease in HbA1c (8.6% vs. 7.6%; p < 0.0001) and body weight (97.8 vs. 94.0 kg; p < 0.0001) was observed at week 28 after treatment switching. Significant improvements were also seen in all measurements of the 7-point glycemic profile (p < 0.0001), reduction in the number of hypoglycemia episodes per patient, and the proportion of patients with at least one hypoglycemia event (p < 0.001). Furthermore, there was a significant decrease in daily insulin dose (55.6 vs. 32.7 IU/day; p < 0.0001), as well as improvements in blood pressure, blood lipids, and liver enzymes (gamma glutamyl transferase and alanine aminotransferase). The subgroup of patients who underwent CGM showed a significant increase in TIR (57.9% vs. 69.0%; p < 0.01) and a decrease in TAR (40.1% vs. 28.8%; p < 0.01), while TBR, hypoglycemia (number of episodes per patient and proportion of patients), and glucose variability did not change significantly.ConclusionThe results of this study suggest that switching from BBIT to IDegLira in patients with T2DM and preserved insulin secretion can simplify treatment without compromising glycemic control. The switch to IDegLira was associated with significant improvements in various glucose control parameters, including HbA1c, glycemic profile, hypoglycemia, insulin doses, and CGM-derived parameters TIR and TAR. Additionally, it led to significant reductions in body weight, blood pressure, lipid profile, and liver enzyme levels. Switching to IDegLira may be considered a safe and beneficial approach in clinical practice settings, offering metabolic and individual advantages.

Background Quality of Life (QoL) is an essential consideration in healthcare. Numerous studies have examined QoL in India; however, data on QoL following different dietary interventions are lacking. Similarly, the use of technology such as continuous glucose monitoring (CGM) for diabetes care has independently demonstrated improvements in glycemic control; however, its association with QoL remains limited. Purpose The purpose was to study the role of different dietary interventions on QoL and its association with Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR) among the Type 2 Diabetes Mellitus (T2DM) population. Methodology A crossover interventional clinical trial (CTRI/2022/07/044356) was conducted among participants with T2DM of less than 5 years' duration, aged between 25 and 55 years, with an HbA1c level of less than 8%, and who were on Metformin only. Their QoL was assessed after following two diet patterns: the Continuous Calorie Restricted Diet (CCRD) - calorie reduction with small frequent meals, and Time Restricted Intermittent Fasting (TRIF) - calorie reduction with only two meals a day, using the Modified QoL (MDQOL-17) questionnaire. The association between post-dietary interventions QoL and TIR was studied using a 14-day CGM device. Results The overall QoL of 51 participants at the end of the dietary interventions was significantly better compared to their QoL before any dietary intervention (85.6±19.0% and 63.1±13.0%, respectively, p = 0.000). Decreased TIR correlated with increased role limitations due to physical functioning (p = 0.002) and decreased energy levels (p = 0.00). As TBR increased, role limitation due to emotional well-being increased, and energy levels decreased significantly (p = 0.01). As TAR increased, energy levels decreased (p = 0.01). A simple linear regression model was statistically significant for role limitations due to physical functioning (p = 0.003) and energy fatigue (p = 0.000), suggesting that higher TIR is associated with higher scores in these domains. Conclusion Dietary interventions that improve the TIR and reduce the TAR and TBR can enhance the QoL of individuals with T2DM.

Introduction and Objective: Type 1 diabetes mellitus (T1DM) is associated with increased risks of adverse pregnancy outcomes. While Continuous Glucose Monitoring Systems (CGMS) improve glycemic monitoring, self-monitoring of blood glucose (SMBG) remains the primary method for most pregnant women due to accessibility and cost barriers. This study aimed to evaluate the association between time-in-range (TIR) extrapolated from SMBG and adverse perinatal outcomes in T1DM pregnancies. Methods: A retrospective cohort study included singleton pregnancies with live births and no malformations, starting prenatal care before 20 weeks (2010-2019). Glycemic data were categorized into TIR (63-140 mg/dL), time below range (TBR), and time above range (TAR) and stratified as TIR &amp;lt;50%, 50-70%, and &amp;gt;70%. Logistic regression analyzed independent predictors of adverse outcomes, including prematurity, LGA, and neonatal respiratory distress. Results: Among 140 participants that provided 142,997 capillary blood measurements, 20% had TIR &amp;lt;50%, 53.6% had TIR 50-70%, and 26.4% had TIR &amp;gt;70%. Higher TIR was inversely associated with adverse outcomes. Compared to TIR &amp;lt;50%, TIR 50-70% and TIR&amp;gt;70% were associated with lower risks of prematurity (OR 0.271, 95%CI 0.094-0.786; OR 0.219, 95%CI 0.058-0.826), neonatal respiratory distress (OR 0.341, 95%CI 0.124-0.936; OR 0.122, 95%CI 0.029-0.516), and LGA (OR 0.246, 95%CI 0.084-0.719; OR 0.115, 95%CI 0.028-0.469). Conclusion: Achieving glucose targets during pregnancy is particularly challenging for women with T1DM. Nonetheless, this study demonstrates that even modest achievements such as &amp;gt;50% TIR, extrapolated from SMBG, significantly reduces the risks of prematurity, LGA, and neonatal respiratory distress. These findings highlight the importance of supporting pregnant women with T1DM in achieving individualized glycemic goals to optimize maternal and neonatal outcomes. Disclosure E.A.M. Santos: None. T. Assuncao Zaccara: None. F.C.F. Mikami: None. M.D. Bernardi: None. C. de Freitas Paganoti: None. R.P.V. Francisco: None. R.A. Costa: None.

Patients with classical type 1 diabetes mellitus (T1DM) require lifelong dependence on exogenous insulin therapy due to pancreatic beta-cell destruction and absolute insulin deficiency. T1DM accounts for about 90% of children with diabetes in China, with a rapid increase in incidence and a younger-age trend. Epidemiological studies have shown that the overall glycated haemoglobin (HbA1c) and compliance rate are low in Chinese children with T1DM. Optimal glucose control is the key for diabetes treatment, and maintaining blood glucose within the target range can prevent or delay chronic vascular complications in patients with T1DM. Therefore, this study aims to investigate the glycemic control of children with T1DM from Hunan and Henan Province with flash glucose monitoring system (FGMS), and to explore factors associated with glycemic variability. A total of 215 children with T1DM under 14 years old were enrolled continuously in 16 hospitals from August 2017 to August 2020. All subjects wore a FGMS device to collect glucose data. Correlation of HbA1c, duration of diabetes, or glucose scan rates with glycemic variability was analyzed. Glucose variability was compared according to the duration of diabetes, HbA1c, glucose scan rates and insulin schema. HbA1c and duration of diabetes were positively correlated with mean blood glucose, standard deviation of glucose, mean amplitude of glucose excursions (MAGE), and coefficient of variation (CV) of glucose (all P<0.01). The glucose scan rates during FGMS wearing was significantly positively correlated with time in range (TIR) (P=0.001) and negatively correlated with MAGE and mean duration of hypoglycemia (all P<0.01). Children with duration ≤1 year had lower time below range (TBR) and MAGE when compared with those with duration >1 year (all P<0.05). TIR and TBR in patients with HbA1c ≤7.5% were higher (TIR: 65% vs 45%, TBR: 5% vs 4%, P<0.05), MAGE was lower (7.0 mmol/L vs 9.4 mmol/L, P<0.001) than those in HbA1c >7.5% group. Compared to the multiple daily insulin injections group, TIR was higher (60% vs 52%, P=0.006), MAGE was lower (P=0.006) in the continuous subcutaneous insulin infusion group. HbA1c was lower in the high scan rates (≥14 times/d) group (7.4% vs 8.0%, P=0.046), TIR was significantly higher (58% vs 47%, P<0.001), and MAGE was lower (P<0.001) than those in the low scan rate (<14 times/d) group. The overall glycemic control of T1DM patients under 14 years old in Hunan and Henan Province is under a high risk of hypoglycemia and great glycemic variability. Shorter duration of diabetes, targeted HbA1c, higher glucose scan rates, and CSII are associated with less glycemic variability.

The world has changed tremendously for patients suffering from diabetes mellitus with the development of cutting-edge technologies like continuous glucose monitoring and flash glucose monitoring systems. Now, the details of constant fluctuations of glucose in their blood can be monitored not only by medical professionals but also by patients, and this is called glycemic variability (GV). Traditional metrics of glycemic control measurement, such as glycated hemoglobin (HbA1c), fail to reflect various short-term glycemic changes like postprandial hyperglycemia and hypoglycemic episodes, paving the way to the occurrence of various diabetic complications even in asymptomatic, well-controlled diabetic patients. This need for advanced management of diabetes and effective monitoring of these swings in blood glucose can be met by using a continuous glucose monitoring system (CGMS). To evaluate the extent of GV in well-controlled type 2 diabetes mellitus (T2DM) patients using a flash CGMS and to assess the correlation between GV and HbA1c. A hospital-based prospective observational study was carried out from May 2020 to Oct 2021 at the Department of Medicine, SMS Hospital, Jaipur, Rajasthan (India), after approval from the Ethics Committee of the institution. A total of 30 patients with well-controlled T2DM (HbA1c was ≥6.5, but ≤7.5) were included in the study using simple random techniques after written informed consent from patients. Patients were studied for glycemic excursions over a period of 7 days by using FreeStyle® Libre Pro™, which is a flash glucose monitoring system. The CGM sensor was attached to the left upper arm of the patient on day 0 and removed on day 7. The data recorded in the sensor was then retrieved using pre-installed computer software and analyzed using standard CGM metrics like standard deviation (SD), percentage coefficient of variation (%CV), time above range (TAR), time below range (TBR), and time in range (TIR), out of which %CV was used to quantify GV. %CV has been used to cluster patients into four cohorts from best to worst, namely: best/low CV ≤ 10%, intermediate CV from 10 to 20%, high CV from 20 to 30%, and very high CV of >30%. Scatterplots are used to establish correlations between various parameters. Data from a total of 30 patients were analyzed using CGMS and thus used for calculating standard CGM metrics; glucose readings every 15 minutes were recorded consecutively for 7-day periods, making it a total of 672 readings for each patient. Interpreting the CGM data of all 30 patients, the following results were found: the mean blood glucose of all cases is 134.925 ± 22.323 mg/dL, the mean SD of blood glucose of all cases is 35.348 ± 9.388 mg/dL, the mean of %CV of all cases is 26.376 ± 6.193%. CGM parameters of time are used in the form of percentages, and the following results were found: the mean of TAR, TBR, and TIR is 14.425 ± 13.211, 5.771 ± 6.808, and 82.594 ± 12.888%, respectively. Clustering the patients into cohorts, the proportion of patients exhibiting best/low %CV (10%) is 0, intermediate %CV (10-20%) is 16.67% (five out of 30 patients), high %CV (20-30%) is 50% (15 out of 30 patients) and very high %CV (>30%) is 33.33% (10 out of 30 patients). Also, there is no significant correlation found between HbA1c and %CV (ρ = 0.076, p-value = 0.690); a significant negative correlation was found between %CV and TIR (ρ = -0.604, p < 0.001S); a positive correlation of %CV with TAR and TBR is significant (ρ = 0.816, p-value of <0.001). Using a flash CGMS device and considering %CV as the parameter and primary measure of GV, the study demonstrated the overall instability of a person's glycemic control, making note of unrecognized events of hypoglycemia and hyperglycemia in asymptomatic well-controlled T2DM patients, revealing the overall volatile glycemic control. The most important finding of this study is that even those diabetics who are considered well-controlled experience a great degree of GV as assessed by CGM-derived metrics. This study also demonstrated that there is no significant correlation between HbA1c and GV, suggesting that patients may not have optimal control of their diabetes despite having "normal HbA1c" values; hence, GV can be considered an HbA1c-independent danger factor, having more harmful effects than sustained hyperglycemia in the growth of diabetic complications. So, by using CGM-derived metrics, the measurement of GV has the potential to complement HbA1c data. In this manner, a more comprehensive assessment of glycemic excursions can be provided for better treatment decisions, thereby facilitating optimal glycemic control, which is essential for reducing overall complications and promoting good quality of life.

BackgroundFew studies have reported the adherence to and efficacy of continuous glucose monitoring (CGM) for improving diabetes management in insulin-treated older adults with type 2 diabetes mellitus (T2DM).MethodsProspective observational cohort study using FreeStyle Libre Flash CGM in insulin-treated adults > 65 years with T2DM and HbA1c between 7% and 9%. The participants wore the CGM during the 6-weeks study period. The primary outcome was time in range (TIR) between 70 and 180 mg/dL. Secondary outcomes included time below range (TBR), glycemic variability (GV), adherence, and use of glucose data for self-insulin adjustment. Linear regressions with random effects verified the changes in TBR, TIR, time above range (TAR), GV, and GMI across the three visits using CGM (baseline, 4 weeks and 6 weeks), controlled for sex, age, educational level, and health system (private or public).ResultsA total of 66 participants completed the six weeks of CGM (age 72·8 ± 5·3 years; BMI 27·8 ± 3·6 kg/m2), HbA1c: 8·0 ± 0·6%, with an overall sensor utilization of 93·1 ± 6·0%. We observed a stability in TIR (baseline: 63.5 ± 18.9% vs. endpoint: 65.5 ± 18.8%; β = 1,0, p = 0.190). Despite the low TBR at the baseline, we observed statistically significant reduction over the study period (baseline: 5.8 ± 7.0% vs. endpoint: 3.8 ± 4.7%; (β=-1.00, p = 0.008). Glucose variability also reduced from the baseline (34.9 ± 7.2%) to the endpoint (33.0 ± 6.8%) (β=-0.99, p = < 0.001).ConclusionFreeStyle Libre Flash CGM is well accepted by older adults with T2DM and allows participants to make therapeutic decisions to reduce TBR and glycemic variability.

ObjectiveTo 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 methodsThis 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).ResultsSeasonal 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).ConclusionSeasonal 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.

COVID-19 vaccination is recommended in diabetic patients since diabetes is associated with worse clinical outcomes in COVID-19 infection. The safety profile of different types of COVID-19 vaccines, especially on glycemic control, can be explored due to availability of data from continuous glucose monitoring (CGM) devices. This meta-analysis aimed to quantify the impact of COVID-19 vaccination on glycemic control in patients with type 1 diabetes mellitus (T1DM). A systematic search of PubMed, Embase, and Google Scholar was conducted using a search strategy for studies published till January 2023 in English language. Comparative observational studies reporting glycemic control obtained from CGM before and after COVID-19 vaccination in T1DM patients were included. The primary outcome was time in range (TIR) metric of proportion of glucose results falling within the range: 3.9-10mmol/l. Other outcomes were time above range (TAR) (>10mmol/l), time below range (TBR) (<3.9mmol/l), coefficient of variation (CV), and mean blood glucose levels. The pooled outcomes were compared pre-vaccination and post-vaccination using Hedges' g (HG) with 95% CI. A total of seven studies (632 participants) were included in the meta-analysis. COVID-19 vaccination caused small and statistically insignificant decrease in TIR after both the first (HG = 0.21, 95% CI: -0.02 to 0.44, P=0.07) and second dose (HG = 0.09, 95% CI: -0.04 to 0.21, P = 0.19). Likewise, TAR was not affected after neither first (HG = -0.09, 95% CI: -0.22 to 0.03, P = 0.12) nor second vaccine dose (HG = -0.07, 95% CI: -0.21 to 0.06, P = 0.30). Likewise, TBR, mean blood glucose levels, and CV were not significantly altered following uptake of either of the doses. COVID-19 vaccination has an excellent safety profile in T1DM patients owing to its minimal impacts on immediate glycemic control.