Randomized comparison of self-monitored blood glucose (BGM) versus continuous glucose monitoring (CGM) data to optimize glucose control in type 2 diabetes

AimsEvaluate whether structured BGM testing (BGM) or real-time CGM (CGM) lead to improved glucose control (A1c). Determine which approach optimized glucose control more effectively.Methods—multi-arm paralleltrial of three type 2 diabetes (T2D) therapies ± metformin: (1) sulfonylurea (SU), (2) incretin (DPP4 inhibitor or GLP-1 agonist), or (3) insulin. After a baseline CGM, 114 adult subjects were randomized to either BGM (4 times daily) or CGM (24/7) for 16 weeks with therapies adjusted every 4 weeks.ResultsA1c means decreased from 8.19 to 7.07 (1.12% difference) with CGM (n = 59) and 7.85 to 7.03 (0.82% difference) with BGM (n = 55) (p < 0.001). BGM and CGM groups showed significant improvements in time in range and glucose variability—with no significant difference between the two groups. Clinically important hypoglycemia (<50 mg/dL) was significantly reduced for the CGM group compared with BGM (p < 0.01), particularly in subjects taking insulin or therapies with higher hypoglycemic risk (SU).ConclusionIn T2D, structured, consistent use of glucose data regardless of device (structured BGM or CGM) leads to improvements in A1c control. CGM is more effective than BGM in minimizing hypoglycemia particularly for those using higher hypoglycemic risk therapies.     

High Intensity Interval Training reduces hypoglycemic events compared with continuous aerobic training in individuals with type 1 diabetes: HIIT and hypoglycemia in type 1 diabetes

Aimsto investigate if a High Intensity Interval Training (HIIT) protocol improves glycemic control and fitness capacity, compared to traditional moderate Intensity Continuous Training (MICT) exercise.Methods30 sedentary individuals with type 1 diabetes (T1D) and 26 healthy controls were assigned to a 3-week HIIT or MICT protocol. Blood glucose levels by continuous glucose monitoring system and fitness status were compared before and after the study period.ResultsDuring workouts, blood glucose levels remained stable in HIIT exercise (+3.2 ± 16.2 mg/dl (p = 0.43)), while decreased in MICT (-27.1 ± 17.5 mg/dl (p < 0.0001)) exercise. In addition, out of the 9 training sessions, HIIT volunteers needed to take carbohydrate supplements to avoid hypoglycemia in 0.56 ± 0.9 sessions, compared to 1.83 ± 0.5 sessions (p < 0.04) in MICT individuals. In the analysis of blood glucose levels between rest and training days (24h-period), training significantly reduced mean glycemic levels in both groups, but the MICT exercise results in an increase in the frequency of hypoglycemic episodes. The response to exercise seems to be attenuated in individuals with T1D, especially in HIIT group.ConclusionHIIT training results in a greater glycemic stability, with reduction of hypoglycemic episodes.     

Insulin resistance-associated genetic variants in type 1 diabetes

AimsTo examine candidate insulin resistance single nucleotide polymorphisms (SNPs) for associations with glycemic control, insulin resistance, BMI, and complications in an observational type 1 diabetes (T1D) cohort: the Pittsburgh Epidemiology of Diabetes Complications (EDC) study.MethodsIn 422 European-ancestry participants, we assessed associations using additive models between 15 candidate SNPs and 25-year mortality, cardiovascular disease, microalbuminuria, overt nephropathy and proliferative retinopathy, and 25-year mean HbA1c, estimated glucose disposal rate (eGDR, inverse measure of insulin resistance), and BMI.ResultsThe A allele of rs12970134 was associated with higher mean HbA1c (β = +0.34 ± 0.09, p = 0.00009) and nominally associated with worse eGDR (p = 0.02). Further analyses suggest the HbA1c association may be modified by diabetes therapy regimen: rs12970134 AA genotype was associated with higher HbA1c under non-intensive therapy conditions (<3 insulin injections/day or monitoring blood glucose<3 times/day [p = 0.004]), but not under intensive therapy (≥3 injections/day or insulin pump and monitoring glucose≥3 times/day [p = 0.71]). There were no significant associations between any SNPs and BMI or complications.Conclusionsrs12970134, near MC4R, is strongly associated with HbA1c in this cohort. Further exploration of this genomic region is warranted, as it may hold promise for discovering new therapeutic targets to improve glycemic control in T1D.     

Applying Parkes Grid Method to Evaluate Impact of Variation in Blood Glucose Monitoring (BGM) Strip Accuracy Performance in Type 1 Diabetes Highlights the Potential for Amplification of Imprecision With Less Accurate BGM Strips

Background:The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being used to increase test frequency with less focus on accuracy.Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact.Method:BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).The Parkes error grid model with its associated category of risk including “alter clinical decision” and “affect clinical outcomes” was used, with the profile of frequency of expected results fitted into each BGM accuracy category.Results:Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the “alter clinical action” category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%.Conclusion:There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.     

Glucose-independent racial disparity in HbA1c is evident at onset of type 1 diabetes

ObjectiveHigher levels of HbA1c, independent of blood glucose levels, have been described in Blacks compared to Whites patients with established diabetes. The goal of this study was to determine if glucose-independent racial disparity in HbA1C is evident at diabetes onset.Research design and methodsWe conducted a retrospective single-center chart review of 189 youth with new onset Type 1 diabetes (T1D) 60 % Whites and 40 % Blacks. HbA1c, glucose and other biochemistry measures were obtained at presentation in the Emergency Department before initiation of any therapy. HbA1c levels were adjusted for presenting glucose, self-identified race, age, gender, hematocrit, and RDW-CV.ResultsBlacks with T1D had statistically significant higher unadjusted HbA1c (11.9 ± 1.9 vs 11.04 ± 2.0 %, p = 0.004), initial glucose (530.6 ± 230.4 vs 442 ± 211.3 mg/dL, p = 0.0075) and lower pHs (7.28 ± 0.15 vs 7.33 ± 0.12, p = 0.02) compared to white patients. Least squares means of HbA1c remained higher in Black patients even after statistical adjustment for presenting glucose, age, gender, RDW-CV, and pH. In a multiple variable model (R² = 0.38, p < 0.0001) c-peptide was influenced by HCO₃ (p = 0.0035), gender (p = 0.0092), BMI (p < 0.0001), but not race or glucose.ConclusionsHbA1c at initial presentation of T1D is higher in young Black patients compared to Whites even after adjustment for glucose, age, gender, and RDW-CV. This racial disparity is consistent with other studies in individuals without diabetes and patients with long-standing diabetes under treatment.     

Impact of the FreeStyle Libre flash glucose monitoring system on diabetes- self-management practices and glycemic control among patients with type 2 diabetes in Saudi Arabia: A prospective study

Background and aimsTo examine the efficacy of FreeStyle Libre Flash Glucose Monitoring System (FGMS) on Diabetes Self-Management Practices (DSMP) and glycemic control among patients with type 2 diabetes (T2D).MethodsThis prospective study was conducted among 105 patients with T2D (aged 30–70 years), who self-tested their glucose levels by conventional finger-prick method using blood glucose meters (BGM). At baseline visit, FGMS sensors were fixed by a diabetes educator to all patients. At the baseline and at 12 weeks of the study, an interviewer collected the responses of Diabetes Self-Management (DSM) from all the study population using a questionnaire.ResultsAt 12 weeks, significant improvements in the DSM subscales were observed, which includes glucose management (P = 0.042), dietary control (P = 0.048), physical activity (P = 0.043), health care use (P = 0.001) and self-care (P = 0.001), compared to the values at baseline. At baseline, when the HbA1c level was 8.2%, at 12 weeks, it dropped to 7.9%. Also, at baseline, when the hypoglycemia frequency was 3.1, it declined to 1.2 episodes/month at 12 weeks. While comparing the blood glucose monitoring through BGM at the baseline (1.92/day), a higher degree of frequency of blood glucose monitoring was evident at 12 weeks (6.84/day), after the patients employed the FreeStyle Libre.ConclusionAfter 12 weeks of using the FreeStyle Libre, the frequency of hypoglycemic episodes and the HbA1c levels were dropped, while the practice of DSM and frequency of blood glucose monitoring were improved.     

The relationship of glycemic control,insulin dose,and race with hypoglycemia in youth with type 1 diabetes

IntroductionBlack youth with T1D have been reported to experience more episodes of hypoglycemia than white patients, despite blacks having higher levels of HbA1c. We hypothesized that black patients may be prescribed higher daily doses of insulin putting them at greater higher risk for hypoglycemia.MethodsWe performed a retrospective analysis of data from a study of social and environmental factors influencing HbA1c in a biracial pediatric population with T1DM. Changes in patient insulin dose were made at clinic visit based on their self-monitored glucose (SMG) data. Insulin dose (units/kg/d) was compared with HbA1c, reported hypoglycemic episodes and occurrence of low blood glucose from SMG data.ResultsAge, duration of diabetes and BMI-z were similar for black and white patients. Black patients had higher levels of HbA1c and mean blood glucose (MBG). HbA1c was higher in blacks even after adjustment for MBG. Reported insulin dose increased with increasing HbA1c (ρ = 0.30, p = 0.0052) or MBG (ρ = 0.36, p < 0.0008). There was no difference in insulin dose between blacks and whites. Reported hypoglycemia was inversely associated with HbA1c and MBG, but there was no racial difference. Occurrence of low glucoses from meter data was slightly higher in whites (p = 0.047).ConclusionInsulin dose increased with increasing HbA1c or MBG for both groups. Occurrence of hypoglycemia was inversely related to glycemic control. There was slightly higher occurrence low glucose meter readings in white patients. Reported racial disparities in occurrence of hypoglycemia and insulin dosing may be due to clinic specific factors.     

Glycaemic control in individuals with type 1 diabetes using an open source artificial pancreas system (OpenAPS)

Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9–10 mmol/L was 77.5 ± 10.5%, <3.9 mmol/L was 4.3 ± 3.6%, <3.0 mmol/L was 1.3 ± 1.9%, >10 mmol/L was 18.2 ± 11.0% and > 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor-augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (−0.6 ± 0.7; P < 0.0001), lower estimated HbA1c (−0.4 ± 0.5%; P < 0.0001), higher time in range 3.9–10 mmol/L (+9.3 ± 9.5%; P < 0.0001), less time < 3.0 mmol/L (−0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (−2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (−0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology-adept cohort, despite not being approved for the treatment of individuals with T1D.     

Impact of flash glucose monitoring on glycaemic control and quality of life in patients with type 1 diabetes: A 18-month follow-up in real life

We conducted a prospective observational study to evaluate the medium-term impact of the flash glucose monitoring system (FGM) in a type 1 diabetic population. We included 248 patients, switched from conventional blood glucose monitoring (BGM) to FGM. We evaluated glycaemic control at 2–4 (T1) and 5–11 (T2) months after initiation and at the last available visit (T3, 18 ± 4 months). We asked patients to fill in, at T0 and T2, two questionnaires based on the Diabetes Treatment Satisfaction Questionnaire; and on the Hypoglycaemia Fear Survey.Glycaemic control improved, from 8.1 ± 1.3% at T0 to 7.8 ± 1.2% at T1 (p < 0.001) and remained unchanged after. Average number of controls increased from 3.2 ± 1.2 BGM to 7.7 ± 3.9 at T1 (p < 0.001). We observed a modest decrease in daily insulin doses. We evidenced an increase in mild hypoglycaemic events, especially in well-controlled subjects, but no increase of severe events. Satisfaction score improved from 30.5 ± 7.7 points to 38.3 ± 5.1 points (p = 0.018), was correlated with the reduction in and was higher in less controlled patients at inclusion. “Behaviour” score regarding hypoglycaemias decrease from 5.7 ± 4.1 to 4.4 ± 3.6 points (p < 0.001).In conclusion, this 18-months study trial indicates that using the FGM technology in patients with T1D may improve glycaemic control, in real-life conditions, especially in less controlled patients. FGM was associated with an increase of patients’ satisfaction regarding treatment. Hypoglycaemic events, however, were not reduced in frequency. Therefore, the need for an educational team and a structure program in the management of this new technology remains mandatory.     

The associations between physical activity,health-related quality of life,regimen adherence,and glycemic control in adolescents with type 1 diabetes: A cross-sectional study

BackgroundAdolescents with Type 1 Diabetes (T1D) display a greater than two-fold higher risk of developing diabetes-related complications compared with their healthy peers and the risk increases markedly as glycated hemoglobin (HbA1c) increases. The majority of the known factors associated with improved glycemic control in adolescents with T1D are geared toward Western populations. Therefore, this study examined the associations between Physical Activity (PA), Health-Related Quality of Life (HRQoL), and regimen adherence on glycemic control in a Middle Eastern population of adolescents with T1DMethodsThe study utilized a cross-sectional design of Jordanian adolescents (aged 12–18) with T1D (n = 74). Self-reported measures used were the Pediatric Quality of Life-Diabetes Module, the International Physical Activity Questionnaire, and the Summary of Diabetes Self-Care Activities. HbA1c values were obtained from the medical records. Correlation analyses were conducted using Pearson’s and Spearman’s correlation tests. Multiple regression analyses were conducted to determine if HRQoL, PA, and regimen adherence predict glycemic control.ResultsOnly 14.8 % of the participants demonstrated good glycemic control (HbA1c ≤ 7.5 %). Participants with poor control had a statistically significant lower mean PA of MET-minutes/week (3531.9 ± 1356.75 vs. 1619.81 ± 1481.95, p < .001) compared to those with good control. The total sample was found to demonstrate low HRQoL (47.70 ± 10.32). Participants were within the acceptable range of PA (1885.38 ± 1601.13) MET-minutes/week. HbA1c significantly inversely correlated with PA (r = −0.328, p = .010) and regimen adherence (r = −0.299, p = .018). The regression analysis revealed that PA significantly predicted glycemic control (β = −0.367, p < .01) as adherence (β = −0.409, p < .01) and disease duration did (β = 0.444, p < .01).ConclusionBetter glycemic control was significantly associated with higher PA and regimen adherence levels. The correlation between PA and glycemic control depends highly on the level of regimen adherence or arguably, adherence acts as a buffer in the correlation between PA and glycemic control. There was no significant association between glycemic control and HRQoL.     

Insulin Bolusing Software: The Potential to Optimize Health Outcomes in Type 1 Diabetes Mellitus

Background:

Insulin bolusing calculators alleviate the burden of having to calculate insulin bolus doses for patients with type 1 diabetes mellitus (T1DM). Three important pieces of information are needed: a blood glucose monitoring (BGM) result, carbohydrates to be consumed, and the amount of insulin bolus delivered. The purpose of this study was to describe insulin pump adherence behaviors associated with the use of bolus calculators in youth who use Medtronic insulin pumps.

Methods:

Data were downloaded from the MiniMed Paradigm insulin pumps (Medtronic) of 31 youth with T1DM. Areas of adherence that were evaluated included fundamental insulin pump adherence behaviors (e.g., BGM, carbohydrate entry, and insulin bolusing), decisions about Wizard^® recommendations, and three Wizard steps: BGM result–carbohydrate input–insulin bolus.

Results:

On average, patients conducted BGM ≥4 times/day on 69% of days, inputted carbohydrates ≥3 times/day on 63% of days, and insulin bolused ≥3 times/day on 85% of days. Participants generally followed Wizard recommendations. Finally, participants completed all three Wizard steps (BGM, carbohydrate input, insulin bolus) within 30 min for an average of 29% of boluses. Almost 3% of boluses that were preceded by Wizard use were delivered without conducting BGM or inputting carbohydrates.

Conclusion:

There was substantial variability in insulin pump adherence behaviors (e.g., days when no BGM occurred, reliance on basal insulin). Interventions targeting insulin pump adherence behaviors have the potential to optimize diabetes health outcomes and glycemic control. Improving insulin pump software reports is one promising avenue for improving adherence.     

Hemoglobin A1c,frequency of glucose testing and social disadvantage: Metrics of racial health disparity in youth with type 1 diabetes

Introduction

Black youth with type 1 diabetes (T1D) have higher HbA1c than whites. To understand HbA1c differences, we examined the relationship of psycho-social factors and glucose testing with HbA1c.

Efficacy of flash glucose monitoring in pregnant women with poorly controlled pregestational diabetes (FlashMom): A randomized pilot study

Background and aimsGood glycemic control is crucial to reduce the risk of adverse pregnancy outcomes. Our aim was to evaluate the efficacy of Flash Glucose Monitoring (FGM) on glucose control in women with pregestational diabetes.Methods and resultsForty women with inadequately controlled type 1 (T1D, n = 34) and type 2 (T2D, n = 6) diabetes at conception were randomly assigned to two arms: the Flash Glucose group (FG, n = 21) using FGM, and the control group (CG, n = 19) using self-monitoring of blood glucose (SMBG). Glycated hemoglobin (HbA1c, %), time in (TIR), below (TBR) and above (TAR) range, glucose variability as well as the occurrence of maternal and neonatal adverse outcomes, were evaluated.HbA1c decreased significantly (p < 0.01) and similarly (?0.65 ± 0.7 vs. ?0.67 ± 0.8 for FG and CG, respectively; p = 0.89) in both groups during pregnancy. HbA1c reduction was positively associated with the number of daily FGM scans (p < 0.01). TBR (12.1 ± 2.0% vs. 19.6 ± 3.9%, p = 0.04) and the mean of the daily serum glucose difference (MODD) index (59.1 ± 5.4 vs. 77.7 ± 4.6, p = 0.02) were significantly lower in FG at second trimester. The rates of perinatal adverse outcomes were not different in the two studied groups.ConclusionsIn women with pregestational diabetes, FGM and SMBG had similar efficacy on glucose control during pregnancy. FGM showed additional advantages in terms of TBR and glucose variability. Achievement of good metabolic results depended on the adequate use of glucose sensor.RegistrationAt ClinicalTrials.gov as NCT04666818 on December 14, 2020.     

Race/ethnicity and measures of glycaemia in the year after diagnosis among youth with type 1 and type 2 diabetes mellitus

AimsTo assess associations between race/ethnicity, glycated hemoglobin (HbA1c), and glycemic control among youth with type 1 (T1D) or type 2 diabetes (T2D).MethodsThe study sample was youth < 20 years old from the SEARCH California Center diagnosed from 2002 to 2009 who remained insured for at least one year. HbA1c at one year was from clinical data; HbA1c at diagnosis was from clinical data (81%) or imputed (19%). Multivariable logistic and linear regression models were used to examine associations between race/ethnicity and poor glycemic control (≥ 9.5%), HbA1c at one-year, and change in HbA1c.ResultsThe study included 1162 Hispanic (52.3%), non-Hispanic White (NHW, 28.4%), African American (15.1%) and Asian/Pacific Islander (4.1%) youth. Among T1D youth (n = 789), Hispanics were 1.60 times as likely (95% CI 1.01–2.53) to have poor control at one year compared to NHWs, after adjustments. Among T2D youth (n = 373), only African American youth were significantly more likely (OR = 4.85; 95% CI 1.49–15.77) to have poor control at one year, after adjustments. HbA1c at one year and change in HbA1c did not differ by race/ethnicity.ConclusionPoor glycemic control was evident one year after diagnosis in some minority youth with T1D or T2D in an integrated managed health care setting.     

Predictors of use and improvement in glycemic indices after initiating continuous glucose monitoring in real world: Data from Saudi Arabia

Background and aimsTo identify predictors of use and benefit from continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D).MethodsPredictors of CGM use and changes in glycemic indices and other clinical parameters after initiating intermittently-scanned CGMs were examined in 116 individuals with T1D living in Saudi Arabia. Participants were categorized based on frequency of CGM sensor scanning at month 6 into: Frequent users (≥10 scans/day) and infrequent users (<10 scans/day).ResultsFrequent CGM users had an improvement in time in range (TIR) and time above range (TAR) at months 6 and 12; whereas infrequent users had comparable improvements but only at month 12. Individuals with baseline TIR <50% had a significant improvement in TIR and TAR; whereas those with baseline TIR ≥50% had a significant improvement only in time below range (TBR). Baseline TIR <50% and higher frequency of scans were predictive of improvement in TIR at month 6 (OR: 4.84, p <0.01, 1.05, p= 0.04; respectively); whereas baseline TBR was the only predictor of improvement in TBR (OR:1.24,p < 0.01). Being a woman, higher number of scans/day during the first 2 weeks of CGM use, and having a lower A1C at baseline predict being a frequent scanner at month 6 (OR: 2.81, p=0.04; 1.12, p <0.01; and 0.73, p <0.01; respectively).ConclusionsImprovement in glycemic control with CGM use can be predicted by: number of scans per day and baseline TIR and TBR in people with T1D.     

Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting

ObjectiveThere is limited real-life data demonstrating that hypo-/hyperglycemic alarms added to continuous glucose monitoring (CGM) improve metabolic control in adults with type 1 diabetes (T1D).We evaluated the usefulness of switching from a flash or intermittent-scanned continuous glucose monitoring (is-CGM) device without low or higher glucose alarms to a is-CGM device with alarms to prevent hypoglycemia in adults with T1D.MethodsIndividuals with T1D and fearful of hypoglycemia, prone to hypoglycemia unawareness, and/or experiencing severe hypoglycemia while using is-CGM Free Style Libre 1 (FSL1) were switched to FSL2 with individually-programmable low glucose alarms. The primary endpoint was the changes in % time below range (TBR%) <70 mg/dl [3.9 mmol/l] and <54 mg/dl [3.0 mmol/l] after 12 weeks on FSL2 compared with FSL1. Secondary endpoints were changes in % time in range (TIR% 70–180 mg/dl [3.9–10.0 mmol/l]), % time above range (TAR%) >180 [10.0 mmol/l], mean interstitial glucose, glycemic management indicator (GMI), interstitial glucose coefficient of variation (CV%), hemoglobin A1c, and sensor's scans/day.ResultsWe included 108 individuals (57.4 % men), aged 58.2 ± 17.3 [95 % CI: 55.0 to 61.5] years, with mean diabetes duration 25 ± 14.6 [95 % CI: 22.1 to 27.7] years. Among individuals, 40 (37.0 %) had hypoglycemia awareness with Clarke's score ≥4 and 19 (17.5 %) had a history of severe hypoglycemia. The median low glucose alarm threshold was 70 [IQR: 65–70] mg/dl (3.9 [IQR: 3.6–3.9] mmol/L). By comparison of first 12 weeks on FSL2 vs. last 12 weeks on FSL1, TBR% <70 mg/dl decreased from 4.5 ± 4.4 to 2.3 ± 2.8 % (p < 0.001), TBR% <54 mg/dl decreased from 1.4 ± 2.2 to 0.3 ± 0.9 % (p < 0.001). TIR% was not significantly different (51.5 ± 14.9 vs. 52.9 ± 16 % (p = 0.13)), nor was TAR% (43.8 ± 16.2 vs. 44.7 ± 16.5 % (p = 0.5)). CV% decreased from 39.4 ± 6.9 to 37.9 ± 6.1 % (p < 0.001). Those at risk for hypoglycemia (TBR >4 % and >1 %, respectively, at baseline) showed a significant decrease in the incidence of hypoglycemia <70 and <54 mg/dl (p < 0.0001). Patients' satisfaction with hypoglycemia alarms was high, since all individuals opted to pursue using individual alarm beyond the study period.ConclusionSwitching from FSL1 to FSL2 with low glucose alarms reduced the frequency of hypoglycemia in middle-age adults with T1D, particularly in those who were prone to hypoglycemia awareness or severe hypoglycemia.     

Association of C-peptide level with peripheral neuropathy in type 2 diabetes: An observational cross-sectional preliminary study

Backround and aimsPeripheral neuropathy is the most common microvascular complication of diabetes mellitus. In subjects with type 1 diabetes (T1D) relationship of C-peptide levels and neuropathy has been observed in several studies, however, there are very few studies in type 2 diabetes (T2D) subjects. In this study we aim to assess the association of C-peptide levels with peripheral neuropathy in Indian subjects with T2D.MethodOne hundred patients of T2D were included in this study. Clinical and laboratory parameter was assessed for all participants. The C-peptide level was measured by fluorometric enzyme immunoassay method. Assessment of diabetic peripheral neuropathy was based on diabetic neuropathy symptom score and the diabetic neuropathy examination scores.ResultsTotal 100 patients completed the study. Mean age of subjects was 60.03 years and male: female ratio was 1.17. Peripheral neuropathy was detected in 47% of subjects evaluated. Subjects were further divided in to neuropathy group and no-neuropathy group for analysis. Age in neuropathy group was significantly higher than no-neuropathy group [65.62 ± 10.5 vs 55.08 ± 9.41 yrs (p-value <0.0001)] and similarly duration of T2D was significantly higher in neuropathy group [10.11 ± 6.13 vs 4.16 ± 3.7 yrs (p-value <0.0001)]. Importantly mean fasting C-peptide (2.27 ± 0.98 vs 3.12 ± 0.84 ng/ml) and mean post meal C-peptide (4.27 ± 1.34 vs 5.33 ± 0.89 ng/ml) were significantly lower in neuropathy group compared to no-neuropathy group. An association of HbA1c level and neuropathy was statistically not significant (p = 0.793).ConclusionSerum C-peptide concentrations are associated with peripheral neuropathy in T2DM patients, independent of the degree of glycemic control.     

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

Background and aimsDespite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.Methodology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use.Results188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1–10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19–1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79–2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66–0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58–0.95; p:0.017).ConclusionIn spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.     

Starch digestibility modulation significantly improves glycemic variability in type 2 diabetic subjects: A pilot study

Background and aimsIn type 2 diabetes (T2D) patients, the reduction of glycemic variability and postprandial glucose excursions is essential to limit diabetes complications, beyond HbA1c level. This study aimed at determining whether increasing the content of Slowly Digestible Starch (SDS) in T2D patients’ diet could reduce postprandial hyperglycemia and glycemic variability compared with a conventional low-SDS diet.Methods and resultsFor this randomized cross-over pilot study, 8 subjects with T2D consumed a controlled diet for one week, containing starchy products high or low in SDS. Glycemic variability parameters were evaluated using a Continuous Glucose Monitoring System.Glycemic variability was significantly lower during High-SDS diet compared to Low-SDS diet for MAGE (Mean Amplitude of Glycemic Excursions, p < 0.01), SD (Standard Deviation, p < 0.05), and CV (Coefficient of Variation, p < 0.01). The TIR (Time In Range) [140–180 mg/dL[ was significantly higher during High-SDS diet (p < 0.0001) whereas TIRs ≥180 mg/dL were significantly lower during High-SDS diet. Post-meals tAUC (total Area Under the Curve) were significantly lower during High-SDS diet.ConclusionOne week of High-SDS Diet in T2D patients significantly improves glycemic variability and reduces postprandial glycemic excursions. Modulation of starch digestibility in the diet could be used as a simple nutritional tool in T2D patients to improve daily glycemic control.Registration numberin clinicaltrials.gov: NCT 03289494.     

Impact of decreased physical activity due to COVID restrictions on cardio-metabolic risk parameters in Indian children and youth with type 1 diabetes

Background and aimsImplementation of COVID restrictions following lockdown led to changes in routines of subjects with type 1 diabetes due to closure of schools and outdoor activities. The aim of this study was to assess the impact of decreased physical activity due to COVID restrictions on cardio-metabolic risk parameters (waist circumference, laboratory and body composition parameters) in Indian children and youth with T1D.MethodsThis observational study included 312 participants (2–21 years) with T1DM having disease duration of ≥6 months. Demographic, anthropometric, body composition data and investigations were recorded at two time points (during COVID restrictions when children came for follow up, pre COVID restriction data from medical and electronic records).ResultsGlycemic control improved during COVID restrictions (Hba1c: 9.8 ± 2.0%) as compared to pre COVID restrictions (Hba1C: 10.0 ± 1.9%) (p < 0.05). However, due to significant reduction in physical activity (p < 0.05) there was significant increase in waist circumference z-scores (?0.9 ± 1.1 vs ?1.6 ± 1.1), body fat percentage z-scores (?0.2 ± 1.0 vs ?0.3 ± 0.9) and lipid parameters like cholesterol, low density lipid cholesterol and triglyceride concentrations (p < 0.05) without any significant change in body mass index z-scores (p > 0.05).ConclusionsProlonged COVID restrictions, although led to modest improvement in glycemic control, there was worsening of cardio-metabolic risk factors or indices of adiposity like waist circumference, body fat percentage and lipid parameters which could be attributed to decreased time spent in physical activity.