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At least 19 recordsLinked to original sources
PubMed2003

Continuous glucose monitoring for treatment adjustment in diabetic pregnancies--a pilot study.

AIMS: To examine the efficacy of a continuous glucose monitoring (CGM) system for treatment adjustment in patients with diabetic pregnancy treated with insulin. METHODS: The study sample consisted of eight women with diabetic pregnancy, six with pre-pregnancy Type 1 diabetes mellitus and two with gestational diabetes (GDM), all being treated with multiple daily insulin injections. Gestational age ranged from 24 to 32 weeks. Data derived from the Continuous Glucose Monitoring System (MiniMed) for 72 h were compared with fingerstick glucose measurements (six to eight times a day), and treatment was adjusted on the basis of the findings. Two to four weeks later, the patients were re-evaluated with CGM. RESULTS: In the first part of the study, an average of 744+/-33 glucose measurements was recorded for each patient with CGM. The mean total time of hyperglycaemia (glucose level >7.7 mmol/l) undetected by the fingerstick method was 152+/-33 min/day. Nocturnal hypoglycaemic events (glucose level <2.7 mmol/l) were recorded in seven patients. Based on the additional information obtained by continuous monitoring, the insulin regimen was changed in all patients. CGM re-evaluation after treatment adjustment showed a reduction in undetected hyperglycaemia to 89+/-17 min/day and in nocturnal hypoglycaemic events, which were recorded in only one patient. CONCLUSIONS: Continuous glucose monitoring may diagnose high blood glucose levels and nocturnal hypoglycaemic events that are unrecognized by intermittent blood glucose monitoring and could serve as a useful tool for the long-term management of diabetic pregnancies. A large prospective study is needed to determine the clinical implications of this new monitoring technique.

Adult↗
PubMed2003

Continuous glucose monitoring for the evaluation and improved control of gestational diabetes mellitus.

OBJECTIVES: To compare the daily glycemic profile reflected by continuous versus self-monitoring of blood glucose in women with gestational diabetes mellitus (GDM), and to evaluate possible differences in treatment strategy based on the two monitoring methods. MATERIALS AND METHODS: The study sample consisted of 57 women with gestational diabetes, 47 in Israel and ten in California. Gestational age ranged from 24 to 32 weeks in the Israeli women, and 32 to 36 weeks in the American women. Data derived from the Continuous Glucose Monitoring (CGM) System (MiniMed) for 72 h were compared to fingerstick glucose measurements (6-8 times a day). During continuous monitoring, patients documented the timing of food intake, insulin injections and hypoglycemic events. RESULTS: In the Israeli group, 23 women were treated by diet alone, and 24 by diet plus insulin. An average of 763 +/- 62 glucose measurements was recorded for each patient with continuous glucose monitoring. The mean total time of hyperglycemia (glucose level > 140 mg/dl) undetected by the fingerstick method was 132 +/- 31 min/day in the insulin-treated group and 94 +/- 23 min/day in the diet-treated group. Nocturnal hypoglycemic events (glucose levels < 50 mg/dl) were recorded in 14 patients, all insulin-treated. On the basis of the additional information provided by continuous monitoring, the therapeutic regimen (insulin therapy, diet adjustment, or both) was changed in 36 of the 47 patients. All ten American women were treated with insulin. The mean time of undetected hyperglycemia for a total group monitoring time of 30 days was 78 +/- 13 min/day. Eight women had nocturnal hypoglycemia on at least one of the three nights of monitoring for a total of 12 nights. A change in insulin dosage was made in all women on the basis of the data provided by continuous glucose monitoring. CONCLUSION: Continuous glucose monitoring is helpful for monitoring women with GDM and for adjusting diabetes therapy. It can accurately detect high postprandial blood glucose levels and nocturnal hypoglycemic events that may go unrecognized by intermittent blood glucose monitoring. A large prospective study on maternal and neonatal outcome is needed to determine the clinical implications of this new monitoring technique.

Adult↗
PubMedDate not supplied

Continuous glucose monitoring has left the station: are you onboard?

Continuous glucose monitoring (CGM) is a new technology that is poised to dramatically alter the practice of managing diabetes in the near future. To understand the potential utility of CGM in clinical practice, the goal for monitoring glucose must be redefined. Is obtaining a mere snapshot of the current blood glucose level a satisfactory goal? This differs substantially from a more comprehensive assessment of the patient's current (and immediate future) glycemic status that could be gained through real-time continuous monitoring. The diabetes educator will play a critical role in introducing and implementing the next generation of real-time CGM systems.

Blood Glucose↗
PubMed2006

[Continuous glucose monitoring in high-risk subjects for developing type 2 diabetes].

BACKGROUND AND OBJECTIVE: To assess the relevance of unrecognised hyperglycaemia among high-risk subjects for developing type 2 diabetes. SUBJECTS AND METHOD: Cross-sectional multicentre study in primary care. Subjects aged 40 to 75 years with previous evidence of impaired glucose metabolism were screened according to the World Health Organization rules by means of an oral glucose tolerance test (OGTT). Those with diabetes diagnostic criteria were excluded. An individualized 48-h ambulatory continuous glucose monitoring (CGM) calibrated by plasma values (Glucoday) was performed. Data from biosensor activity were computerized in order to identify hyperglycaemic records. RESULTS: 50 out of 64 screened subjects were included and evaluated; there were 30 female (60%). Mean age was 59 years; body mass index, 30.9; HbA1c, 5.3%, and HOMA, 2.8. Regarding the CGM results, 9 whole registers (18%) were rejected because of technical problems and 41 continuous profiles (72%) were analysed, corresponding to 17 (41.5%) subjects with normal OGTT and 24 (58.5%) with non-diabetic hyperglycaemia on OGTT. Mean CGM time with hyperglycaemia (fasting > or = 6.1 mmol/l or non-fasting > or = 7.8 mmol/l) accounted for 5.8 h, a 17.2% of the effective register per subject (33.8 h). Both groups matched the World Health Organization diabetes diagnostic interval (fasting > or = 7 mmol/l or non-fasting > or = 11.1 mmol/l): 1.1 h of effective register (3.6%) and 1.2 h (3.3%), respectively. CONCLUSIONS: The CGM evidenced a high degree of unrecognised hyperglycaemia among high-risk individuals. Therefore, casual plasma glucose measurements should be recommended to achieve early screening.

Adult↗
PubMed2006

Relationship of fasting and hourly blood glucose levels to HbA1c values: safety, accuracy, and improvements in glucose profiles obtained using a 7-day continuous glucose sensor.

OBJECTIVE: In this study, we evaluated the safety and efficacy of 7-day transcutaneous, real-time, continuous glucose monitoring (CGM) in subjects with insulin-requiring diabetes. RESEARCH DESIGN AND METHODS: Eighty-six subjects were enrolled at five U.S. centers. Subjects wore a sensor inserted under the skin of the abdomen for 7 days during each of three consecutive periods. Data were blinded during period 1 and unblinded during periods 2 and 3. RESULTS: Of the 6,811 matched self-monitoring of blood glucose to sensor values prospectively analyzed, 97.2% fell in the Clarke error grid zones A and B, and median absolute relative difference was 11.4%. After unblinding, subjects reduced time spent at <55 mg/dl by 0.3 h/day, reduced time spent at >240 mg/dl by 1.5 h/day, and increased time in the target zone (81-140 mg/dl) by 1.4 h/day (P < 0.05 for all three comparisons). Improvements were seen in both types 1 and 2 diabetes and with use of both multiple daily injections and continuous subcutaneous insulin infusion. Modal day graphs were generated in six groups of subjects based on HbA1c (A1C) ( 10%). Mean glucose levels from midnight to 7:00 a.m. (fasting and dawn phenomenon periods) were only normal for subjects with A1C <or=6%. All other groups were hyperglycemic during this and all periods. Reductions in overall mean glucose were achieved for the four highest A1C groupings with unblinded device use. CONCLUSIONS: This is the first report of a real-time, transcutaneous glucose sensor that functioned for 7 days. The use of CGM in the unblinded phase resulted in improvements in target-range glycemia across all A1C values.

Blood Glucose↗
PubMed2025

Evolution of the Artificial Pancreas: Components and Integration-CGMs, Insulin, and AP Systems.

The landmark Diabetes Control and Complications Trial (DCCT) showed that glucose control is critical to reducing the risk of diabetes-related complications. This chapter outlines a series of innovations and investigations that followed the DCCT, aimed at minimizing the risk of hypoglycemia while further improving glucose control. The chapter presents an example of innovations in wired enzyme technology that facilitated the movement from capillary glucose monitoring to continuous glucose monitoring (CGM) and ultimately, the first-factory calibrated CGM system. The next glycemic management innovation was to connect CGM data to an insulin pump containing an algorithm able to adjust insulin delivery based on the changing glucose levels and trends. The key features of automated insulin delivery (AID) systems, currently approved in the United States, are presented. The AID summary table includes type of pump, type and function of the insulin delivery algorithm, the data management system, and the indications for use. The next section explores the innovation of alternative routes of insulin delivery to move toward the goal of a fully automated insulin delivery system. The main trials in developing and implementing an implantable intraperitoneal programmable system are summarized. The last section explores if sensor input in addition to glucose levels such as continuous sensing of ketone, lactate, or insulin levels may provide valuable feedback to move us closer to a fully autonomous AID system. Much of this diabetes innovation and investigation work has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases over that last 75 years.

Humans↗
PubMed2026

Interaction of melatonin receptor 1B (MTNR1B) genotype and type of breakfast (protein-enriched v carbohydrate-rich) on postprandial glucose response: a randomised crossover trial.

BACKGROUND: The risk allele (G) of MTNR1B rs10830963 has been associated with impaired glucose tolerance, increased fasting glucose and type 2 diabetes (T2D). Late evening eating, when endogenous melatonin levels are elevated, is associated with impaired glucose control in MTNR1B risk carriers. Endogenous melatonin levels remain elevated into the morning so may influence glucose response to breakfast. OBJECTIVE: To investigate the interaction of MTNR1B genotype and type of breakfast on postprandial glucose response in a real-world setting. METHODS: Following an overnight fast, participants consumed either a standard carbohydrate-rich or protein-enriched porridge breakfast. Post-prandial glucose levels were recorded for two hours using a continuous glucose monitor (CGM). One week later, participants repeated the protocol consuming the alternate breakfast. A two-way mixed ANOVA determined the effect of breakfast and genotype on post-prandial glucose levels. RESULTS: Fifty-four adults completed the study. Fasting glucose was significantly higher (p&#x2009;=&#x2009;0.008) in GG (5.53&#x2009;&#xb1;&#x2009;0.43&#x2009;mmol/L) compared to CC or CG participants (5.02&#x2009;&#xb1;&#x2009;0.42 and 5.19&#x2009;&#xb1;&#x2009;0.52&#x2009;mmol/L). Post-prandial iAUC was significantly greater following the carbohydrate-rich breakfast compared to the protein-enriched breakfast (p&#x2009;<&#x2009;0.001). Following the carbohydrate-rich breakfast iAUC was significantly greater in GG participants compared to CC (p&#x2009;=&#x2009;0.026) and CG participants (p&#x2009;=&#x2009;0.029). There was no significant difference between genotype groups following the protein-enriched breakfast (p&#x2009;>&#x2009;0.05). CONCLUSION: The study findings demonstrate, in a relatively young and healthy population, MTNR1B genotype significantly affects markers associated with T2D risk. Personalised genotype-based advice to adjust timing and composition of meals consumed when endogenous melatonin levels are increased may reduce subsequent T2D risk.

Humans↗
PubMed2026

Relationship between participant-reported outcomes, residual beta cell function and metabolic parameters in youth with newly diagnosed type 1 diabetes.

AIMS/HYPOTHESIS: Clinical trials of interventions to preserve beta cell function in new-onset type 1 diabetes frequently employ participant-reported outcome measures (PROMs). However, the expected changes in PROMs scores immediately following diagnosis and their association with residual beta cell function, metabolic markers and continuous glucose monitoring (CGM) are unclear. METHODS: Repeated PROMs including Paediatric Quality of Life Inventory diabetes module (PedsQL) and hypoglycaemia fear survey (HFS) were recorded from participants aged 10-18 years with newly diagnosed type 1 diabetes and their parents in two clinical trials: CLOuD (N=97, hybrid closed loop [HCL] vs multiple daily injections [MDI]) and USTEKID (N=72, ustekinumab immunotherapy vs placebo). Scores were compared with serial mixed meal-stimulated C-peptide levels (AUC C-peptide), HbA1c and CGM data. RESULTS: PedsQL and HFS scores for children/adolescents and their parents showed wide variation between individuals but did not change substantially within individuals over the first 48 months from diagnosis. Baseline scores were highly predictive of scores at 12-48 months (p<0.001). PedsQL scores were higher (better) in those reported by children/adolescents than by their parents (p<0.01). In contrast, HFS scores were higher in parents than children (p<0.001), indicating more fear. Strong correlations were observed between child and parent scores (p<0.001). No significant improvement in these scores was detected following intervention (ustekinumab or HCL). Meta-analysis revealed modest but statistically significant associations between HbA1c and PedsQL (&#x3b2;(std)=-0.11; 95% CI -0.20, -0.03) and HFS (&#x3b2;(std)=0.11; 95% CI 0.00, 0.21), and between CGM time in range and PedsQL (&#x3b2;(std)=0.14; 95% CI 0.03, 0.26) but not HFS (&#x3b2;(std)=-0.05; 95% CI -0.16, 0.06). Beta cell function (AUC C-peptide) was strongly associated with HbA1c (&#x3b2;(std)=-0.29; 95% CI -0.39, -0.20) and CGM time in range (&#x3b2;(std)=0.41; 95% CI 0.30, 0.52). Higher beta cell function showed a trend towards better PedsQL (&#x3b2;(std)=0.11; 95% CI -0.03, 0.25) and lower HFS (&#x3b2;(std)=-0.05; 95% CI -0.17, 0.07) but this did not reach statistical significance. CONCLUSIONS/INTERPRETATION: PedsQL and HFS scores changed little during the first 48 months after diagnosis of type 1 diabetes. These scores showed modest but statistically significant associations with measures of glucose management (HbA1c and CGM time in range), whereas the relationships with residual beta cell function (C-peptide) were weaker and did not reach significance. The modest size of these effects suggests current PROMs capture only limited aspects of the clinical benefit associated with beta cell preservation. Future research should incorporate psychometric instruments that are specifically adapted for young people using modern diabetes technologies and undergoing disease-modifying therapy, to ensure outcomes are meaningfully represented in early-stage type 1 diabetes trials.

Adolescent↗
PubMedDate not supplied

Continuous glucose monitoring in managing diabetes in children.

Continuous glucose monitoring (CGM) devices have now been added to the repertoire of technological devices useful in the management of patients with diabetes. In this issue, Schiaffini and colleagues confirm and extend published data describing the benefits of CGM in diabetic children. Specifically, such monitoring enables clinicians to detect occult hypoglycemia not otherwise discernable with intermittent testing of blood glucose. Although results of monitoring are not yet available in real time, the data can be used to adjust insulin regimens to allow more effective glycemic control. This is especially important in the pediatric population for whom strict glycemic control has traditionally been limited owing to concerns about the negative effects of hypoglycemia on the developing central nervous system. Additionally, postprandial hyperglycemia can be more readily detected and controlled. CGM provides new and important information not necessarily provided by measurement of HbA1c, and will likely prove an indispensable adjunct to diabetes care. Finally, this procedure has potential applications in the diagnosis and management of patients with other metabolic disorders.

Blood Glucose↗
PubMedDate not supplied

Clinical experience with an integrated continuous glucose sensor/insulin pump platform: a feasibility study.

The recent US Food and Drug Administration approval of an integrated real-time continuous glucose monitoring (CGM) system and insulin pump (Medtronic Mini-Med Paradigm REAL-Time System; Medtronic MiniMed, Inc., Northridge, Calif) is the most recent breakthrough paving the way toward the development of a closedloop insulin delivery system that could revolutionize diabetes care. An early prototype of the MiniMed Paradigm REAL-Time System--which provided both realtime CGM and insulin pump therapy but was not yet fully integrated--was tested in 20 volunteer subjects with type 1 diabetes who wore the device for up to 2 y. Subjects were instructed on the technical use of the device but were provided no additional support, aside from their usual diabetes care. Participation in the trial averaged 317 d (minimum, 88 d; maximum, 618 d). Five participants prematurely dropped out of the study (2 because of the inconvenience of carrying 2 devices). Data from all subjects were analyzed. Subjects reduced their A1C by a mean of 1.1% (standard deviation, 0.8). After 3 mo of device use, the number of participants who achieved A1C <7% increased by more than 3-fold. Individuals with baseline A1C >or= 7% had a 67% likelihood of achieving an A1C <7% by the first follow-up assessment. This observational study of combined CGM and insulin pump therapy suggests that this innovative device can help people achieve improved glycemic control. Given that only about one third of individuals with diabetes are achieving glycemic control targets, more effective methods to help patients avoid the devastating consequences of uncontrolled diabetes are desperately needed.

Adolescent↗
PubMed2005

Seizures as the only clinical manifestation of reactive hypoglycemia: a case report.

A 45-yr old diabetic patient, who had received a diagnosis of grand mal epilepsy, for two episodes of nocturnal tonic-clonic convulsions, underwent continuous glucose monitoring (CGM). During CGM, the patient had an episode of tonic-clonic convulsion, and at the same time a prolonged nocturnal hypoglycemia (<2.2 mmol/l) was observed. CGM revealed that tonic-clonic convulsions, which had been interpreted as a manifestation of epilepsy, were in fact a symptom of severe hypoglycemia. In order to assess the pathogenesis of hypoglycemia, the patient underwent a 75-g oral glucose tolerance test, that was interrupted shortly afterwards for hypoglycemic symptoms (i.e., reactive hypoglycemia). To our knowledge, this is the first case ever reported of reactive hypoglycemia associated with Type 2 diabetes.

Blood Glucose↗
PubMed2006

Is glycemic variability important to assessing antidiabetes therapies?

Traditionally, statistical estimation of glycemic variability includes computing standard deviation of glucose readings or related statistics (eg, M value, mean amplitude of glucose excursions, and so forth). We advocate an alternative approach using risk measures of variability, which have substantial clinical and numerical advantages. In addition, continuous glucose monitoring (CGM) data have clinically important inherent temporal structure that should be taken into consideration. Thus, temporal variability methods are discussed for the analysis and interpretation of CGM output.

Blood Glucose↗
PubMedDate not supplied

Issues for the coming age of continuous glucose monitoring.

PURPOSE: This article reports the results of a symposium in which diabetes educators considered and discussed issues that are likely to arise when continuous glucose monitoring (CGM) becomes available and readily accessible. METHODS: Fifteen certified diabetes educators and 5 others with complementary expertise participated in a discussion based on their responses to 11 questions designed to elicit perspectives on issues related to CGM. Issues for discussion and debate include those related to patient acceptance and lifestyle, implications for professional practice and reimbursement, concerns about professional liability, use of CGM data by insurers and payers, and CGM data transfer. RESULTS: Educators offered varied and sometimes conflicting responses to CGM-related issues. CONCLUSIONS: Awareness of CGM-related issues will likely become an important part of diabetes professional development and perspectives in practice. Identifying and framing the issues before the new technologies become available allow diabetes educators to participate proactively in structuring the emerging policies, procedures, and standards of care.

Attitude of Health Personnel↗
PubMed2006

Intensive insulin therapy in the intensive care unit: assessment by continuous glucose monitoring.

OBJECTIVE: Hyperglycemia occurs in most critically ill patients. Using continuous glucose monitoring (CGM), we investigated whether intensive insulin therapy based on discontinuous glucose monitoring can achieve normoglycemia (80-110 mg/dl) in a medical intensive care unit (MICU). RESEARCH DESIGN AND METHODS: Fifty adults (men/women 31/19, age 62 +/- 16 years, nondiabetic/diabetic 30/20, intravenous/subcutaneous insulin 22/28, and Acute Physiology and Chronic Health Evaluation II score 22 +/- 7) were prospectively recruited. Forty-eight-hour CGM was performed using a subcutaneous glucose sensor (GlucoDay) and compared with arterial glycemia. Main outcome measures were percent of time in normoglycemia and accuracy/applicability of CGM. RESULTS: During 48-h CGM, glycemia reached target (80-110 mg/dl) in only 22 +/- 18%, was >140 mg/dl in 39 +/- 27%, and was <60 mg/dl in 5 +/- 10% of the time. Patients on subcutaneous versus intravenous insulin had more glycemia readings >110 mg/dl (P = 0.016). Glycemia was higher in diabetic patients (170 +/- 77 vs. 129 +/- 35 mg/dl, P = 0.013). BMI was an independent determinant for bad glycemic control (beta = 0.73, P < 0.0001). Diabetic state (beta = 0.47, P < 0.0001), septic shock (beta = 0.22, P = 0.045), sequential organ failure assessment score (beta = 0.40, P = 0.001), and use of corticoids (beta = 0.28, P = 0.014) and inotropics (beta = -0.24, P = 0.035) were independent determinants of insulin dose. GlucoDay values and arterial glycemia correlated well (r = 0.85, P < 0.0001, n = 555 after six-point calibration), with 97% of data falling in regions A and B of error grid analysis. There were no adverse events using GlucoDay. CONCLUSIONS: GlucoDay, a well-tolerated 48-h CGM system, revealed that normoglycemia was only achieved 22% of the time in MICU patients. Further studies should investigate whether application of CGM to titrate insulin therapy can improve patient outcome.

Blood Glucose↗
PubMed2005

Continuous glucose monitoring in interstitial subcutaneous adipose tissue and skeletal muscle reflects excursions in cerebral cortex.

Continuous glucose monitoring (CGM) is being explored using several types of glucose sensors. Some are designed for subcutaneous adipose tissue. It is important to determine to which extent these glucose fluctuations in different tissues reflect changes taking place in the central nervous system, where glucose sensing is thought to occur. We studied the ability of subcutaneous adipose interstitial fluid measurements to parallel glucose propagations in blood, muscle, and central nervous system (CNS) during hyper- and hypoglycemia. A subcutaneous CGM system was applied in the CNS, subcutaneous adipose tissue, and skeletal muscle of nine Vietnamese potbellied pigs, and data were compared with frequent sampling in blood. Alterations in glucose levels were induced with intravenous glucose and insulin. During hyperglycemia, no difference was detected in delay between blood and interstitial glucose levels in subcutaneous adipose tissue (18.0 +/- 0.8 min), muscle (18.0 +/- 0.9 min), and CNS (20.3 +/- 1.2 min), respectively. During hypoglycemia, we found no time difference between interstitial parameters in the three tissues. However, the amplitude of glucose changes varied considerably, with a smaller magnitude of glucose change taking place in the brain. The timing of glucose excursions in subcutaneous adipose tissue and muscle reflect excursions in CNS. The reduced magnitude of glucose excursions in the brain suggests that different mechanisms of glucose transport are operative in CNS compared with subcutaneous adipose tissue and muscle.

Adipose Tissue↗
PubMed2026

Factors Associated With Progression From Level 1 to Level 2 Sensor-Detected Hypoglycaemia in People With Type 1 and Insulin-Treated Type 2 Diabetes: A Post Hoc Analysis From the Hypo-METRICS Study.

AIMS: We investigated the proportion of sensor-detected hypoglycaemic (SDH) events progressing to level 2, and associated variables. MATERIALS AND METHODS: We used data from Hypo-METRICS, which recruited people with type 1 (pwT1D) and insulin-treated type 2 diabetes (pwT2D) with &#x2265;&#x2009;1 hypoglycaemic event in preceding 3&#x2009;months, wearing blinded continuous glucose monitoring devices (CGM), additional to usual monitoring modality, and FitBits for 10&#x2009;weeks. We defined: L1: SDH <&#x2009;3.9&#x2009;mmol/L for &#x2265;&#x2009;15&#x2009;min but &#x2265;&#x2009;3.0&#x2009;mmol/L; L2: SDH <&#x2009;3.9&#x2009;mmol/L with &#x2265;&#x2009;15&#x2009;min of sensor glucose <&#x2009;3.0&#x2009;mmol/L; L2 ratio&#x2009;=&#x2009;L2/(L1&#x2009;+&#x2009;L2), restricted to participants with both event types during the study period. Associations of selected variables on L2 ratio was assessed using beta regression and purposeful variable selection. RESULTS: We analysed 23&#x2009;768 SDH events from 212 pwT1D and 213 pwT2D. PwT1D were predominantly female (53% vs. 42% in T2D, p&#x2009;=&#x2009;0.023) and younger (median age 49 vs. 62&#x2009;years, p&#x2009;<&#x2009;0.001), with a higher L2 ratio (16% vs. 14%, p&#x2009;=&#x2009;0.03). Coefficient of variation (CV), OR&#x2009;=&#x2009;1.07, 95% CI&#x2009;=&#x2009;1.06-1.09 (p&#x2009;<&#x2009;0.001), and mean sensor glucose, OR&#x2009;=&#x2009;1.13, 95% CI&#x2009;=&#x2009;1.08-1.18 (p&#x2009;<&#x2009;0.001) were the principal predictors in T1D and T2D respectively; mean L1-SDH duration, weekly L1-SDH frequency, personal CGM usage, impaired awareness were not. Progression was higher during sleep than wakefulness (21% vs. 11%, p&#x2009;<&#x2009;0.001); L2 ratios during sleep did not differ by awareness status. CONCLUSIONS: Approximately one sixth of SDH events progressed to L2, with a higher proportion in T1D than T2D. The principal determinants were CV in T1D and mean glucose in T2D. Awareness status was not associated with progression risk during sleep.

Humans↗
PubMed2026

Gold and Clarke Questionnaires Identify Different People With Insulin-Treated Diabetes and Impaired Awareness of Hypoglycaemia in Almost 60% of Cases: A Post Hoc Analysis From the Hypo-METRICS Study.

AIMS: Gold and Clarke questionnaires are used to identify impaired awareness of hypoglycaemia (IAH) and severe hypoglycaemic event (SHE) risk in people with diabetes (pwD). We explored their overlap, including Clarke's Hypoglycaemia Awareness Status (Clarke-HAS) subfactor, and subsection differences in SHE incidence (Gold, Clarke, overlap). MATERIALS AND METHODS: This post hoc analysis of the Hypo-METRICS study recruited pwD on insulin (type 1: T1D; type 2: T2D) with &#x2265;&#x2009;1 hypoglycaemic event in the previous 3 months. IAH was defined as Gold &#x2265;&#x2009;4, Clarke &#x2265;&#x2009;4, Clarke-HAS &#x2265;&#x2009;2. RESULTS: Gold and Clarke were completed by 232 pwT1D and 285 pwT2D (age: 47 vs. 62&#x2009;years, p&#x2009;<&#x2009;0.001; HbA1c: 56&#x2009;mmol/mol (7.3%) vs. 57&#x2009;mmol/mol (7.4%), p&#x2009;=&#x2009;0.03). IAH prevalence in T1D vs. T2D was 21% vs. 26% (p&#x2009;=&#x2009;0.1), 14% vs. 18% (p&#x2009;=&#x2009;0.2), and 41% vs. 48% (p&#x2009;=&#x2009;0.2) according to Gold, Clarke and Clarke-HAS. The overlap between Gold &#x2265;&#x2009;4, Clarke &#x2265;&#x2009;4 was 40% (T1D: 45%; T2D: 37%); between Gold &#x2265;&#x2009;4, Clarke-HAS &#x2265;&#x2009;2 it was 45% (T1D: 43%; T2D: 47%). The overlap between Gold &#x2265;&#x2009;4, Clarke &#x2265;&#x2009;4 was 41% vs. 39% in CGM vs. non-CGM users (T1D: 50% vs. 35%; T2D: 32% vs. 40%); between Gold &#x2265;&#x2009;4, Clarke-HAS &#x2265;&#x2009;2 it was 39% vs. 53% (T1D: 38% vs. 54%; T2D: 40% vs. 53%). In the IAH sample, SHE rate was lower in people found with IAH by Gold vs. Clarke, 9% vs. 53%, and in those assessed by Gold-Clarke overlap vs. Clarke, 20% vs. 53% (all p&#x2009;<&#x2009;0.001). CONCLUSIONS: Gold and Clarke show moderate consistency (37%-54%) in identifying IAH in insulin-treated diabetes, with/without CGM use, each associated with different SHE rates, indicating the two questionnaires measure different, independent aspects of IAH.

Humans↗
PubMedDate not supplied

Fasting and post-prandial glycemia and their correlation with glycated hemoglobin in Type 2 diabetes.

OBJECTIVE: The relative contribution of fasting and post-prandial glucose to glycated hemoglobin (HbA1c) is controversial. In the present study, we assessed the relationship with HbA1c of fasting and post-prandial glucose measured in a more naturalistic setting, through home glucose self-monitoring or with a continuous glucose monitoring system (CGM). MATERIALS AND METHODS: A consecutive series of 300 patients with Type 2 diabetes were enrolled in the study, provided that they performed blood glucose self-monitoring. HbA1c and fasting plasma glucose (FPG) were measured at enrolment. RESULTS: Both fasting plasma and capillary glucose showed a significant correlation with HbA1c (r=0.66 and 0.61, respectively; p<0.001). When home glucose monitoring was considered, both mean fasting and post-prandial glucose showed a significant correlation with HbA1c (r=0.71 and 0.73, respectively). In patients in the lower tertile of body mass index (BMI), HbA1c showed a significant correlation at multivariate analysis with post-prandial glucose, but not with fasting glucose. In patients with HbA1c >7%, both fasting and post-prandial glucose showed a significant correlation, after adjustment for age and BMI, with HbA1c (both p<0.01); conversely, in those with HbA1c < or =7%, such a correlation could be observed for fasting (p<0.01), but not for post-prandial glucose. CONCLUSION: In conclusion, both fasting and post-prandial glucose contribute to the determination of HbA1c . Home glucose self-monitoring appears to provide a more accurate assessment of metabolic control than a single plasma glucose measurement in experimental conditions. Fasting glucose could provide a greater contribution to HbA1c in patients with lower HbA1c, while post-prandial glucose seems to play a major role in leaner Type 2 diabetic subjects.

Aged↗