BACKGROUND: The escalating prevalence of diabetes, with its multifaceted complications, poses a pressing challenge for healthcare systems globally. In response, the advent of continuous glucose monitoring (CGM) systems, offering technological solutions for daily diabetes management, presents significant opportunities. However, the widespread adoption faces several barriers, linked both to the technological configuration of the devices and to the psychological dimension of patients. Therefore, this study aims to apply and test a theoretical model that investigates the antecedents of the intention to use Continuous Glucose Monitoring systems.
METHODS: The research model was built to unveil the impacts of psychological factors, functional components and rational constructs derived from the Technology Acceptance Model (TAM) on CGM systems sustained adoption. To ensure the comparability of results, we have collected data from people who had used Dexcom ONE Dexcom (San Diego, CA) for the first time for at least one month. Employing Structural Equation Modelling (SEM) techniques, the hypothesized relationships among constructs were assessed.
RESULTS: The analyses confirmed the positive correlation of rational factors to the Intention to Use. Subjective Norm, intended as the physicians\' influence, is positively correlated with the Perceived Usefulness. Trend Arrows, albeit being negatively correlated with Perceived Usefulness, have a positive correlation on Perceived Ease Of Use, reinforcing its mediating effect towards Perceived Usefulness. Among psychological factors, Trust in the CGM technology positively correlates with Intention to Use. Health Literacy is negatively correlated to the Intention to Use.
CONCLUSIONS: These findings contribute to theoretical and managerial understanding, providing recommendations to enhance the adoption of CGM systems like Dexcom ONE.

Pancreatic diabetes is associated with glycemic variability, poor metabolic control, and reduced quality of life. Though hybrid closed-loop (HCL) insulin delivery systems were not originally developed for these types of diabetes, they could address the therapeutic challenge. We aimed to evaluate long-term metabolic control in ten adult patients (mean ± SD age: 59 ± 12) treated with HCL insulin delivery systems for pancreatitis or pancreatectomy-induced diabetes. After a median of 346 days (range 64 - 631) with HCL insulin delivery, continuous glucose monitoring showed 59±19 % time-in-range [70-180 mg/dl] (versus 49±24 % before HCL insulin delivery, P = 0. 049) and 0.8 ± 1.0 % time-below-range [< 70 mg/dl] (versus 2.2 ± 2.6 %, P = 0.142), with the coefficient of glucose variability at 35.4 ± 7.6 (versus 37.8 ± 7.1, P = 0.047). HbA1c decreased from 8.5 ± 1.7 % to 7.7 ± 1.3 % [69±18 to 60±14 mmol/mol] (P = 0.076). No patient experienced an acute adverse metabolic event.

Maturity-onset diabetes of the young (MODY) is a grouping of monogenic disorders. It is characterized by dominantly inherited, non-insulin-dependent diabetes. MODY is relatively rare, encompassing up to 3.5% in those diagnosed under 30 years of age. Specific types are most commonly treated with sulfonylurea, particularly those identified as HNF4A-MODY and HNF1A-MODY. HNF1B-MODY is another type that is most frequently managed with insulin therapy but lacks a defined precision treatment. We present an 18-year-old, non-obese female patient diagnosed with HNF1B-MODY. She displays complete gene deletion, a renal cyst, and hypomagnesemia. Her treatment plan includes both long- and short-acting insulin, though she frequently encountered hypoglycemia and hyperglycemia. Semaglutide, a GLP-1RA, was administered weekly over 4 months. The patient\'s glucose level was continuously tracked using Dexcom\'s Continuous Glucose Monitoring system. The data suggested a notable improvement in her condition: time-in-range (TIR) increased from 70% to 88%, with some days achieving 100%, and the frequency of hypoglycemic episodes, indicated by time-below-range values, fell from 5% to 1%. The time-above-range values also dropped from 25% to 10%, and her HbA1c levels declined from 7% to 5.6%. During the semaglutide therapy, we were able to discontinue her insulin treatment. Additionally, her body mass index (BMI) was reduced from 24.1 to 20.1 kg/m2. However, the semaglutide treatment was halted after 4 months due to side effects such as nausea, vomiting, and reduced appetite. Other contributing factors included exam stress and a COVID-19 infection, which forced a switch back to insulin. Her last recorded HbA1c level under exclusive insulin therapy rose to 7.1%, and her BMI increased to 24.9 kg/m2. In conclusion, semaglutide could potentially replace insulin to improve glucose variability, TIR, and HbA1c in patients with HNF1B-MODY. However, more extensive studies are required to confirm its long-term safety and efficacy.

Catamenial hyperglycemia is a rare type of spontaneous, recurring Diabetic Ketoacidosis(DKA) in females during the luteal phase, most commonly observed in type 1 diabetes mellitus. Even with controlled serum glucose levels, adherence to a diabetic diet, medications, and in the absence of other common influencing factors such as infection, glucose levels tend to increase during the premenstrual period. This uncommon issue related to the menstrual cycle phase has not been extensively researched. Therefore, this study aims to diagnose catamenial hyperglycemia promptly and initiate early treatment to prevent complications.
We presented a case of a 19-year-old girl who experienced recurrent DKA during the premenstrual period, without an apparent cause. She was admitted multiple times to various hospitals and sought consultations, undergoing numerous laboratory and imaging examinations, yet the etiology remained elusive. Ultimately, she received a diagnosis of catamenial diabetic hyperglycemia. To prevent recurrence of complications associated with catamenial hyperglycemia, we initiated a comprehensive approach which included continuous glucose monitoring, adherence to a strict diabetic diet, diabetic health education, regular exercise, timely medication administration, and increase in insulin dosage during the premenstrual period based on glucose levels.
Although catamenial hyperglycemia is rare, it should be considered a cause of recurrent hyperglycemia in any postpubertal female to prevent complications. The specific underlying mechanisms responsible for catamenial hyperglycemia or DKA remain unidentified.

Type 1 diabetes mellitus is characterized by an absolute insulin deficiency requiring the lifetime intensive insulin therapy accompanied by daily self-monitoring, self-management, ongoing education, and complex diabetes care. Regular patient-clinician shared therapeutic decisions based on age, sex, comorbidities, medications, predicted impact of meals, physical activity, stress, hormonal changes, insulin therapy, and patterns of glycemic changes are key for achieving glycemic targets. The impact of various phases of bipolar disorder and their treatment on continuous glucose levels remains unexplored and calls for future assessments.
The present case reports a 41-year-old Caucasian female with an established diagnosis of bipolar II disorder and type 1 diabetes mellitus who discontinued long-term mood-stabilizing pharmacotherapy with quetiapine. Real-time continuous glucose monitoring performed before and 6-months following the discontinuation of quetiapine revealed hidden glucose patterns in medicated versus unmedicated bipolar disorder. Despite the known adverse metabolic effects of quetiapine, the continuous glucose monitoring captured more stable and near-normal continuous glucose values during the antipsychotic treatment compared to unmedicated stages of bipolar disorder with considerably higher glucose values and glucose variability.
The case report highlights the importance of the ongoing psychopharmacotherapy of bipolar disorder in comorbid type 1 diabetes mellitus to reduce mood-induced reactivity, emotional urgency, and non-emotional impulsivity that may contribute to dysglycemia. If not effectively treated, the \"bipolar diabetes\" is likely to progress to multiple psychiatric and somatic complications. The bidirectional links between the phases of bipolar disorder and the corresponding continuous glucose patterns can help advance clinical decision-making and yield innovative1 research that can translate into efficacious clinical practice.

BACKGROUND: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.
METHODS: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.
RESULTS: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.
CONCLUSIONS: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

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BACKGROUND: Acute severe hypoglycemia immediately following anesthesia induction is a rare but life-threatening complication that is frequently underdiagnosed due to insufficient awareness. Among the various physiological processes influenced by opioids, alterations in blood glucose levels induced by opioids are a side effect that is commonly overlooked. The significance of this report lies in emphasizing the neglected association between opioids and hypoglycemia and highlighting the importance of close glucose monitoring to prevent hypoglycemic events in the perioperative setting.
METHODS: An 89-year-old man with type 2 diabetes mellitus experienced acute severe hypoglycemic episode immediately after general anesthesia induction. Baseline blood glucose level before starting anesthesia induction was 4.0 mmol/L. However, it decreased substantially to 0.96 mmol/L immediately after anesthesia induction.
METHODS: The patient exhibited normal serum insulin, C-peptide, and cortisol levels, alongside unremarkable renal and hepatic function. After excluding other causes of hypoglycemia, we speculate that opioids were the culprits due to the temporal association and the rapid decline in blood glucose levels.
METHODS: Forty milliliters of 50% dextrose were administered intravenously followed by an infusion of 5% dextrose.
RESULTS: Recovery from anesthesia, extubation, and postoperative recovery were unremarkable. No further hypoglycemic episodes occurred during hospitalization.
CONCLUSIONS: A precipitous and rapid decline in blood glucose following anesthesia induction is extremely uncommon. When a clinical anesthesiologist detects an abnormally low bispectral index during general anesthesia, hypoglycemia should be suspected. Instituting glucose monitoring in these situations can enable a timely diagnosis, forestalling the onset of life-threatening severe hypoglycemia.

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