In this three-year retrospective study, data from 51 patients with type 1 or type 2 diabetes mellitus (DM), receiving a minimum of 3-4 insulin injections per day and self-monitoring their blood glucose (SMBG) four times a day, were derived from our internal medicine residency primary care clinic. The patients were equipped with a continuous glucose monitoring (CGM) device that shared 24-hour glucose data with the clinic. They were assigned to members of our CGM team, which included internal medicine or transitional year medical residents who functioned under the supervision of a board-certified endocrinologist. The residents, in consultation with our endocrinologist, assessed the patients\' glucose management data and adjusted their treatment regimens biweekly by calling the patients, and monthly by seeing the patients in the clinic. Significant results from the study include a reduction in HbA1c from 9.9% to 7.6%, an average blood glucose decrement from 242 mg/dL to 169 mg/dL, a reduction in the incidence of mild hypoglycemia from below 70 mg/dL to 54 mg/dL, from 4.68% to 0.76% per day, and a more pronounced hypoglycemia with glucose less than 54 mg/dL from 3.1% per day to 0.2% per day. We observed a significant increase in the time in the range of the blood glucose from 33% to 67% per day. Furthermore, 9.5% of the patients in this study eventually discontinued their daily insulin injections and continued treatment with oral diabetic medications with or without the use of injectable GLP-1 receptors once a week. Our study affirms that CGM devices significantly improve glycemic control compared to SMBG, supporting its efficacy in optimizing glycemic control in real-world clinical practice. The results imply that this can be accomplished in internal medicine residency clinics and not exclusively in specialized endocrine clinics. As far as we know, this is the first study of its kind in a residency clinic in the USA. This study confirms the benefits of widening the application of CGM in DM, along with the challenges that must be overcome to realize the evidence-based benefits of this technology. CGM needs to become a part of routine monitoring for type 1 and type 2 DM.

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes.

UNASSIGNED: After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type 2 diabetes(T2D).
UNASSIGNED: 63 T2D patients complicated with COVID-19 were evaluated. Patients were divided into favorable outcome group and adverse outcome group according to whether achieving composite endpoint (a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death). TIR, the time-below-range (TBR) and the time-above-range (TAR) were calculated from intermittently scanned continuous glucose monitoring. Logistic regression analysis and other statistical methods were used to analyze the correlation between glucose variability and prognosis to establish the appropriate reference range of TIR.
UNASSIGNED: TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes.
UNASSIGNED: The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with T2D.

UNASSIGNED: The nutritional status of the patients before critical illness and nutrition support given during the critical illness play an important role in the recovery. We aimed to evaluate the nutritional prescription and its effect on ICU mortality.
UNASSIGNED: This was a prospective observational study conducted after institutional ethical committee approval (IEC 94/2018, CTRI/2018/06/014625) in a case-mixed (medical and surgical) ICU. Patients admitted to the ICU were enrolled within 24 hours of admission. The amount of calories and proteins prescribed and received by the patients was collected for 7 days. The primary outcome was ICU mortality.
UNASSIGNED: A total of 100 patients were included. The mean age was 48.63 (16.25) years, and 62% were males. The acute physiology and chronic health evaluation (APACHE II), sequential organ failure assessment (SOFA), and modified Nutric (mNUTRIC) scores were comparable between the two groups. The ICU mortality was 30%. The calorie and protein deficits were comparable between survivors and non-survivors. Among the secondary outcomes, a significant time effect (p = 0.013) and interaction effect (p = 0.004) were noted for maximum glucose levels. The glucose variability calculated by coefficient of variation (CV) was significantly higher in non-survivors than survivors (p = 0.031).
UNASSIGNED: The calorie and protein deficits did not affect ICU mortality. The maximum glucose variability and CV were significant parameters associated with ICU mortality.
UNASSIGNED: Havaldar AA, Selvam S. Nutritional Prescription in ICU Patients: Does it Matter? Indian J Crit Care Med 2024;28(7):657-661.

The efficacy of glucagon-like peptide-1 receptor agonists (GLP1-RA) in type 2 diabetes mellitus is well-established. GLP1-RAs are not approved for use in type 1 diabetes mellitus (T1DM). A 34-year-old woman with a 23-year history of T1DM presented for review for weight gain (weight 63 kg, BMI 26.9 kg/m2) and increased HbA1c (8.3%) and glycemic variability. Subcutaneous semaglutide (1 mg weekly) was commenced. After two months, there was decrease in weight by 12 kg, body fat percent by 15%, visceral fat by 7%, and a reduction in insulin dose, glycemic variability, and HbA1c. Semaglutide could be an important adjunct to insulin treatment in T1DM.

Introduction: Hypoglycemia has been associated with cardiovascular events, and glucose variability has been suggested to be associated with increased cardiovascular risk. Therefore, in this study, we examined the effect on proteomic cardiovascular risk protein markers of (i) mild iatrogenic hypoglycemia and (ii) severe iatrogenic hypoglycemia followed by rebound hyperglycemia. Methods: Two iatrogenic hypoglycemia studies were compared; firstly, mild hypoglycemia in 18 subjects (10 type 2 diabetes (T2D), 8 controls; blood glucose to 2.8 mmoL/L (50 mg/dL) for 1 h), and secondly, severe hypoglycemia in 46 subjects (23 T2D, 23 controls; blood glucose to <2.2 mmoL/L (<40 mg/dL) transiently followed by intravenous glucose reversal giving rebound hyperglycemia). A SOMAscan assay was used to measure 54 of the 92 cardiovascular protein biomarkers that reflect biomarkers involved in inflammation, cellular metabolic processes, cell adhesion, and immune response and complement activation. Results: Baseline to euglycemia showed no change in any of the proteins measured in the T2D cohort. With severe hypoglycemia, the study controls showed an increase in Angiopoietin 1 (ANGPT1) (p < 0.01) and Dickkopf-1 (DKK1) (p < 0.01), but no changes were seen with mild hypoglycemia. In both the mild and severe hypoglycemia studies, at the point of hypoglycemia, T2D subjects showed suppression of Brother of CDO (BOC) (p < 0.01). At 1 h post-hypoglycemia, the changes in ANGPT1, DKK1, and BOC had resolved, with no additional protein biomarker changes despite rebound hyperglycemia from 1.8 ± 0.1 to 12.2 ± 2.0 mmol/L. Conclusions: Proteomic biomarkers of cardiovascular disease showed changes at hypoglycemia that resolved within 1 h following the hypoglycemic event and with no changes following hyperglycemia rebound, suggesting that any cardiovascular risk increase is due to the hypoglycemia and not due to glucose fluctuation per se.

暂无摘要。

UNASSIGNED: A variety of metrics are used to describe glycemic variation, some of which may be difficult to comprehend or require complex strategies for smoothing of the glucose curve. We aimed to describe a new metric named time with rapid change of glucose (TRC), which is presented as percentage of time, similar to time above range (TAR), time in range (TIR), and time below range (TBR).
UNASSIGNED: We downloaded glucose data for 90 days from 159 persons with type 1 diabetes using the Abbott Freestyle Libre version 1. We defined TRC as the proportion of time (%) with an absolute rate of change of glucose > 1.5 mmol/L/15 minutes (1.8mg/dL/min) corresponding to a minimum rate of change for glucose in the 3.9-10.0 mmol/L (70-180 mg/dL) range within 1 hour. TRC is related to the other glucose variability metrics: CV within day (CVw) and mean amplitude of glycemic excursion (MAGE).
UNASSIGNED: The more than 1.27 million glucose rates were t-location scale distributed with SD 0.91 mmol/L/15 min (1.1 mg/dL/15 min). The median TRC was 6.9% (IQR 4.5%-9.5%). The proportion of TRC with positive slope was 3.9% (2.6%-5.3%) and significantly higher than the proportion with negative slope 2.8% (1.5%-4.4%) P < .001. TRC correlated with CVw and MAGE (Spearman\'s correlation coefficient .56 and .65, respectively, P < .001).
UNASSIGNED: TRC is proposed as an easily perceived metric to compare the performance of hybrid or fully automated closed-loop insulin delivery systems to obtain glucose homeostasis.

OBJECTIVE: To analyze the time in tight range (TITR), and its relationship with other glucometric parameters in patients with type 1 diabetes (T1D) treated with advanced hybrid closed-loop (AHCL) systems.
METHODS: A prospective observational study was conducted on pediatric and adult patients with T1D undergoing treatment with AHCL systems for at least 3 months. Clinical variables and glucometric parameters before and after AHCL initiation were collected.
RESULTS: A total of 117 patients were evaluated. Comparison of metabolic control after AHCL initiation showed significant improvements in HbA1c (6.9 ± 0.9 vs. 6.6 ± 0.5%, p < 0.001), time in range (TIR) (68.2 ± 11.5 vs. 82.5 ± 6.9%, p < 0.001), TITR (43.7 ± 10.8 vs. 57.3 ± 9.7%, p < 0.001), glucose management indicator (GMI) (6.9 ± 0.4 vs. 6.6 ± 0.3%, p < 0.001), time below range (TBR) 70-54 mg/dl (4.3 ± 4.5 vs. 2.0 ± 1.4%, p < 0.001), and time above range (TAR) > 180 mg/dl (36.0 ± 7.6 vs. 15.1 ± 6.4%, p < 0.001). Coefficient of variation (CV) also improved (36.3 ± 5.7 vs. 30.6 ± 3.7, p < 0.001), while time between 140-180 mg/dl remained unchanged. In total, 76.3% achieved TITR > 50% (100% pediatric). Correlation analysis between TITR and TIR and GRI showed a strong positive correlation, modified by glycemic variability.
CONCLUSIONS: AHCL systems achieve significant improvements in metabolic control (TIR > 70% in 93.9% patients). The increase in TIR was not related to an increase in TIR 140-180 mg/dl. Despite being closely related to TIR, TITR allows for a more adequate discrimination of the achieved control level, especially in a population with good initial metabolic control. The correlation between TIR and TITR is directly influenced by the degree of glycemic variability.

UNASSIGNED: This study aimed to investigate the glycometabolism, fat mass, and lean mass in primary aldosteronism (PA) during disease progression.
UNASSIGNED: Patients diagnosed with PA and healthy controls (HCs) were enrolled. A flash glucose monitoring system (FGMS) and dual-energy X-ray absorptiometry (DEXA) were used to measure glucose variability and glucose target rate along with fat mass and lean mass. Comparative analysis of FGMS- or DEXA-derived parameters along with correlation analyses between these parameters and PA progression were performed.
UNASSIGNED: Increased glucose variability and poor glucose target rate, along with an increased proportion of truncal fat mass, and decreased proportion of appendicular lean mass, were identified in PA group compared to those in HCs. Plasma aldosterone concentration was positively correlated with glucose variability and poor glucose target rate. Plasma renin concentration was positively correlated with the proportion of truncal fat mass and lean mass, and negatively correlated with the proportion of appendicular fat mass. Aldosterone-to-renin ratio was negatively correlated with the proportion of truncal fat mass and lean mass, and positively correlated with the proportion of appendicular fat mass.
UNASSIGNED: Patients with PA presented significant differences in glycometabolism, fat mass, and lean mass compared with HCs, and these alterations correlated with PA progression.