High Glycemic Variability Research Articles

Early use of Hybrid Closed-loop following Total Pancreaticoduodenectomy.

Diabetes secondary to total pancreaticoduodenectomy (TP) is challenging to manage due to high glycemic variability and risk of hypoglycemia, in a frail population. We report the case of four patients with no prior diabetes who underwent TP. Three of four patients needed artificial nutritional support. Hybrid closed-loop (HCL) insulin therapy was initiated within 12 weeks of surgery. After 90 days of HCL treatment, continuous glucose measurement showed a 70.4 ± 11.8% time in range (versus 43 ± 6.5% before HCL); 0.2 ± 0.2% time below range (versus 0.6 ± 0.5% before HCL); 23.8 ± 9.1% time above range 180-250 mg/dl (versus 22.9 ± 6.1% before HCL); 4.2 ± 2.5% time above range > 250 mg/dl (versus 33.8 ± 3.9% before HCL). The glucose management indicator improved from 8.5 ± 0.6% to 6.9 ± 0.6%. There was no severe hypoglycemia or need for unplanned medical attention. Early post-operative use of HCL allowed our patients to achieve safely optimal glycemic control after TP.

Acute kidney disease and postoperative glycemia variability in patients undergoing cardiac surgery: A multicenter cohort analysis of 8,090 patients

BackgroundNo previous research has specifically investigated the relationship between postoperative glycemic variability (GV) and acute kidney disease (AKD) in patients undergoing cardiac surgery. In this study, several methods of modelling postoperative GV were used to examine the association between GV and AKD risk and subtypes of AKD. MethodsWe undertook a retrospective study involving a total of 8,090 adult patients from three academic medical centers in Eastern China who underwent cardiac surgery with cardiopulmonary bypass between 2015 and 2023. Seven-day postoperative GV was calculated using the standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), average daily risk range (ADRR), and time out of target range (TOR). The primary focus was on the occurrence of AKD between 8 and 90 days post-surgery, which was further categorized into persistent AKD and delayed AKD depending on the acute kidney injury (AKI) status in the first 7 days. ResultsDuring the 8-90 days postoperative period, AKD occurred in 522 out of 8,090 patients (6.5%). Seven-day postoperative GV was significantly and consistently higher in the AKD group (p<0.001 for each metric). After adjusting for relevant covariates, 7-day GV metrics were significantly associated with elevated AKD risk (standardized hazard ratio (SHR):1.20 (95% confidence interval (CI): (1.12 - 1.27) for SD; SHR: 1.30 (95% CI: 1.20 - 1.40) for TOR). GV was correlated with persistent AKD, while no statistically significant association was observed between GV and delayed AKD. Unique cutoff thresholds were calculated for each GV metric to provide a quantitative indicator of high GV, enhancing its practical utility. ConclusionsOur study highlights the association between postoperative GV and increased AKD risk, and identifies specific GV thresholds in adults undergoing cardiac surgery.

Short-term effects of brief stair climbing interruptions on postprandial hyperglycemia during prolonged sitting: a randomized cross-over trial

Prolonged sitting can negatively impact postprandial glucose levels and cognitive function. While short bouts of stair climbing are thought to mitigate these risks, the findings remain inconclusive. The present study aimed to explore the effects of stair climbing bouts on postprandial glucose and cognitive functions during prolonged sitting. Twenty-eight sedentary young adults (aged 20–30 years) underwent two intervention visits after standardised lunch for two hours: (1) STAIR: the participants climbed two flight of stairs for two minutes every 30 min; (2) SIT: the participants continued to sit. Blood glucose was measured using capillary finger prick method while attention function was measured using computer-based cognitive tests at baseline, end of 1st hour and 2nd hour. Significant interaction (F2, 54 = 15.96, p < 0.001) was observed for conditions and time. During STAIR visit, significant changes in postprandial glucose at 1st hour (β = − 2.6 mmol/dl, p < 0.001) and 2nd hour (β = 3.0 mmol/dl, p < 0.001). No significant difference in the attention functions with time and conditions was observed. Stair climbing interruptions may serve as a feasible and effective countermeasure to high glycaemic variability or excursions that occur during prolonged sitting after postprandial hyperglycaemia.

Multidimensional Sleep Health, Glycemic Control, and Self-Reported Outcomes in Type 1 Diabetes: A Cross-Sectional Study.

Sleep health is multidimensional. While studies have shown associations between certain sleep dimensions and health in type 1 diabetes (T1D), global sleep health has rarely been considered. To examine the associations between individual sleep dimensions and multidimensional sleep health (MSH) on glycemic control and self-reported outcomes in T1D. Data from 116 adults with T1D participating in a sleep study (NCT04506151) were analyzed. Sleep satisfaction and alertness were assessed by questionnaires. Sleep timing, efficiency, duration, and regularity were derived from 7-day actigraphy. A composite MSH score was created by counting "healthy sleep" across these six measures. Glycemic control was assessed by 7-day continuous glucose monitoring and A1C. Self-reported outcomes were collected through questionnaires. After adjusting for covariates, greater sleep irregularity was associated with higher glycemic variability (b=5.048, p<0.01), less time in range (TIR) (b=-10.806, p<0.01), higher time above range (TAR) (b=7.40, p<0.05), and higher A1C (b=0.365, p<0.05)]. Poor sleep satisfaction was associated with higher diabetes distress and depression (b=0.29, p<0.05, b=3.59, p<0.05), respectively. Later sleep timing was associated with higher depression (b=1.545, p<0.05), while lower sleep efficiency was associated with higher depression (b=1.545, p<0.01). Worse MSH was significantly associated with lower TIR (b=2.376, p<0.05), higher TAR, A1C, and depression (b=-2.38, p<0.05; b=-0.177, p<0.01; b=-1.275, p<0.05, respectively). Sleep irregularity likely drives the association between MSH and glycemic control, while poor sleep satisfaction, lower efficiency, and later timing contribute to the association between MSH and depression. These results highlight the importance of comprehensive sleep health evaluation in T1D.

Association of glycemic variability with short and long-term mortality among critically ill patients with heart failure: Analysis of the MIMIC-IV database.

High glycemic variability (GV) often indicates a poor prognosis. Our aim is to investigate the relationship between GV and short and long-term mortality in critically ill heart failure (HF) patients. We extracted data from the Medical Information Mart for Intensive Care IV database. The risks of in-hospital and 1-year mortality were calculated using Logistic and COX regression. In addition, the method of mediation analysis was used to analyze the indirect effect of ventricular arrhythmias (VA) on in-hospital mortality. Among 8,980 critically ill HF patients, the multifactorial regression equation showed that high GV was associated with an increased risk of in-hospital and 1-year mortality (OR 1.69, 95 % CI 1.47-1.93; HR 1.12, 95 % CI 1.02-1.22). The Kaplan-Meier survival curve and restricted cubic spline plot also emphasized this association. Furthermore, the impact of GV on in-hospital mortality was partially mediated by VA. And the increased risk of 1-year mortality associated with high GV was more significant in person with diabetes. Our Study indicates that high GV may be an independent risk factor for short and long-term mortality in critically ill HF patients. Maintaining the stability of blood glucose can reduce adverse outcomes in critically ill HF patients.

Relationship between glycemic variability and the incidence of postoperative atrial fibrillation following cardiac Surgery: A retrospective study from MIMIC-IV database.

This study aimed to explore the association between glycemic variability (GV) and postoperative atrial fibrillation (POAF) incidence. In this retrospective study, we included patients undergoing cardiac surgeries. GV was calculated as the coefficient of variation of blood glucose and categorized into tertiles based on its distribution. The primary endpoint was the incidence of POAF. Logistic regression and restricted cubic splines were used to assess the relationship between GV and POAF. 5365 patients were included, with a median age of 68.3 years, and 25.5 % were female. 1056 (19.7 %) patients developed new-onset POAF. Compared with the low GV group, moderate GV group (odds ratio [OR], 1.82; 95 % confidence interval [CI]: 1.49-2.22) and high GV group (OR, 2.25; 95 % CI, 1.80-2.82) were significantly associated with a higher incidence of POAF. The area under the receiver operating characteristic curve of GV in predicting POAF incidence was 0.77 (95 % CI: 0.76-0.79). There is a threshold-based nonlinear relationship between GV and the incidence of POAF: when GV was < 24 %, the likelihood of POAF increases with higher GV, whereas when GV ≥ 24 %, further increases did not significantly affect the risk of POAF. Increased GV is associated with higher incidence of POAF.

Associations of plasma biomarkers of neurodegeneration with characteristics related to type 1 diabetes

AbstractBackgroundCognitive dysfunction is more common in individuals with type 1 diabetes (T1D) compared to the general population. Blood‐based biomarkers are accurate in identifying early signs of neurodegeneration. However, studies using these biomarkers in T1D are lacking. We examined the associations of plasma biomarkers and diabetes characteristics in adults with T1D.MethodsThis study included 114 adults with T1D from the Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog) study. Plasma biomarkers were: ratio of amyloid‐β (Aβ) 42/40, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated Tau181 (pTau181) using Quanterix assays, and phosphorylated Tau 217 (pTau217) using ALZpath. The coefficient of variation (%) from continuous glucose monitor readings was used to define glycemic variability (GV). Relationships between biomarkers, demographics, and diabetes characteristics were evaluated using Spearman rank correlation, Mann‐Whitney U, and Kruskal‐Wallis tests, as appropriate. Linear regression models were used to examine the associations between plasma biomarkers (dependent variable) and diabetes characteristics (that were significant in bivariate analyses) as predictors in separate models adjusted for sex, age, and education. We added the adjustment for nephropathy in models in which the biomarker was associated with this variable.ResultsThe sample [53% female, mean(SD) age of T1D diagnosis 20.3(12.7) years, 37% with hypertension, 11% with neuropathy, 8% with nephropathy)] had a baseline age of 49.0(14.5) years, HbA1c 7.6%(1.3), GV 36.6%(7.3). Adjusted linear regression models revealed significant associations with four biomarkers. Presence of nephropathy was associated with higher concentrations of NfL, pTau181, and pTau217 and was included in the models with these biomarkers. A lower Aβ42/40 ratio was associated with older age at diagnosis. A higher concentration of NfL was associated with higher HbA1c. A higher concentration of pTau181 was associated with higher GV. Lastly, higher pTau217 was associated with neuropathy. GFAP was not associated with any of the diabetes measures.ConclusionThese data show that plasma biomarkers of neurodegeneration are associated with key diabetes characteristics in adults with T1D, particularly HbA1c, GV, neuropathy, and nephropathy. Further research is needed to understand whether T1D characteristics mediate biomarker associations with cognitive decline.

Prognostic effects of glycaemic variability on diastolic heart failure and type 2 diabetes mellitus: insights and 1-year mortality machine learning prediction model

BackgroundDiastolic heart failure (DHF) and type 2 diabetes mellitus (T2DM) often coexist, causing increased mortality rates. Glycaemic variability (GV) exacerbates cardiovascular complications, but its impact on outcomes in patients with DHF and T2DM remains unclear. This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients.MethodsPatients with DHF and T2DM were included from the Medical Information Mart for Intensive Care IV, with admissions (2008–2019) as primary analysis cohort and admissions (2020–2022) as external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to evaluate the associations of GV with 90-day, 1-year, and 3-year all-cause mortality. The primary analysis cohort was split into training and internal validation cohorts, then developing ML models for predicting 1-year all-cause mortality in training cohort, which were validated using the internal and external validation cohorts.Results2,128 patients with DHF and T2DM were included in primary analysis cohort (meidian age 71.0years [IQR: 62.0–79.0]; 46.9% male), 498 patients with DHF and T2DM were included in the external validation cohort (meidian age 75.0years [IQR: 67.0–81.0]; 54.0% male). Multivariate Cox proportional hazards models showed that high GV tertiles were associated with higher risk of 90-day (T2: HR 1.45, 95%CI 1.09–1.93; T3: HR 1.96, 95%CI 1.48–2.60), 1-year (T2: HR 1.25, 95%CI 1.02–1.53; T3: HR 1.54, 95%CI 1.26–1.89), and 3-year (T2: HR 1.31, 95%CI: 1.10–1.56; T3: HR 1.48, 95%CI 1.23–1.77) all-cause mortality, compared with lowest GV tertile. Chronic kidney disease, creatinine, potassium, haemoglobin, and white blood cell were identified as mediators of GV and 1-year all-cause mortality. Additionally, GV and other clinical features were pre-selected to construct ML models. The random forest model performed best, with AUC (0.770) and G-mean (0.591) in internal validation, with AUC (0.753) and G-mean (0.599) in external validation.ConclusionGV was determined as an independent risk factor for short-term and long-term all-cause mortality in patients with DHF and T2DM, with a potential intervention threshold around 25.0%. The ML model incorporating GV demonstrated strong predictive performance for 1-year all-cause mortality, highlighting its importance in early risk stratification management of these patients.

Abstract 4138815: Prognostic Implications of Glycemic Variability Assessed by Continuous Glucose Monitoring-derived Coefficient of Variation among Patients Hospitalized for Acute Heart Failure

Background: Recent findings indicate that high glycemic variability (GV) could increase the risk of adverse outcomes in patients with cardiovascular disease. With the advances in technology, the utility of continuous glucose monitoring (CGM) has grown during recent years, which can provide valuable information that is not captured by HbA1c. We used the glucose coefficient of variation (CV, calculated as the ratio of standard deviation divided by the mean glucose level) of the obtained CGM data to analyze GV. Aims: We aimed to analyze CGM-derived CV as an indicator of GV and to evaluate prognostic implications of CGM-derived CV in patients hospitalized for acute heart failure (HF). Methods: Patients hospitalized for HF who had not been diagnosed diabetes were performed 75g-oral glucose tolerance test (OGTT). CV was evaluated using CGM system for at least 72 consecutive hours after being able to have meals stably during hospitalization. The primary outcome was a composite of cardiovascular death or re-hospitalization for HF. Patients were divided into two groups based on the median value of CV. Results: We prospectively analyzed 130 patients (mean age: 76±10 years, male 56%) admitted for acute HF. Following the 75g-OGTT, only 21% of the entire cohort exhibited normal glucose tolerance. During the median follow up period of 905 days, Kaplan–Meier curve analysis revealed that the cumulative incidence of the primary outcome event was significantly higher in patients with CV above median than in those with CV less than median (51.7% versus 24.4%, Log-rank P=0.01)(Figure). Multivariate Cox proportional analysis demonstrated that the risk of CV per 1% increment for the primary outcome was significant adjusted for history of diabetes, CGM-derived mean glucose level, and HbA1c level (hazard ratio: 1.06, 95% confidence interval 1.03–1.09). Conclusions: CGM-derived CV was associated with long term prognosis of patients hospitalized for HF with or without diabetes.

HFpEF and glycemic variability: GHIDD Trial

Abstract Background Recently the guidelines of European Association of Preventive Cardiology reported that the prevalence of diabetes mellitus in patients with diastolic heart failure (HFpEF) is around 31%. Glunovo® is a transdermal device capable of detecting and storing the concentration of glucose in the abdominal interstitial fluid 480 times a day for up to 14 consecutive days. Purpose In our study we evaluated the non diabetic patients with HFpEF with Glunovo to evaluate the glycaemic variability in this population Methods 100 patients with HFpEF admitted to cardiology units of 4 Italian centres on the major islands were enrolled consecutively. Glunovo® was applied to each enrolled patient for 7 days. At the end of the glycaemic monitoring were calculated for each patient the glycaemic variability and the incidence of hyperglycaemia and hypoglycaemia. Glycaemic variability refers to a blood glucose value of ≥ 200 mg / dL or ≤ 59 mg / dL detected more than 3 times in a day for at least 4 days. The inclusion criterion was the presence of diastolic heart failure while the only exclusion criterion was the presence of diabetes diagnosis. Overall, 43 males and 57 females were enrolled with a mean age of 69.3 years (39-87 years). All patients underwent a timely glucose measurement at admission which excluded the presence of hyperglycaemia. No potentially hyperglycaemic drugs were added to the treatment during the hospital stay. Continuous glucose monitoring was performed as an integral part of the hospitalization diagnostic routine. Results In 94 of the 100 patients enrolled it was possible to conclude the analysis, detecting the glycaemic variability, the point glycaemia values and the estimated glycated haemoglobin value. A glycaemia ≥200 mg/dL was found in 53 patients (56%) while a high glycaemic variability was found in 51 patients (54%). A blood glucose value &amp;lt;59 mg / dl was found in 48 patients (51%). Only 5 times the estimated glycated Hb values were&amp;gt; 7%. 32 of the patients who had at least 3 punctual glucose values ≥200 mg/dL were prescribed an oral glucose load curve, which in 100% of cases confirmed the diagnosis of diabetes.No statistically significant differences were found based on age group or sex. In the control group, consisting of 10 patients without DHF undergoing continuous glucose monitoring at one of the participating cardiology units, an unknown hyperglycaemia was found in only 1 patient (10%) and a glycaemic variability in only 1 patient (10%). Conclusions Our experience suggests an incidence of hyperglycaemia and glycaemic variability more then 65% in patients affected by HFpEF. If our data were reproducible on a large scale, a so high prevalence of diabetes in patients with HFpEF cold explain the efficacy of SGLT-2 inhibitor and GLP-1 Agonist in this class of patients.

Risk factors for Relative and Absolute Hypoglycemia in Patients Treated for Diabetic Ketoacidosis and Hyperosmolar Hyperglycemic Syndrome.

Introduction: Glycemic management in the intensive care unit is an evolving practice area. This evolution has included the refinement of blood glucose targets, matching glycemic management to premorbid status, and investigations into the impact of glycemic variability and relative hypoglycemia on ICU outcomes. The interplay between these phenomena and absolute hypoglycemia has yet to be investigated in hyperglycemic emergencies. Objectives: To examine the incidence of and risk factors for relative hypoglycemia and absolute hypoglycemia in patients admitted to an intensive care unit for the management of hyperglycemic emergencies. Methods: This was a retrospective, single-center, exploratory analysis of adults admitted to the medical intensive care unit for diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome. The primary outcome was the incidence of relative hypoglycemia, defined as a blood glucose level 30% lower than baseline. The baseline was determined by the estimated average blood glucose calculated from hemoglobin A1c within 3 months of index admission. Secondary outcomes were ICU length of stay, glycemic variability, and incidence of absolute hypoglycemia.Results: Relative hypoglycemia was observed in 60% of patients in the cohort. Longer insulin infusion duration and higher hemoglobin A1c levels were found to statistically increase the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU length of stay were associated with the risk of developing absolute hypoglycemia. Conclusions: Relative hypoglycemia is a frequent occurrence in this patient population. Hemoglobin A1c and duration of the insulin infusion statistically influenced the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU stay were significantly associated with developing absolute hypoglycemia. While relative hypoglycemia is common in hyperglycemic emergencies, the clinical impact remains uncertain and warrants additional investigation.

High glycemic variability is associated with a reduced T cell cytokine response to influenza A virus

Diabetes mellitus significantly increases the risk of severe respiratory virus disease like influenza and COVID-19. Early evidence suggests that this susceptibility to respiratory viral disease is driven by glycemic variability, rather than average blood glucose levels. Here, we use blood samples and constant glucose monitoring (CGM) data obtained from people living with type 1 diabetes (T1D) to determine the effects of glycemic variability on the exvivo Tcell response to influenza virus. We show that high glycemic variability in participants living with T1D is associated with a reduced proportion of CD8+CD107a-IFNγ-MIP1β-TNF+ Tcells in response to stimulation with influenza virus and an influenza virus peptide pool. Thus, this study provides evidence that glycemic variability affects the exvivo Tcell response to respiratory viruses. These data suggest that monitoring glycemic variability may have important implications in understanding the antiviral immune response in people with diabetes.

Glycemic Variability and Disordered Eating Among Adolescents and Young Adults with Type 1 Diabetes: The Role of Disinhibited Eating.

Background and Aims: Disordered eating behaviors (DEB) are common among individuals with type 1 diabetes (T1D). Glycemic variability, potentially harmful in T1D, may reveal distinct characteristics between those with higher versus lower variability, particularly concerning DEB. Our aim was to evaluate the prevalence of DEB and associated risk factors among adolescents and young adults with T1D and to investigate unique factors associated with DEB across different levels of glycemic variability. Methods: An observational, cross-sectional study was conducted with 147 individuals with T1D, aged 13-21 years. Data were collected from medical charts, personal technological devices for assessing glycemic variability, and self-reported questionnaires, including assessments of DEB. Results: DEB were found in 62 (42.1%) individuals, and 41.5% achieved the glycemic variability (% coefficient of variation) target ≤36%. Among individuals with low glycemic variability, DEB were positively associated with diabetes distress (odds ratio [OR]: 1.14 [95% confidence interval or CI: 1.05-1.22], P < 0.001), longer diabetes duration (OR: 1.34 [95% CI: 1.05-1.70], P = 0.016) and lower socioeconomic-status (OR: 0.53 [95% CI: 0.31-0.90], P = 0.019). Among those with high glycemic variability, body mass index Z score (OR: 3.82 [95% CI: 1.48-9.85], P = 0.005), HbA1c (OR: 4.12 [95% CI: 1.33-12.80], P = 0.014), disinhibited eating (OR: 1.57 [95% CI: 1.14-2.15], P = 0.005), and tendency to lower socioeconomic status (OR: 0.75 [95% CI: 0.56-1.01], P = 0.065). Discussion: DEB are prevalent among adolescents and young adults with T1D and are associated with various risk factors. Factors associated with DEB vary across different levels of glycemic variability. Both low and high glycemic variability are associated with specific risk factors for DEB. One notable risk factor is diabetes-specific disinhibited eating among individuals with high glycemic variability, in contrast to those with low glycemic variability. Given these different risk factors, it may be prudent to adjust intervention programs to reduce DEB among T1D adolescents according to their glycemic variability levels.

Association Between Glycemic Variability and Persistent Acute Kidney Injury After Noncardiac Major Surgery: A Multicenter Retrospective Cohort Study.

While the relationship between glycemic variability (GV) and acute kidney injury (AKI) has been a subject of interest, the specific association of GV with persistent AKI beyond 48 hours postoperative after noncardiac surgery is not well-established. This retrospective cohort study aimed to describe the patterns of different GV metrics in the immediate 48 hours after noncardiac surgery, evaluate the association between GV indices and persistent AKI within the 7-day postoperative window, and compare the risk identification capabilities of various GV for persistent AKI. A total of 10,937 patients who underwent major noncardiac surgery across 3 medical centers in eastern China between January 2015 and September 2023 were enrolled. GV was characterized using the coefficient of variations (CV), mean amplitude of glycemic excursions (MAGE), and the blood glucose risk index (BGRI). Multivariable logistic regression was used to examine the relationship between GV and AKI. Optimal cutoff values for GV metrics were calculated through the risk identification models, and an independent cohort from the INformative Surgical Patient dataset for Innovative Research Environment (INSPIRE) database with 7714 eligible cases served to externally validate the risk identification capability. Overall, 274 (2.5%) of the 10,937 patients undergoing major noncardiac surgery met the criteria of persistent AKI. Higher GV was associated with an increased risk of persistent AKI (CV: odds ratio [OR] = 1.26, 95% confidence interval [CI], 1.08-1.46; MAGE: OR = 1.31, 95% CI, 1.15-1.49; BGRI: OR = 1.18, 95% CI, 1.08-1.29). Compared to models that did not consider glycemic factors, MAGE and BGRI independently contributed to predicting persistent AKI (MAGE: areas under the curve [AUC] = 0.768, P = .011; BGRI: AUC = 0.764, P = .014), with cutoff points of 3.78 for MAGE, and 3.02 for BGRI. The classification of both the internal and external validation cohorts using cutoffs demonstrated good performance, achieving the best AUC values of 0.768 for MAGE in the internal cohort and 0.777 for MAGE in the external cohort. GV measured within 48 hours postoperative period is an independent risk factor for persistent AKI in patients undergoing noncardiac surgery. Specific cutoff points can be used to stratify at-risk patients. These findings indicate that stabilizing GV may potentially mitigate adverse kidney outcomes after noncardiac surgery, highlighting the importance of glycemic control in the perioperative period.

Preoperative Carbohydrate Load Does Not Alter Glycemic Variability in Diabetic and Non-Diabetic Patients Undergoing Major Gynecological Surgery: A Retrospective Study.

Background/Objectives: Elevated glycemic variability (GV) has been associated with postoperative morbidity. Traditional preoperative fasting guidelines may contribute to high GV by driving the body into catabolism. Enhanced recovery after surgery (ERAS) protocols that include a preoperative carbohydrate load (PCL) reduce hospital length of stay and healthcare costs; however, it remains unclear whether PCL improves GV in surgical patients. The aim of this retrospective study was to determine the effect of a PCL on postoperative GV in diabetic and non-diabetic patients having gynecological surgery. Methods: Retrospective data were collected on patients who had gynecological surgery before and after the rollout of an institutional ERAS protocol that included PCL ingestion. The intervention group included patients who underwent surgery in 2019 and were enrolled in the ERAS protocol and, therefore, received a PCL. The control group included patients who underwent surgery in 2016 and, thus, were not enrolled in the protocol. The primary endpoint was GV, calculated by the coefficient of variance (CV) and glycemic lability index (GLI). Results: A total of 63 patients in the intervention group and 45 in the control were analyzed. GV was not statistically significant between the groups for CV (19.3% vs. 18.6%, p = 0.65) or GLI (0.58 vs. 0.54, p = 0.86). Postoperative pain scores (4.5 vs. 5.2 p = 0.23) and incentive spirometry measurements (1262 vs. 1245 p = 0.87) were not significantly different. A subgroup analysis of patients with and without type 2 diabetes mellitus revealed no significant differences in GV for any of the subgroups. Conclusions: This retrospective review highlights the need for additional GV research, including consensus agreement on a gold standard GV measurement. Large-scale prospective studies are needed to test the effectiveness of the PCL in reducing GV.

Glycemic variability in chronic calcific pancreatitis with diabetes mellitus and its possible determinants

AimsTo study glycemic patterns and variability in patients with pancreatic diabetes or type 3c Diabetes mellitus (DM) due to chronic pancreatitis and its subtypes and assess the role of pancreatic enzyme replacement therapy (ERT) in modulating glycemic variability. MethodsPatients having type 3c DM due to chronic pancreatitis, and on insulin underwent Flash continuous-glucose-monitoring (CGM) for 14 days. Parameters were compared between patients with fibrocalculous pancreatic diabetes (FCPD) and non-FCPD-chronic calcific pancreatitis (non-FCPD) and between the recipients and non-recipients of pancreatic enzyme-replacement-therapy (ERT). ResultsOut of 54 patients with pancreatic diabetes, 35 patients had chronic calcific pancreatitis. They underwent CGM, median HbA1c 9.20 % (77 mmol/mol) and mean Time-In-Range (TIR) being 41.21 % (23.48). Only 5 (15.2 %) patients achieved target TIR>70 % while 16 (48.5 %) patients had high glycemic-variability [Coefficient-of-variation (CV) > 36 %]. Patients with FCPD (n = 14) had higher hypoglycemia-indices like Time-Below-Range (18.92 % vs 8.20 %; p = 0.03) and Low-Blood-Glucose-Index (18.14 % vs 6.04 %; p = 0.02) compared to non-FCPD (n = 21). HbA1c% and hyperglycemic excursions were similar in both groups. Recipients of ERT (n = 20) had lower glycemic-variability [Standard Deviation (SD) 52.15 % vs 68.14 % and CV 32.59 % vs 41.79 %, p < 0.05 for both) than non-recipients. ERT-recipients had no serious hypoglycemia within the 14 days. On subgroup analysis, lower glycemic-variability and hypoglycemia with ERT were seen only in FCPD but not in non-FCPD subgroup (50.13 vs 77.91, 30.09 vs 48.36 for SD and CV respectively, p < 0.05). ConclusionPatients with type 3c DM due to chronic pancreatitis have high frequency of hyperglycemic and hypoglycemic excursions, with those with FCPD having a particularly higher risk of hypoglycemia and glycemic-variability. Those receiving pancreatic ERT had lesser glycemic variability and hypoglycemia. The small sample size and lack of objective markers of documentation of exocrine pancreatic insufficiency like fecal elastase highlight the need for further larger studies in this field.

Avaliação dos principais fatores de risco para alta variabilidade glicêmica em uma Unidade de Terapia Intensiva

Glycemic variability (GV) is an important evaluation parameter for cardiovascular complications. We aimed to identify factors associated with the risk of high glycemic variability in an Intensive Care Unit. In this prospective cohort with 168 adult patients, we first described the variables by absolute and relative frequency and then identified the risk factors for high GV by logistic regression within a 95% confidence interval. Of the 168 patients, 22.6% had high GV, 62.5% were male, 51.2% were under 40 years old, 52.4% had a clinical diagnosis, 73.8% were using mechanical ventilation, 12.3% had &gt; 30% mortality risk (Apache II), 17.9% had sepsis, 47.6% were hypertensive, and 28.0% of the patients died. In the final analysis, patients with sepsis (OR: 2.40; 95%CI: 1.10 – 5.94), over 40 years old (OR: 3.23; 95%CI 1.34-7.81) and who evolved to death (OR: 3.15; 95%CI 1.40-7.08) were those who had a greater chance of high GV. Patients with sepsis and those over 40 years old need greater surveillance of glycemic control to reduce mortality in the ICU.

Associations between time in range and insulin secretory capacity in Japanese patients with type 2 diabetes

Impaired insulin secretory capacity is associated with high glycemic variability in patients with type 2 diabetes (T2DM). However, there are no existing reports on the association between insulin secretory capacity and time in range (TIR). This retrospective study involved 330 T2DM admitted for diabetes education who underwent intermittently scanned continuous glucose monitoring (isCGM) and had their fasting serum C-peptide immunoreactivity (S-CPR) measured within 5 days of admission. The baseline characteristics were as follows: age, 60.2 years; glycated hemoglobin (HbA1c), 9.2%; S-CPR, 2.2 ng/mL; S-CPR index (S-CPR [ng/mL]/fasting plasma glucose [mg/dL] × 100), 1.6; and TIR, 60.3%. TIR correlated significantly with the S-CPR index, which was confirmed by multivariate analysis that included various factors such as HbA1c. Receiver operating characteristic (ROC) analysis showed that 1.88 was the optimal S-CPR index level to predict TIR ≥ 70%. In addition to HbA1c and biguanide use, the S-CPR index was a significant factor associated with TIR > 70%. S-CPR index values of ≥ 1.88 also correlated significantly with TIR > 70%. In conclusion, insulin secretory capacity is associated with TIR in Japanese T2DM, suggesting that the S-CPR index might be a potentially useful biomarker insulin secretory capacity, in association with TIR.Trial registration UMIN0000254333.

HFPEF AND GLYCEMIC VARIABILITY: GHIDD TRIAL

Abstract Background Recently the guidelines of European Association of Preventive Cardiology reported that the prevalence of diabetes mellitus in patients with diastolic heart failure (HFpEF) is around 31%. Purpose In our study we evaluated the non diabetic patients with HFpEF with Glunovo® to evaluate the glycaemic variability in this population. Methods 100 patients with HFpEF admitted to cardiology units of 4 Italian centres on the major islands were enrolled consecutively. Glunovo® was applied to each enrolled patient for 7 days, At the end of the glycaemic monitoring were calculated for each patient the glycaemic variability and the incidence of hyperglycaemia and hypoglycaemia. Glycaemic variability refers to a blood glucose value of ≥ 200 mg / dL or ≤ 59 mg / dL detected more than 3 times in a day for at least 4 days. Overall, 43 males and 57 females were enrolled with a mean age of 69.3 years (39–87 years). All patients underwent a timely glucose measurement at admission which excluded the presence of hyperglycaemia. No potentially hyperglycaemic drugs were added to the treatment during the hospital stay. Results In 94 of the 100 patients enrolled it was possible to conclude the analysis, detecting the glycaemic variability, the point glycaemia values and the estimated glycated haemoglobin value. A glycaemia ≥200 mg/dL was found in 53 patients (56%) while a high glycaemic variability was found in 51 patients (54%). A blood glucose value &amp;lt;59 mg / dl was found in 48 patients (51%). Only 5 times the estimated glycated Hb values were&amp;gt; 7%. 32 of the patients who had at least 3 punctual glucose values ≥200 mg/dL were prescribed an oral glucose load curve, which in 100% of cases confirmed the diagnosis of diabetes.No statistically significant differences were found based on age group or sex. In the control group, consisting of 10 patients without DHF undergoing continuous glucose monitoring at one of the participating cardiology units, an unknown hyperglycaemia was found in only 1 patient (10%) and a glycaemic variability in only 1 patient (10%). Conclusions Our experience suggests an incidence of hyperglycaemia and glycaemic variability more then 65% in patients affected by HFpEF. If our data were reproducible on a large scale, a so high prevalence of diabetes in patients with HFpEF cold explain the efficacy of SGLT–2 inhibitor and GLP–1 Agonist in this class of patients.

Model predictive control of blood glucose in critically ill patients using Gaussian processes

Abstract Stress-induced hyperglycemia and high glycemic variability are common in intensive care patients. Several clinical studies show the benefits of tight blood glucose control, including lower mortality. This article presents an algorithm for blood glucose control in the intensive care unit. An Unscented Kalman Filter is developed to estimate the glucose metabolism state and time-varying insulin sensitivity from blood glucose measurements. Gaussian Processes are used to predict future insulin sensitivity changes based on previous measurements. A model predictive controller is designed to estimate optimal insulin infusion based on current state, predicted insulin sensitivity and planned nutrition. The developed control algorithm allows individualized blood glucose control with reduced glycemic variability and reduced risk of hypoglycemia in the intensive care unit.