Dieses Positionspapier beinhaltet die Empfehlungen der Österreichischen Diabetes Gesellschaft zum Management von Patient*innen mit Diabetes mellitus während stationärer Aufenthalte und im perioperativen Setting, basierend auf aktueller Evidenz zu Glukosezielbereichen, Insulintherapie und Therapie mit oralen/injizierbaren Antidiabetika. Zusätzlich werden Spezialsituationen wie intravenöse Insulintherapie, begleitende Glukokortikoidtherapie sowie die Anwendung von Diabetestechnologie im stationären und perioperativen Bereich diskutiert.

Die Hyperglykämie ist wesentlich an der Entstehung der Folgeerkrankungen bei Menschen mit Diabetes mellitus Typ 2 beteiligt. Während Lebensstilmaßnahmen die Eckpfeiler jeder Diabetestherapie bleiben, benötigen die meisten Menschen mit Typ-2-Diabetes im Verlauf eine medikamentöse Therapie. Bei der Definition individueller Behandlungsziele stellen die Therapiesicherheit, die Effektivität sowie substanzspezifische, organprotektive Effekte der Therapie die wichtigsten Faktoren dar. Diese nationale Leitlinie fasst die Evidenz aus der aktuellen Datenlage für die klinische Praxis zusammen.

Epidemiological investigations have shown that approximately 2-3% of all Austrians have diabetes mellitus with renal involvement (diabetic kidney disease, DKD). Therefore, this concerns approximately 250,000 people in Austria. The risk of occurrence and progression of DKD can be attenuated by lifestyle interventions as well as optimization of blood pressure, blood glucose control and specific drug classes. These guidelines represent the joint recommendations of the Austrian Diabetes Association and the Austrian Society of Nephrology for the definition, diagnostics and treatment strategies of DKD.

Die in der Diabetes-Klassifikation als Typ-3-Diabetes beschriebenen Krankheitsentitäten umfassen andere spezifische Diabetesformen, welche pathophysiologisch und therapeutisch eine sehr heterogene Krankheitsgruppe darstellen. Diese beinhalten Formen, die aufgrund von genetischen Erkrankungen (monogenetische Formen, neonataler Diabetes, Down-Syndrom, Klinefelter-Syndrom, Turner-Syndrom) oder im Rahmen anderer endokrinologischer Erkrankungen auftreten (z. B. Akromegalie, Cushing-Syndrom, Glukagonom), sowie die an Häufigkeit und Komplexität zunehmenden medikamentös induzierten Diabetesformen (z. B. Therapie mit Glukokortikoiden, Immun-Checkpoint-Inhibitoren, Calcineurininhibitoren, Phosphatidylinositol-3-Kinase-Inhibitoren, hochaktive antiretrovirale Therapie [HAART], Antipsychotika). Ebenfalls in diese Diabeteskategorie fallen pankreoprive Formen (z. B. postoperativ, Zustand nach Pankreatitis, Pankreastumoren, Hämochromatose, zystische Fibrose), seltener infektionsgetriggerte (z. B. kongenitale Rötelninfektion) oder autoimmune Formen (z. B. Stiffman-Syndrom, Anti-Insulin-Rezeptor-Antikörper, Insulin-Autoimmunsyndrom). Die korrekte Zuordnung des Diabetestyps hat unmittelbare therapeutische Auswirkungen für die Betroffenen. Zusätzlich findet sich nicht nur bei pankreopriven Formen, sondern auch bei Diabetes mellitus Typ 1 oder langjährigem Typ-2-Diabetes mellitus häufig eine Assoziation mit einer exokrinen Pankreasinsuffizienz.

This position statement presents the recommendations of the Austrian Diabetes Association for the management of patients with diabetes during inpatient stay and in the perioperative setting. These recommendations are based on current evidence with respect to glucose targets, insulin therapy and treatment with oral/injectable antihyperglycemic agents during hospitalization and in perioperative setting. Additionally, it discusses special situations such as intravenous insulin therapy, concomitant therapy with glucocorticoids and use of diabetes technology during hospitalization and the perioperative setting.

Background: Long-term glycemic control in type 1 diabetes (T1D) varies substantially among affected individuals, but the role of baseline characteristics at diagnosis and their association with later glycemic control remain incompletely understood. Identifying early predictors of glycemic control may facilitate timely, individualized therapeutic interventions. Methods: We retrospectively analyzed electronic health records of individuals with newly diagnosed T1D between 2001 and 2022 to assess anthropometric and metabolic parameters at the first presentation of the condition across age groups and determine predictors of glycated hemoglobin (HbA1c) trajectories over 24 months. The multicentric cohort, which comprised people who were diagnosed with T1D in the Austrian federal state of Styria, was classified as children (<10 years), adolescents (10-18 years) or adults (≥18 years). Variables of interest included demographic and anthropometric data, positivity and titers of diabetes-specific autoantibodies, treatment setting (inpatient/outpatient), and presence and severity of diabetic ketoacidosis (DKA). Results: The cohort consisted of 281 individuals (23.1% were children, 41.3% were adolescents, and 35.6% were adults at T1D diagnosis; 46.6% were female). In the unadjusted analyses, younger age (age < 18 years), female sex, and receiving treatment in a general ward were associated with higher HbA1c levels over 24 months. However, after adjustment for important covariates, only younger age remained a significant predictor of inferior glycemic control over 24 months, emphasizing the importance of structured, age-appropriate follow-up care. Conclusions: Younger age at T1D diagnosis independently predicts suboptimal glycemic trajectories over the first two years after T1D onset. Early identification may enable targeted, age-specific interventions to improve long-term outcomes.

Hyperglycemia is substantially involved in the occurrence of complications in people with type 2 diabetes mellitus. While lifestyle interventions remain the cornerstones of diabetes treatment, most people with type 2 diabetes will eventually require pharmacotherapy for improved glycemic management. The definition of individual treatment targets regarding optimal therapeutic efficacy and safety as well as organ-protective effects are the most important factors. These national guidelines summarize the most current evidence-based recommendations for the clinical practice.

Diabetes classified as category 3, usually referred to as type 3 diabetes, encompasses specific types due to other causes. These specific forms of diabetes are clinically and pathophysiologically very heterogeneous. Type 3 diabetes encompasses inherited forms of diabetes (monogenetic diabetes, neonatal diabetes, Down syndrome, Klinefelter syndrome, Turner syndrome), diabetes due to other endocrine disorders (acromegaly, Cushing's disease, glucagonoma) and drug-induced forms, e.g., caused by glucocorticoids, immune checkpoint inhibitors, calcineurin inhibitors, phosphatidylinositol 3 kinase inhibitors, highly active antiretroviral therapy (HAART) and antipsychotics. Additionally, this category includes pancreatogenic forms (after pancreatic surgery, after pancreatitis, pancreatic tumors, hemochromatosis, cystic fibrosis), rare infection-triggered (e.g., congenital rubella syndrome) and autoimmune forms other than type 1 diabetes (e.g., Stiffman syndrome, anti-insulin receptor antibodies, insulin autoimmune syndrome). An exact diagnosis is critical for correct and optimal treatment of affected patients. Exocrine pancreatic insufficiency is not only found in patients with pancreatogenic diabetes but also in patients with type 1 diabetes or long-standing type 2 diabetes.

Introduction and Objective: CGM systems are essential in managing type 1 diabetes (T1D), especially when used alongside automated insulin delivery. However, real-world data comparing different CGM systems, integrated in AID therapy, remain limited. This retrospective study compared glycemic outcomes, specifically time in range, between users of Dexcom G6/G7 and FreeStyle Libre 2/3 CGM systems combined with the CamAPS FX-YpsoPump AID system. Methods: Adult individuals with T1D using Dexcom G6/G7 or FreeStyle Libre 2/3 CGMs with the CamAPS FX system were included. Data of AID users was taken in if they had continuous ≥14 days of valid CGM data. The primary outcome was time in range (70-180 mg/dL), secondary outcomes were TBR and TAR. Results: Data from 52 individuals were analyzed (Dexcom: n=36; Libre: n=16). During a mean use of 89 ± 0.2 days, median HbA1c was 49.4 ± 9.0 mmol/mol for Dexcom users and 52.4 ± 7.6 mmol/mol for Libre users. Table 1 compares the TIR, TBR, and TAR between Dexcom and Libre users. Conclusion: Dexcom G6/G7 (n=35/1) use within CamAPS FX AID therapy was associated with numerically higher TIR compared with FreeStyle Libre 2/3 (n=1/15). These findings should be interpreted with caution given the small sample size, short observation period and lack of statistical power. Larger prospective studies are needed to confirm potential differences between CGM systems in AID therapy. Disclosure S. Basta: None. P.M. Baumann: None. M. Cigler: None. F. Aberer: Advisory Panel; Ended; Bayer AG, Eli Lilly and Company. D.A. Hochfellner: Speaker's Bureau; Ended; Sanofi. Stock/Shareholder; Ended; Novo Nordisk A/S. D.A. Kraus: None. J.K. Mader: Research Support; Current; A. Menarini Diagnostics. Advisory Panel; Current; Abbott Diabetes. Speaker's Bureau; Current; Abbott Diabetes. Advisory Panel; Current; Becton, Dickinson and Company. Speaker's Bureau; Current; Becton, Dickinson and Company. Advisory Panel; Current; Insulet Corporation, Eli Lilly and Company. Speaker's Bureau; Current; Eli Lilly and Company. Advisory Panel; Current; Sanofi. Speaker's Bureau; Current; Sanofi. Advisory Panel; Current; Novo Nordisk A/S. Speaker's Bureau; Current; Novo Nordisk A/S. Advisory Panel; Current; Roche Diagnostics. Speaker's Bureau; Current; Roche Diagnostics. Advisory Panel; Current; Medtronic, Tandem Diabetes Care, Inc., Omnipod. Stock/Shareholder; Current; decide Clinical Software GmbH. Advisory Panel; Current; Dexcom, Inc. Speaker's Bureau; Current; Dexcom, Inc., Sinocare, Buzud. Advisory Panel; Current; Biomea Fusion, Pharmasens. Stock/Shareholder; Current; elyte Diagnostics. Other - CMO (unpaid); Current; elyte Diagnostics. Speaker's Bureau; Current; A. Menarini Diagnostics. Board Member; Current; OMNIA by AI APS. Advisory Panel; Current; Triple Jump.

AIMS:Continuous glucose monitoring (CGM) systems have become important technologies to improve glycaemia in people with type 1 diabetes (T1D). However, it has been shown that during rapid glucose change, sensor performance can deteriorate. Comparative data on sensor performance during high rates of glucose change, such as during exercise, between a real-time continuous glucose monitor (rtCGM) and an intermittently scanned continuous monitor (isCGM) remain limited. METHODS:Twenty-two people with T1D (8 women, age 42 ± 11 years, HbA1c 59 ± 8 mmol/mol (7.6 ± 0.8%)) simultaneously used an rtCGM (Dexcom G6) and an isCGM (Freestyle Libre 1). Sixty-minute exercise sessions were performed on a cycle ergometer at moderate intensity, and glucose values from both CGM systems were compared against capillary reference blood glucose measurements (EKF S-Line; EKF Diagnostics, Germany). Data were assessed using the Median Absolute Relative Difference (MedARD) with interquartile range, as well as the Diabetes Technology Society Error Grid (DTS EG). RESULTS:During exercise, the MedARD was 14.6% [7.0;23.8] for rtCGM (2304 comparison points) vs. 11.6% [5.6;19.6] for isCGM (2266 comparison points) (p < 0.0001). When stratified by glycaemic range, the MedARD was 39.2% [31.8;46.8] vs. 27.0% [17.0;34.6] for time below range (<70 mg/dL) (p = 0.0001), 16.1% [8.1;24.8] vs. 12.8% [6.4;20.4] for time in range (70-180 mg/dL) (p < 0.0001) and 9.5% [4.7;16.0] vs. 8.0% [3.8;13.7] for time above range (>180 mg/dL) (p = 0.0064) for rtCGM vs. isCGM. CONCLUSION:In this head-to-head comparison of rtCGM and isCGM, isCGM demonstrated superior performance during exercise in adults with T1D.

Epidemiologische Untersuchungen zeigen, dass etwa 2–3

Background: Algorithm-based insulin dosing systems are increasingly used in hospitals and have shown the potential to efficiently and safely enable glycemic control. The goal of this study was to evaluate glycemic control using the ultralong-acting basal insulin degludec (IDeg) in combination with insulin aspart (IAsp) within an algorithm-driven electronic clinical decision support system (cDSS) in inpatients with type 2 diabetes (T2D). Methods: In this non-controlled single-arm pilot study, an electronic, algorithm-based cDSS was applied for the management of insulin treatment in an internal general ward. Thirty hospitalized patients with T2D (18 female, age 74.1 ± 10.9 years, HbA1c 72.4 ± 22.3 mmol/mol, BMI 28.6 ± 5.6 kg/m2, diabetes duration 13.2 ± 11.6 years, creatinine 1.5 ± 1.2 mg/dL, length of hospital stay 9.1 ± 4.0 days) were included in the study. Capillary blood glucose (BG) was evaluated four times daily using a point-of-care device integrated into the hospital information system. In addition, all participants received a blinded continuous glucose monitoring (CGM; Abbott Freestyle Libre Pro) system. The primary endpoint was defined as the percentage of BG measurements within the target range of 3.9-7.8 mmol/L. Results: Overall, 722 BG values and 17,242 CGM data points were available. Of those, 52.2% and 55.0% were in the specified target area (3.9-7.8 mmol/L), respectively. Mean BG prior to study start was 11.9 ± 4.4 mmol/L and improved to 7.5 ± 1.9 mmol/L and 7.4 ± 1.4 mmol/L after 6 and 10 days of treatment. BG < 3.9, <3.0 and <2.2 mmol/L was 1.25%, 0.28% and 0%, respectively. Adherence to the total daily insulin dose suggested by the cDSS was 94.2%, and 99.5% of all basal and 85.3% of all bolus insulin suggestions were accepted by the nurses in charge. Basal-bolus therapy using the cDSS covered 85% of the participants' total hospital stay. Conclusions: Glycemic control using IDeg within an algorithm-driven cDSS could effectively and safely be achieved in the hospital and was highly accepted.

Background: In critically ill patients, deviations in glucose levels may lead to significant harm to individuals with and without diabetes. Although subcutaneous continuous glucose monitoring (scCGM) has proven beneficial for patients in standard wards, its implementation in critical care settings has been limited due to multiple factors, potentially resulting in inadequate glycemic control and consequent complications; here, intravascular systems (ivCGM) have the potential to overcome these limitations. Method: This single-center, open-label study, aimed to assess accuracy and safety of a novel intravenous glucose monitoring system in patients with and without diabetes, admitted to a cardiothoracic surgery intensive care unit. Glucose levels were continuously monitored for up to 72 hours in the predefined glucose range of 20 to 400 mg/dL and compared with arterial glucose measurements (blood gas analyses [BGAs]). Results: Twenty-eight participants successfully completed the study, allowing the collection of 1224 ivCGM/BGA data pairs. Due to the exploratory nature of the trial in this vulnerable patient population, no data pairs <70 mg/dL and limited data pairs in level 2 hyperglycemia (>250 mg/dL) were observed. A mean absolute relative difference (MARD) of 8.7 ± 7.8% was found, whereas the mean absolute difference (MAD) for values <100 mg/dL was 3.3 ± 2.7 mg/dL. In participants with diabetes (N = 8,332 ivCGM/BGA data pairs), MARD was 9.6 ± 8.1%. Diabetes Technology Society Error Grid (DTSEG) analysis revealed that all data pairs fell within clinically acceptable zones A and B. Notably, no serious adverse events associated with the device were observed during the study. Conclusion: The present findings indicate that the investigated intravenous glucose monitoring system provides accurate glucose monitoring and demonstrates its safety in critical care settings. This technology offers promise for improved glycemic management in critically ill patients, particularly those with diabetes, potentially mitigating the associated risks and complications.

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR[70–180mg/dL]) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR[<70mg/dL]) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70–180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[<70mg/dL] and the TAR[>180mg/dL] underscored the data in the main analysis. Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.