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ZusammenfassungDie Hyperglykämie ist wesentlich an der Entstehung der Spätkomplikationen bei an Diabetes mellitus Typ 2 erkrankten Patienten/Patientinnen beteiligt. Während Lebensstilmaßnahmen die Eckpfeiler jeder Diabetestherapie bleiben, benötigen im Verlauf die meisten Patienten/Patientinnen mit Typ 2 Diabetes eine medikamentöse Therapie. Bei der Definition individueller Behandlungsziele stellen die Therapiesicherheit, die Effektivität sowie substanzspezifische, kardiovaskuläre Effekte der Therapie die wichtigsten Faktoren dar. In dieser Leitlinie haben wir die rezenten evidenzbasierten Daten für die klinische Praxis zusammengestellt.

The mechanisms accounting for the functional changes of α- and β-cells over the course of Type 1 Diabetes (T1D) development are largely unknown. Permitted by our established technology of high spatiotemporal resolution imaging of cytosolic Ca2+ ([Ca2+]c) dynamics on fresh pancreas tissue slices, we tracked the [Ca2+]c dynamic changes, as the assessment of function, in islet α- and β-cells of female non-obese diabetic (NOD) mice along the development of spontaneous diabetes. We showed that during the phases of islet inflammation, 8 mM glucose-induced synchronized short [Ca2+]c events in β-cells were diminished, whereas long [Ca2+]c events were gradually more triggerable at sub-stimulatory 4 and 6 mM glucose. In the islet destruction phase, the synchronized short [Ca2+]c events in a subset of β-cells resumed at high glucose condition, while the long [Ca2+]c events were significantly elevated already at sub-stimulatory glucose concentrations. In the α-cells, the glucose sensitivity of the [Ca2+]c events persisted throughout the course of T1D development. At the late islet destruction phase, the α-cell [Ca2+]c events exhibited patterns of synchronicity. Our work has uncovered windows of functional recovery in β-cells and potential α-cells functional synchronization in NOD mice over the course of T1D development.

The mechanisms accounting for the functional changes of α- and β-cells over the course of Type 1 Diabetes (T1D) development are largely unknown. Permitted by our established technology of high spatiotemporal resolution imaging of cytosolic Ca2+ ([Ca2+]c) dynamics on fresh pancreas tissue slices, we tracked the [Ca2+]c dynamic changes, as the assessment of function, in islet α- and β-cells of female non-obese diabetic (NOD) mice along the development of spontaneous diabetes. We showed that during the phases of islet inflammation, 8 mM glucose-induced synchronized short [Ca2+]c events in β-cells were diminished, whereas long [Ca2+]c events were gradually more triggerable at sub-stimulatory 4 and 6 mM glucose. In the islet destruction phase, the synchronized short [Ca2+]c events in a subset of β-cells resumed at high glucose condition, while the long [Ca2+]c events were significantly elevated already at sub-stimulatory glucose concentrations. In the α-cells, the glucose sensitivity of the [Ca2+]c events persisted throughout the course of T1D development. At the late islet destruction phase, the α-cell [Ca2+]c events exhibited patterns of synchronicity. Our work has uncovered windows of functional recovery in β-cells and potential α-cells functional synchronization in NOD mice over the course of T1D development.

Pulmonary hypertension (PH) is a severe hemodynamic, progressive condition associated with high morbidity and mortality where early and less invasive diagnostics could crucially improve management. There is a need for biomarkers in PH that are functional, diagnostic, and prognostic. We used a broad metabolomics approach with machine learning analysis and specific free fatty acid (FFA)/lipid-ratios to develop diagnostic and prognostic PH biomarkers.In a training cohort of 74 PH patients, 30 disease controls without PH, and 65 healthy controls, we identified diagnostic and prognostic markers that were validated in an independent cohort of 64 subjects. Markers based on lipophilic metabolites were more robust than those based on hydrophilic metabolites. FFA/lipid-ratios provided excellent diagnostic accuracy for PH with an AUC of up to 0.89 and 0.90 in the training and the validation cohorts, respectively. The ratios provided age-independent prognostic information and a combination of a ratio with established clinical scores increased the hazard ratio (HR) for FPHR4p and COMPERA2 from 2.5 to 4.3 and from 3.3 to 5.6, respectively.Pulmonary arteries (PA) of idiopathic PAH (IPAH) lungs show lipid accumulation and altered expression of lipid homeostasis-related genes that may explain this accumulation. Our functional studies in PA endothelial and smooth muscle cells have shown that increased FFA levels caused excessive proliferation and PA endothelial barrier dysfunction, both hallmarks of pulmonary artery hypertension (PAH).In conclusion, lipidomic changes in PH provide novel diagnostic and prognostic biomarkers and may point to new metabolic therapy targets.

Summary Background The number of dialysis patients is steadily increasing. Associated comorbidities include impaired bone and mineral metabolism, termed chronic kidney disease-mineral and bone disorder (CKD-MBD), leading to a high fracture risk, increased morbidity and mortality and impaired quality of life. While the bone density is assessed with dual-energy X‑ray absorptiometry (DXA), the trabecular bone score (TBS) captures the image texture as a potential index of skeletal microarchitecture. The aim of this study was to evaluate the clinical relevance of DXA and TBS in dialysis patients with and without prevalent fractures. Methods Bone disorders were evaluated in 82 dialysis patients (37% female) at the University Hospital of Graz, Austria, by DXA including the assessment of the TBS based on a patient interview and the local routine patient database software. The patient cohort was stratified by having sustained a fragility fracture in the past or not. Descriptive statistics, t‑tests for continuous variables and χ2-tests for nominal variables including results of DXA and TBS were performed to compare these groups considering the dialysis modality and duration as well as the number of kidney transplantations. Results Of the 82 patients, 32 (39%) had a positive history of fractures. There was a significant association between dialysis duration and fracture prevalence (p p p Conclusion The use of DXA has a limited role in fracture prediction in dialysis patients; however, the TBS seems to add information as an additional tool for fracture risk estimation in this patient population.

OBJECTIVE To investigate the safety and feasibility of 3 nonconsecutive days of intermittent fasting (IF) per week over 12 weeks in participants with insulin-treated type 2 diabetes. RESEARCH DESIGN AND METHODS Forty-six people were randomized to an IF or control group. Dietary counseling and continuous glucose monitoring was provided. Coprimary end points were the change in HbA1c from baseline to 12 weeks and a composite end point (weight reduction ≥2%, insulin dose reduction ≥10%, and HbA1c reduction ≥3 mmol/mol). RESULTS The IF group showed a significant HbA1c reduction (−7.3 ± 12.0 mmol/mol) compared with the control group (0.1 ± 6.1 mmol/mol) over 12 weeks (P = 0.012). The coprimary end point was achieved by 8 people in the IF and none in the control group (P < 0.001). No severe hypoglycemia occurred. CONCLUSIONS IF is a safe and feasible dietary option to ameliorate glycemic control while reducing total daily insulin dose and body weight in insulin-treated people with type 2 diabetes.

Coronavirus disease 2019 (COVID-19)-induced metabolic alterations have been proposed as a source for prognostic biomarkers and may harbor potential for therapeutic exploitation. However, the metabolic impact of COVID-19 in hemodialysis (HD), a setting of profound a priori alterations, remains unstudied. To evaluate potential COVID-19 biomarkers in end-stage kidney disease (CKD G5), we analyzed the plasma metabolites in different COVID-19 stages in patients with or without HD. We recruited 18 and 9 asymptomatic and mild, 11 and 11 moderate, 2 and 13 severely affected, and 10 and 6 uninfected HD and non-HD patients, respectively. Plasma samples were taken at the time of diagnosis and/or upon admission to the hospital and analyzed by targeted metabolomics and cytokine/chemokine profiling. Targeted metabolomics confirmed stage-dependent alterations of the metabolome in non-HD patients with COVID-19, which were less pronounced in HD patients. Elevated kynurenine levels and lipid dysregulation, shown by an increase in circulating free fatty acids and a decrease in lysophospholipids, could distinguish patients with moderate COVID-19 from non-infected individuals in both groups. Kynurenine and lipid alterations were also associated with ICAM-1 and IL-15 levels in HD and non-HD patients. Our findings support the kynurenine pathway and plasma lipids as universal biomarkers of moderate and severe COVID-19 independent of kidney function.

Breakdown of self-tolerance is an important common mechanism in autoimmunity. We use machine learning (ML) to identify common patterns and dissimilarities between type 1 diabetes (T1D) , rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) based on immune phenotyping. PBMCs were isolated from patients with T1D, RA, SLE, and controls. A FACS approach was applied, and a traditional analysis was compared to a ML method implemented on R and based on self-organizing maps (Fig) . Our pipeline includes unsupervised pre-gating, normalization, FlowSOM clustering, and a statistical model (GLMM) , to check for significant differential abundances of cell populations among the autoimmune conditions. After applying our automated workflow to one T cell panel we could identify 14 new cell clusters present in all the samples. The GLMM test revealed a cluster with a significant difference (p=0.035) and a trending one (p=0.059) on the abundance across the different diseases. In particular, CD4pos T cells expressing high IL-7 receptor (CD127) levels and median amounts of CD15s but low CD25, CD161 and FoxP3 are increased in T1D whereas CD4+CD25++CD15s+FoxP3lowCD161lowCD45RA- cells are increased in SLE. In conclusion, our ML workflow identifies a new subtype of T cells significantly increased in T1D. This unsupervised analysis approach for large datasets enables the discovery of new biomarkers complementing traditional workflows. Disclosure J.Vera-ramos: None. B.Prietl: None. L.Herbsthofer: None. V.Pfeifer: None. P.López-garcía: None. M.Stradner: Consultant; AbbVie Inc., AstraZeneca, Speaker's Bureau; Janssen Pharmaceuticals, Inc. T.Pieber: Advisory Panel; Arecor, Novo Nordisk A/S, Research Support; AstraZeneca, European Union, JDRF, Novo Nordisk Foundation, Sanofi, Speaker's Bureau; Novo Nordisk A/S, Roche Diagnostics. Funding JDRF, LRA, & NMSS Joint RFA (2-SRA-2021-1043-S-B)

Insulin icodec is a basal insulin in development for once-weekly (OW) dosing. The aim of this study was to compare the hypoglycemia frequency and response after icodec vs. insulin glargine U100 (IGlar) overdosing. In a randomized, open-label, two-period crossover trial, 43 individuals with T2D on basal insulin±metformin (mean±SD age 56±9 yrs, HbA1c 7.2±0.7%) received OW icodec for 6 weeks and once-daily IGlar for 12 days at equimolar total weekly doses based on the individual daily run-in IGlar dose (mean 30±14 U) titrated to a fasting SMPG target of 80-130 mg/dL. Once at steady state, double (DD) and triple (TD) doses of icodec and IGlar were followed by hypoglycemia induction 44 h (icodec) or 7 h (IGlar) post-dose (expected time of maximum glucose-lowering effect) : First, euglycemia was maintained at 100 mg/dL by variable i.v. glucose. Then, PG was allowed to decrease to a nadir of no less than 45 mg/dL maintained for 15 min. Euglycemia was restored by constant i.v. glucose. Hypoglycemic symptom score (HSS) and counterregulatory hormones were assessed at PG 100 mg/dL and at predefined PG levels until nadir PG. For DD, clinically significant hypoglycemia (PG In conclusion, a DD or TD of once-weekly insulin icodec does not lead to increased risk of hypoglycemia compared to once-daily IGlar. During hypoglycemia, a comparable symptomatic response and a moderately greater endocrine response were seen for icodec vs. IGlar. Disclosure E.Svehlikova: n/a. K.Niss arfelt: Employee; Novo Nordisk. R.Cailleteau: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. S.Deller: None. K.Thomsen: Employee; Novo Nordisk. M.Hart: None. I.Mursic: None. T.Pieber: Advisory Panel; Arecor, Novo Nordisk A/S, Research Support; AstraZeneca, European Union, JDRF, Novo Nordisk Foundation, Sanofi, Speaker's Bureau; Novo Nordisk A/S, Roche Diagnostics. H.Haahr: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. Funding Novo Nordisk

Background: Medication errors are common in hospitals. Our aim was to investigate risks of the analogue and digitally-supported medication use process and potential for solutions.Methods: A mixed-method study including a structured literature search, and questionnaires based on the Delphi method was conducted. To identify major risks, a literature search was supplemented by risks from Critical-Incident-Reporting-System cases and risks from experts’ know-how. First, all risks were structured into main and sub-risks and second, risks were grouped into risk clusters. Third, experts were asked to assess risk clusters regarding likelihood of occurrence and impact on patient safety and regarding potential for solutions to strengthen the competence of health care professionals and potential for solutions in digitalization.Results: Overall, 160 main risks and 542 sub-risks were identified. Main risks were grouped into 43 risk clusters. In total, 33 experts (56% female, 50% with >20 years professional-experience) ranked the likelihood of occurrence and the impact on patient safety to 15 top risk clusters regarding the following process steps: admission (n=4), prescribing (n=3), verifying (n=1), preparing/dispensing (n=3), administering (n=1), discharge (n=1), health care professional competence (n=1), and patient (n=1). In total 28 experts (64% female, 43% with >20 years professional-experience) mostly suggested awareness building and training, strengthening of networking, and involvement of pharmacists at point-of-care as solutions to strengthen health care professional competence. For solutions in digitalization a digital medication list, digital warning systems, barcode-technology, and digital support in integrated care were suggested.Conclusions: A multitude of different risks was identified in the medication use process. To increase patient safety and to minimize potential errors, different solutions to strengthen health care professional competence as well as digital solutions exist. As a next step, health care professional competence and digital solutions will be investigated more deeply to derive specific recommendations for intra-hospital clinical practice use.

BACKGROUND AND AIMS Patients on haemodialysis (HD) are expected to have excess mortality in coronavirus disease 2019 (COVID-19). This was challenged by a recent study reporting HD patients to have comparable mortality and decreased ICU admissions when hospitalized with COVID-19. It was speculated that an altered immune system due to chronic inflammation might protect HD patients from severe COVID-19. Therefore, we designed a study to describe the peripheral blood immune phenotype in HD patients and respective controls with COVID-19. METHOD Sixty-four patients (31 HD, 33 non-HD) with PCR-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 16 control patients (10 HD, 6 non-HD) were prospectively included. According to symptoms, COVID-19 patients were categorized as asymptomatic/mild and moderate/severe COVID-19 phenotypes. Cytokine profiling and immune phenotyping were performed. RESULTS Th1 and Th17 plasma cytokine levels were highly increased in HD patients without SARS-CoV-2 infection and were not significantly regulated during COVID-19. In non-HD COVID-19 patients, these cytokines increased significantly with disease severity. While all patients with moderate/severe COVID-19 showed hallmarks of COVID-19 such as decreased CD3+ CD4+ and CD8+ and CD4+CD25hiFoxP3+ regulatory T cells, significantly increased CD38+CD8+ effector memory and CD38+CD8+ TEMRA T cells were detected in HD compared to non-HD patients with moderate/severe COVID-19. Furthermore, CD161+CD8+ T cells decreased significantly in non-HD COVID-19 patients dependent on disease severity, but not in HD patients. Dynamics of B cells and subtypes were comparable in HD and non-HD COVID-19 patients. Significantly fewer moderate/severe COVID-19 HD patients needed ICU treatment [1/13 (7.7%) HD, 12/24 (50%) non-HD], whereas no difference in mortality was observed [4/31 (12.9%) HD, 6/33 non-HD (18.2%)]. CONCLUSION HD patients might be protected from severe COVID-19 due to their chronic inflammatory state with increased CD38+CD8+ effector memory and TEMRA T cells as well as CD161+CD8+ T cells.

BACKGROUND In precision oncology ex vivo drug screening systems have the potential to improve clinical outcomes. Traditionally, cancer drugs are tested on cancer cell line models, but these cannot represent an individual patient and are biologically too distinct. Drug screening systems usually rely on viability assays and correlations to genomic alterations. Beside genomic alterations, the cellular metabolism is significantly altered which may lead to drug resistance. Here we aim to establish a drug screening platform using tumor cells derived directly from the individual patient glial tumor tissue, create patient derived tumor cells (PDCs) and combine the outcomes from standardized viability- and genetic-assays with a new developed metabolomics platform. Materials and METHODS Fresh native tissue from patients harbouring low- and high-grade glioma are collected (n=46). Tumor tissue used for NMR-based metabolomic analyses and targeted sequencing analyses as well as PDC isolation. To preserve the original tumor similarity, tissue is short term cultured for two weeks, and PDCs are seeded and treated with a panel of clinical- and preclinical drugs followed by viability assessment, sequencing and metabolomic profiling. RESULTS Culturing of PDCs is successful in ≥ 85% of patient cases, provided that at least 2 g of tumor tissue is available. The automatized high throughput ex vivo drug response identifies drug candidates, which might become relevant for therapeutic approaches in future. It is possible to distinguish between IDH1-wild-type and IDH1-mutant tumors based on the metabolomic profile, which is confirmed by immunohistochemical staining and molecular analysis of IDH1 R132H-mutation. Strong metabolomic variations have been identified, including GABA, lactate, and myo-inositol levels between tumor and healthy tissue. CONCLUSION Entangling drug screening and genetic assays with metabolomic profiling of glial tumors enriches the information about cellular drug response and paves the way for future clinical studies and better understanding of underlying drug resistance mechanisms in gliomas.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

When designing new smart sensor platforms, the system should be well adapted to the intended use case. In most cases this means that the sensor will be implemented as a part of a larger system—be it as a part of a sensor network or a component of a machine. In both cases the sensor should have a long lifetime, use the available resources with care, handle the data securely, and prevent the system from getting damaged by misusing the sensor knowingly or unknowingly. To test all of these properties, models of the sensor (and its component parts) can be created and used in simulations that represent the environment and the possible uses of the sensor in it. This chapter describes the possibility of creating a new power aware and secured smart sensor using a model-based design approach.

The present article is a recommendation of the Austrian Diabetes Association for the practical use of insulin in type 2 diabetes, including the various insulin regimens.

Realistic simulations of new hardware are of utmost importance to achieve good results. The current approach to such simulations is that the Device under Test is exposed to stimuli that are either generated randomly, or that are generated by engineers reverse engineering the use cases and extending the inputs by some extreme cases. In this paper we describe an approach to generate useful stimuli for a SystemC simulation directly from a simulation of the use case. In this approach the use case is simulated using the Gazebo simulator. The stimuli for the Device under Test are then extracted and sent to the SystemC simulation.

In modern society sensors are omnipresent. They gather information about their environment in order to optimize production flows, minimize energy usage, learn about the environment, or maximize the owner’s comfort. To achieve the desired goal in already existing buildings, sensors are introduced afterwards. These sensors might not be able to connect to a sensor network because of obstacles or user policies. If this happens, other mechanisms to create a network to gather the data need to be found. Additionally, these sensors should last for a long period and are therefore probably powered using energy harvesting methods. In this paper we present an approach for simulating the charging process of such sensors and connecting them to a network using mobile communication partners.

A fast and accurate data transmission from glucose meter to clinical decision support systems (CDSSs) is crucial for the management of type 2 diabetes mellitus since almost all therapeutic interventions are derived from glucose measurements.Aim was to develop a prototype of an automated glucose measurement transmission protocol based on the Continua Design Guidelines and to embed the protocol into a CDSS used by healthcare professionals.A literature and market research was performed to analyze the state-of-the-art and thereupon develop, integrate and validate an automated glucose measurement transmission protocol in an iterative process.Findings from literature and market research guided towards the development of a standardized glucose measurement transmission protocol using a middleware. The interface description to communicate with the glucose meter was illustrated and embedded into a CDSS.A prototype of an interoperable transmission of glucose measurements was developed and implemented in a CDSS presenting a promising way to reduce medication errors and improve user satisfaction.

The Surgical Safety Checklist (SSC) is routinely used in operating rooms (OR) but its acceptance is low. One promising way to improve acceptance of the SSC and thus quality of patient care is digitalization.To investigate how a digitalization of the SSC could be implemented in a teaching hospital. Based on the identified user requirements we designed a first user interface (UI).We performed a literature review, identified user perceptions and requirements during 12 interviews including a standardized questionnaire in surgical departments at the University Hospital Graz (Austria). Subsequently a first prototype of a UI was designed.Seven different approaches for digital SSC were identified in literature. Our interviews showed that 90% of the participants had a positive attitude towards a digitalization of SSC. The most favoured version of a digitalized SSC was a tablet-based client-server system with integration in the EHR and projection on an OR monitor.Digitalization of the SSC is requested by medical and nursing personnel. Based on the identified user requirements we designed a process oriented UI of a digital SSC.

BACKGROUND: Cardiovascular morbidity and mortality are higher among patients with type 2 diabetes, particularly those with concomitant cardiovascular diseases, than in most other populations. We assessed the effects of lixisenatide, a glucagon-like peptide 1-receptor agonist, on cardiovascular outcomes in patients with type 2 diabetes who had had a recent acute coronary event.METHODS: We randomly assigned patients with type 2 diabetes who had had a myocardial infarction or who had been hospitalized for unstable angina within the previous 180 days to receive lixisenatide or placebo in addition to locally determined standards of care. The trial was designed with adequate statistical power to assess whether lixisenatide was noninferior as well as superior to placebo, as defined by an upper boundary of the 95% confidence interval for the hazard ratio of less than 1.3 and 1.0, respectively, for the primary composite end point of cardiovascular death, myocardial infarction, stroke, or hospitalization for unstable angina.RESULTS: The 6068 patients who underwent randomization were followed for a median of 25 months. A primary end-point event occurred in 406 patients (13.4%) in the lixisenatide group and in 399 (13.2%) in the placebo group (hazard ratio, 1.02; 95% confidence interval [CI], 0.89 to 1.17), which showed the noninferiority of lixisenatide to placebo (PCONCLUSIONS: In patients with type 2 diabetes and a recent acute coronary syndrome, the addition of lixisenatide to usual care did not significantly alter the rate of major cardiovascular events or other serious adverse events. (Funded by Sanofi; ELIXA ClinicalTrials.gov number, NCT01147250.).