The diagnosis of type 2 diabetes using classical clinical and laboratory biomarkers (HbA1c, glucose, lipids, BMI, and blood pressure) is a classification by symptoms and does not provide insight into the underlying pathophysiological disorders (insulin resistance, β-cell dysfunction, visceral adipose tissue hormonal secretion, and chronic systemic inflammation). A better understanding of these disorders may help in the selection of appropriate and potentially more successful personalized therapeutic interventions. Based on extensive clinical trial experience, a method for individual phenotyping and consecutive personalized diabetes therapy has been developed in our practice, which we have been using for more than 15 years and would like to share for discussion and debate. In this Part 1, the pathophysiological background and diagnostic approach to phenotyping is described. A consecutive Part 2 will present the translation of the phenotyping result into a personalized diabetes therapy, and another consecutive Part 3 will provide more comprehensive real-world patient observations when practicing this concept. This article is intended as a discussion/concept paper and does not present unpublished patient-level outcome data or formal effectiveness analyses. Prospective validation studies are needed to evaluate the clinical utility of this phenotype-based framework.

Conventional diabetes therapy primarily targets HbA1c using a standardized, stepwise approach, often neglecting individual clinical and diagnostic phenotypes. In this second part of our discussion, we present an alternative strategy. After phenotyping the patient, we initiate a targeted pharmacological combination therapy tailored to the individual’s underlying pathophysiology, alongside lifestyle modifications. Sulfonylureas are completely avoided in this approach. Instead, medications are selected based on their alignment with the patient’s phenotype and absence of contraindications. Early insulin therapy, for example, is particularly effective in patients with β-cell-dysfunction-driven diabetes, whereas GLP-1-supported weight reduction and glitazone therapy are more suitable for insulin-resistance-driven diabetes. For monitoring and determining when temporary therapy intensification may be necessary, we rely on a combination of functional biomarkers (intact proinsulin, adiponectin, hsCRP, and leptin) and conventional clinical parameters (HbA1c, BMI, lipids, blood pressure). Using this personalized strategy, we have consistently achieved long-term glycemic control—often maintaining normal HbA1c levels for up to 15 years in our patients so far.

Background:BeaT-2/Sugarburner is a nutritional supplement based on a co-existing microbial consortium of bacterial strains. The purpose of this study was to investigate potential beneficial effects of the supplement versus placebo on glycemic control and on biomarkers of inflammation, insulin resistance and ß-cell dysfunction. Methods:A total of 40 Patients with type 2 diabetes (31 male, 9 female, age:65.5 ± 8.0 years, BMI: 33.6 ± 5.5 kg/m², HbA1c: 7.2 ± 0.9%) were included into the study. They were randomized to receive 2 capsules of either BeaT-2 or placebo once daily for 6 weeks. Observation parameters were time in normoglycemia as assessed by continuous glucose monitoring, and biomarkers of glycemic control, ß-cell function, insulin resistance and chronic systemic inflammation. Results:Time in range was stable with BeaT-2 between weeks 0 and 2 versus weeks 4 and 6, while it slightly impaired with placebo (-4%, n.s.). At endpoint, there were significant improvements versus baseline with BeaT-2, but not with placebo for biomarkers of glycemic control (BeaT-2 vs Placebo, HbA1c: -0.3% vs 0.1%, fasting glucose: -14% vs +8%, both P < .05), insulin resistance (insulin: -17% vs +14%, HOMA-IR: -26% vs +21%, both P < .05), ß-cell dysfunction (intact proinsulin: -40% vs -2%, P < .05) and chronic systemic inflammation (adiponectin: +8% vs -8%; P < .05, hsCRP: -31% vs +27%, n.s.). In addition, observed changes in the lipid profiles and other parameters of metabolic syndrome were more favorable with BeaT-2 than with placebo. There were no differences between the groups with respect to number and type of adverse events. Conclusion:The results observed with BeaT-2 were comprehensively indicative for improvements in the cardiometabolic situation. BeaT-2 may be a valuable supplement to any existing treatment combination in patients with type 2 diabetes.

Introduction:The active matrix metalloproteinase-8 (aMMP-8) is a functional biomarker of active periodontal tissue destruction. It bridges the gap between clinical findings and underlying biological processes, providing information on active collagen degradation that conventional clinical examinations do not capture. Methods:In a prospective within-subject clinical study, we assessed point-of-care (PoC) mouthrinse aMMP-8 concentrations, periodontal probing depth (PPD), and clinical attachment level (CAL) in 27 adults with stage III/IV (grade C) periodontitis before and 1 month after anti-infective treatment. aMMP-8 PoC results were validated by laboratory western blotting using an independent polyclonal MMP-8 antibody. Results:At baseline, 85.2% of samples showed elevated aMMP-8 PoC concentrations, declining to 18.5% post-treatment. Baseline aMMP-8 was significantly associated with disease severity, correlating with both PPD (r s  = 0.39, p = 0.045) and CAL (r s  = 0.39, p = 0.042). Following treatment, aMMP-8 concentrations decreased significantly (V = 378, p < 0.001), with mean reductions of 78.5 ± 72.6 ng/mL (d = -1.08). Biochemical reductions paralleled clinical improvements, with 100% directional concordance between changes in aMMP-8 and changes in PPD and CAL (95% CI 0.87-1.00). Reductions in aMMP-8 were correlated with improvements in PPD (r s  = 0.47, p = 0.014) but not CAL (r s  = 0.12, p = 0.544). Linear regression analysis indicated that each 10 ng/mL reduction in aMMP-8 corresponded to an estimated 0.034 mm gain in PPD. Conclusion:These findings demonstrate that aMMP-8 PoC monitoring aligns with both baseline disease severity and short-term periodontal healing. aMMP-8 provides an objective biochemical dimension to periodontal assessment, capturing active collagen degradation and serving as a sensitive and clinically meaningful indicator of treatment response.

BACKGROUND:Patch insulin pumps are often treated as a single class, although fully disposable and semi-reusable designs differ in cost and waste. We evaluated real-world clinical, pharmacoeconomic, and environmental outcomes of a semi-reusable tubeless insulin pump (Microtech Equil™, referred as SR-TIP). METHODS:Prospective, multicentre, open-label real-world study in adults with type 1 or type 2 diabetes transitioning from continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI). Follow-up was 3 months. Primary endpoints were HbA1c non-inferiority and change in hypoglycaemic event frequency. Secondary endpoints included safety, device deficiencies, patient-reported outcomes (Diabetes Treatment Satisfaction Questionnaire, Device Assessment Questionnaire), monthly disposable treatment costs, and waste based on disposable component counts and material composition. RESULTS:Ninety-seven participants completed follow-up (CSII n = 79; MDI n = 18). HbA1c changes met non-inferiority criteria in both groups. Hypoglycaemic events decreased by 45% in CSII users and 86% in MDI users. Device-related adverse events were infrequent and mainly mild. Among participants previously using fully disposable tubeless pumps, mean monthly disposable treatment costs decreased by €108.6 (p < 0.001). The semi-reusable architecture eliminated routine disposal of batteries/electronic components and reduced overall waste. CONCLUSIONS:In routine care, a semi-reusable tubeless pump maintained glycaemic control while reducing hypoglycaemia and disposable costs versus fully disposable patch pumps, with measurable reductions in electronic waste. These data support value-based, sustainable diabetes technology adoption.

Assessing bleeding duration in patients on direct oral anticoagulants (DOACs) is crucial for evaluating reversal strategies, yet no standardized methods are currently available for clinical trials. We investigated a non-invasive, reproducible bleeding model based on a routine dental cleaning procedure, aiming to provide a clinically meaningful endpoint for testing DOAC antidots. We enrolled 90 subjects in this prospective observational pilot study: 49 not receiving DOACs (group 1; 30 female, 19 male; mean age: 40.7 ± 16 years) and 41 on DOACs (group 2; 22 female, 19 male; mean age: 71.3 ± 20.4 years). Mouth-rinse samples were collected before and at 5-minute intervals for up to 60 min following bleeding-on-probing assessment, periodontal staging, and dental cleaning. Red blood cell (RBC) counts in these samples were determined using a hemocytometer. Bleeding cessation was defined as an RBC count below 150 cells/µL. Reaching this threshold within 15 min was defined as the clinical endpoint (EP). In group 1, 83.7

Adult-onset type 1 diabetes (T1D) likely exceeds childhood-onset in absolute numbers, yet many cases are underestimated due to misclassification as type 2 diabetes. This pragmatic review synthesizes current evidence on epidemiology, pathophysiology, diagnosis, and early disease-modifying therapy in adults. Incidence data from 32 countries indicate that adults account for a median 42% of new T1D diagnoses. Autoimmunity follows the pediatric, HLA-restricted paradigm, but β-cell dysfunction appears slower, reflected by measurable C-peptide for years. Misdiagnosis delays insulin initiation, increases ketoacidosis risk, and forfeits opportunities for β-cell-sparing interventions. We present a four-step diagnostic algorithm integrating an islet autoantibody panel with a fasting or random C-peptide-to-glucose ratio, and highlight red-flag scenarios warranting repeat testing. We also propose a hypothetical, risk-enriched four-step pathway to identify presymptomatic T1D in adults that begins with a higher HbA1c trigger, uses enrichment to raise pretest probability, and reserves full autoantibody testing for high-probability individuals. Given low prevalence and false-positive risk, this pathway needs prospective validation before routine care. We review adult and adolescent evidence for targeted immunomodulators, including teplizumab, abatacept, rituximab, low-dose anti-thymocyte globulin, ustekinumab, golimumab, baricitinib and alefacept, as well as β-cell-directed agents such as verapamil and imatinib, and discuss emerging HLA- and autoantibody-defined endotypes that may predict response. Collectively, current evidence supports routine autoimmune diabetes screening in adults with new-onset diabetes.

Diagnosis of type 2 diabetes using the classical clinical and laboratory biomarkers (HbA1c, glucose, lipids, BMI, and blood pressure) is a classification by symptoms and does not provide insight into the underlying pathophysiological disorders (insulin resistance, ß-cell dysfunction, visceral adipose tissue hormonal secretion, and chronic systemic inflammation). A better understanding of these disorders may help for the selection of appropriate and potentially more successful personalized therapeutic interventions. Based on an extensive clinical trial experience, a method for individual phenotyping and consecutive personalized diabetes therapy has been developed in our practice, which we have been using for more than 15 years and which we would like to share for discussion and debate. In this part 1, the pathophysiological background and the diagnostic approach to phenotyping will be described. A consecutive part 2 will present the translation of the phenotyping result into a personalized diabetes therapy and will provide real-world patient examples when practicing this concept.

Background: Sensors for continuous glucose monitoring (CGM) are now commonly used by people with type 1 and type 2 diabetes. However, the response of these devices to potentially interfering nutritional, pharmaceutical, or endogenous substances is barely explored. We previously developed an in vitro test method for continuous and dynamic CGM interference testing and herein explore the sensitivity of the Abbott Libre2 (L2) and Dexcom G6 (G6) sensors to a panel of 68 individual substances. Methods: In each interference experiment, L2 and G6 sensors were exposed in triplicate to substance gradients from zero to supraphysiological concentrations at a stable glucose concentration of 200 mg/dL. YSI Stat 2300 Plus was used as the glucose reference method. Interference was presumed if the CGM sensors showed a mean bias of at least ±10% from baseline with a tested substance at any given substance concentration. Results: Both L2 and G6 sensors showed interference with the following substances: dithiothreitol (maximal bias from baseline: L2/G6: +46%/−18%), galactose (>+100%/+17%), mannose (>+100%/+20%), and N-acetyl-cysteine (+11%/+18%). The following substances were found to interfere with L2 sensors only: ascorbic acid (+48%), ibuprofen (+14%), icodextrin (+10%), methyldopa (+16%), red wine (+12%), and xylose (>+100%). On the other hand, the following substances were found to interfere with G6 sensors only: acetaminophen (>+100%), ethyl alcohol (+12%), gentisic acid (+18%), hydroxyurea (>+100%), l-cysteine (−25%), l-Dopa (+11%), and uric acid (+33%). Additionally, G6 sensors could subsequently not be calibrated for use after exposure to dithiothreitol, gentisic acid, l-cysteine, and mesalazine (sensor fouling). Conclusions: Our standardized dynamic interference testing protocol identified several nutritional, pharmaceutical and endogenous substances that substantially influenced L2 and G6 sensor signals. Clinical trials are now necessary to investigate whether our findings are of relevance during routine care.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool—the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

BACKGROUND:Testing the potential influence of interfering substances on the measurement performance of needle sensors for continuous glucose monitoring (CGM) is a challenging task. For proper function, the sensors need an almost stable fluidic environment. Previously published in vitro interference experiments were measuring under static concentration conditons. Our experimental setup allows for interference testing with dynamic changes of the interferent concentrations.METHODS:We designed a macrofluidic test stand that is fueled by several high-pressure liquid chromatography (HPLC) pumps generating programmable glucose and/or interferent gradients in phosphate-buffered saline (PBS). After optimizing experimental parameters (channel dimensions, temperature, flow rates, gradient slopes, buffer, pH etc.), we validated the setup using Dexcom G6 (G6) and Freestyle Libre 2 (L2) sensors with/without interferents, and using YSI 2300 Stat plus as the reference glucose device at room temperature.RESULTS:Both sensors tracked the programmed glucose changes. After calibration, G6 results closely matched glucose reference readings, while L2 routinely showed ~50% to 60% lower readings, most likely because of the factory-based calibration and temperature compensation. Gradients of maltose, acetaminophen, and xylose were employed to further validate the setup. As expected, both sensors were not affected by maltose. We confirmed previous findings regarding susceptibility of G6 readings to acetaminophen and L2 readings to xylose. Signals from both sensors are influenced by temperature in a linear fashion.CONCLUSIONS:Our experimental in vitro setup and protocol may provide a useful method to dynamically test CGM sensors for interfering substances. This may help to improve the accuracy of future CGM sensor generations.

Rapid and sensitive detection of pathogens is critical in interrupting the transmission chain of infectious diseases. Currently, real-time (RT-)PCR represents the gold standard for the detection of SARS-CoV-2. RNase HII-assisted amplification (RHAM) is a promising technology, enabling reliable point-of-care (PoC) testing; however, its diagnostic accuracy has not yet been investigated. The present study compared the Pluslife Mini Dock (RHAM technology), with Abbott ID Now and Cepheid GeneXpert IV. The positive percent agreement (PPA) and negative percent agreement (NPA) were determined in 100 SARS-CoV-2 positive and 210 SARS-CoV-2 negative samples. Further, the reliability of the Pluslife Mini Dock was investigated in different SARS-CoV-2 variants (Delta and Omicron subvariants). The PPA was 99.00% for Pluslife, 100.00% for Abbott ID Now, and 99.00% for Cepheid GeneXpert, with an NPA of 100.00%, 98.90%, and 93.72%, respectively. Abbott ID Now demonstrated the highest rate of invalid results. All SARS-CoV-2 analysed variants were detected by the Pluslife device. Altogether, the Pluslife Mini Dock demonstrated a PPA of 99.16% (235/237) for CT < 36 and an NPA of 100.00% (313/313), respectively. In conclusion, the Pluslife Mini Dock demonstrated better analytical performance than Abbott ID Now and Cepheid GeneXpert IV, representing a highly accurate and rapid PoC alternative to RT-PCR.

Background: Diabetic foot syndrome (DFS) is one of the most severe secondary complications of diabetes mellitus. It is currently treated with improvement and maintenance of good glycemic control, pain drugs, antibiotics, and drugs and surgical measures to improve the vascular blood flow into the legs. In late stages, foot amputation (in part or total) is the only means to safe the patient’s life. Case Report: A 56-year-old woman with type 1 diabetes for more than 40 years, suffered from diabetic foot syndrome with complete closure of the arteria fibularis and a non-healing foot ulcer at the left leg. The need for lower ankle amputation was already determined. We tried to improve the vascular situation by means of a series of intravenous hyaluronidase infusions over a period of three weeks. An immediate improvement of the general condition was observed. The ulcer healed completely within 8 weeks, and a re-opening of the previously closed vessel as well as further additional collateral blood-flow into the left foot could be determined by means of an angiogram six months later. Discussion: The observed beneficial impact of intravenous hyaluronidase treatment on atherosclerotic lesions can be explained by the molecular action of the enzyme on the glycocalix, the extracellular hyaluronan layer that separates the endothelial cells from the blood stream. Successful treatment of DFS with hyaluronidase infusions has been reported already 50 years ago but research on this topic ceased, when stents and other apparently more compelling vascular treatment methods were detected. Conclusions: In a severe case of DFS, we were able to re-open a critical arterial vessel and improve the entire vascular blood flow by means of intravenous hyaluronidase infusions. Clinical studies are required to confirm the value of hyaluronidase infusions as treatment alternative to amputation for DFS.

Background: Sensors for continuous glucose monitoring (CGM) are increasingly used by people with type 1 and type 2 diabetes. However, the reaction of the glucose oxidase-based sensor technologies to potentially interfering nutritional, endogenous, or pharmaceutical substances is barely understood. We have developed an in-vitro test method for continuous and dynamic CGM interference testing and explored the sensitivity of the Libre 2 sensor to a panel of 68 individual substances. Method: In each interference experiment, three sensors were exposed to substance gradients from zero to supra-physiological concentrations generated by HPLC-pumps at a fixed glucose concentration of 200 mg/dL. YSI Stat 2300 Plus was used as the glucose reference method. Interference was assumed if the CGM sensors showed a mean bias of more than ±10% from baseline with a tested substance at any given substance concentration. Results: Interference was seen with the following substances: xylose (difference from baseline: +178%), galactose (+134%), mannose (+130%), hydroxyurea (+84%), ascorbic acid (+48%), dithiothreitol (+46%), methyldopa (+16%), ibuprofen (+14%), red wine (+12%), N-acetyl-cysteine (+11%), icodextrin (+10%), while no interference was seen with the other substances. Suspected sensor fouling, in that the needle sensors subsequently ceased to operate after exposure to a substance, was not observed with Libre 2. Conclusions: Our standardized dynamic interference testing protocol identified several nutritional and pharmacological substances that substantially influenced the Libre 2 signal. Clinical trials are now necessary to investigate, whether our findings are of relevance for routine care. Disclosure H.Jensch: None. N.Thomé: None. G.Srikanthamoorthy: None. L.Weingärtner: None. S.J.Setford: Employee; LifeScan Scotland Ltd. E.H.Holt: Employee; LifeScan Inc. M.Grady: Employee; Lifescan. C.Kuhl: None. A.Pfützner: Consultant; Novo Nordisk A/S, Research Support; LifeScan Diabetes Institute, Speaker's Bureau; AstraZeneca, Stock/Shareholder; Lifecare A/S, Diakard. Funding European Union’s Horizon 2020 Research and Innovation Program (951933); LifeScan Global Corporation

Background: Continuous glucose monitoring (CGM) by means of needle sensors is becoming a standard option to gather the necessary glucose information for treatment of patients with type 1 and type 2 diabetes mellitus. Little is known, however, about the reaction of the glucose oxidase-based sensor technologies to potentially interfering nutritional, endogeneous or pharmaceutical substances. Here we report on results obtained with the Dexcom G6 needle sensor with our in-vitro dynamic intererence testing method. Method: We used HPLC pump-controlled substance gradients to expose Dexcom G6 needle sensors in a 3D-printed test cartridge to varying concentrations of potentially interfering substances at a fixed glucose concentration of 200 mg/dL. We tested 68 substances in triplicate using YSI Stat 2300 Plus as the glucose reference method. Interference was assumed if a CGM needle sensor showed more than ±10% difference from baseline with a tested substance at the given test concentration. Results: Interference was seen with the following substances: acetaminophen (>+100% bias from baseline), hydroxyurea (>+100%), uric acid (+33%), mannose (+20%), N-acetyl-cysteine (+18%), gentisic acid (+18%), galactose (+17%), ethyl alcohol (+12%), L-dopa (+11%), dithiothreitol (−18%), L-cysteine (−25%), while no interference was seen with other tested substances. In addition, the needle sensors subsequently ceased to operate when exposed to dithiothreitol, L-cysteine, gentisic acid, and mesalazine (suspected sensor fouling). Conclusions: Employing our standardized dynamic interference testing protocol, several nutritional and pharmacological substances were identified as influencing the Dexcom G6 signal. If confirmed by clinical trials, such interference will have to be considered when making treatment decisions using Dexcom G6 results in daily routine care. Disclosure A.Pfützner: Consultant; Novo Nordisk A/S, Research Support; LifeScan Diabetes Institute, Speaker's Bureau; AstraZeneca, Stock/Shareholder; Lifecare A/S, Diakard. H.Jensch: None. G.Srikanthamoorthy: None. C.Kuhl: None. S.J.Setford: Employee; LifeScan Scotland Ltd. M.Grady: Employee; Lifescan. E.H.Holt: Employee; LifeScan Inc. N.Thomé: None. Funding European Union’s Horizon 2020 Research and Innovation Program (951933); LifeScan Global Corporation

Diagnosis of type-2-diabetes using the classic clinical and laboratory markers (HbA(1c), glucose, lipids, BMI, and blood pressure) is a classification by symptoms and does not provide insight into the underlying pathophysiological disorders (insulin resistance, beta-cell dysfunction, adipogenetic hormone secretion, and chronic systemic inflammation). A better understanding of these disorders could be helpful for the selection of appropriate and successful therapeutic interventions in terms of personalized therapy. Based on our extensive study experience, a method for phenotyping and consecutive personalized diabetes therapy has been developed in our practice, which we have been using for almost 15 years and which I would like to share and present here for discussion. In this part 1, the background and approach to phenotyping will be described. The following part 2 will present the implementation of phenotyping into individualized diabetes therapy and show what results we have been able to achieve in practice with this concept so far.

Background: We conducted a prospective placebo-controlled double-blind randomized Study to assess the impact of a single dose of a nutritional Supplement (AB001) on alcohol absorption in healthy subjects. Other objectives were the impact on breath alcohol content, cognitive function 1 hour after alcohol uptake and tolerability. Method: A total of 24 healthy volunteers were enrolled into the study (12 male, 12 female, age: 28.3 ± 10.8 years, BMI: 23.5 ± 5.7 kg/m²). On the experimental day, they ingested a light breakfast together with a single dose (2 capsules) of AB001 (or placebo) and drank 2 moderate glasses of spirit (a total of 0.6 g/kg body weight). Breath alcohol tests and blood draws for determination of blood alcohol levels were performed for up to 6 hours. After crossover, the experiment was repeated in the following week. Areas under the curves were calculated to determine alcohol absorption rates. Results: There was a significant reduction of blood alcohol by 10.1% ( P < .001) with AB001, when compared to placebo. There was a less pronounced but also significant reduction of alcohol in the breath test by 7.2% ( P < .05). No difference in the cognitive function test between AB001 and placebo could be observed 60 minutes after alcohol ingestion (22.6 ± 8.0 seconds vs 23.0 ± 11.2 seconds, n.s.). The supplement uptake was well tolerated and there were no adverse events related to the study intervention. Conclusion: Uptake of a single dose of AB001 shortly before drinking alcohol significantly reduced plasma alcohol and breath alcohol concentrations, but the effect was less pronounced compared to chronic uptake as shown previously.