Abstract Background The global rise in adolescent obesity is driving an aggressive phenotype of type 2 diabetes (T2D), which is marked by rapid progression and premature mortality. Therefore, evidence-based, internationally applicable prevention guidelines are urgently needed. Methods A multidisciplinary international panel (n = 21) developed consensus recommendations through a systematic literature review (utilizing Web of Science, Scopus, PubMed, Cochrane, and EMBASE for the years 2000–2025) and a structured meeting process (held from October 2024 to December 2025). Consensus required ≥ 80% agreement. A total of eighty statements were initially proposed, from which sixty achieved preliminary agreement in Phase 1. Following iterative refinement in Phases 2 and 3, fifty achieved final consensus, and thirty were eliminated, either for failing to achieve 80% agreement or by being designated for further research. Results The panel established fifty evidence-based recommendations across nine domains, which include screening, lifestyle intervention, school-based initiatives, digital health, therapeutic options, and policy. Key recommendations are to implement standardized screening protocols in schools, regulate the advertising of unhealthy foods and beverages, promote healthy lifestyle trends through media, and integrate artificial intelligence (AI) to tailor prevention plans. Conclusion This consensus provides a practical, multisectoral framework for the global prevention of adolescent obesity and T2D, designed for application across healthcare, governmental, and non-governmental settings.

Background Digital technologies for type 2 diabetes mellitus (T2DM) care hold great potential to improve patients’ health in the long term. Only a subset of telemedicine offerings are digital interventions that meet the criteria for prescribable digitale Gesundheitsanwendung (digital health apps; DiGAs) in Germany. Digital treatments further provide vast amounts of patient data that are important to generate evidence. Objective This systematic review aims to analyze the efficacy of multimodal digital therapies that mainly meet the DiGA criteria for patients with T2DM and to elicit the potential of such therapies. This includes evidence from randomized controlled trials (RCTs) as well as from real-world data. The outcome of interest was a reduction in glycated hemoglobin (hemoglobin A1c [HbA1c]; long-term blood glucose measurements). Methods A systematic literature search was conducted in the literature bases PubMed, LIVIVO, and Cochrane, based on the predefined PICO (Population; Intervention; Control; Outcome) scheme. Identified studies were assessed for risk of bias, pragmatism, and overall quality of evidence. Meta-analyses were conducted for between group differences using RCTs only, and for within-group differences using RCTs and non-RCTs, to examine the effect of the interventions on HbA1c. Results In total, 795 records were identified, of which 24 were eligible for this systematic review and 23 studies were eligible for the meta-analysis. The results of the meta-analyses showed significant and clinically relevant reductions in HbA1c in patients with T2DM. Regarding the between-group difference for HbA1c reduction, the pooled effect of the RCTs showed a reduction of –0.36% (95% CI –0.59% to –0.14%; P<.001), favoring app-based interventions. The average mean within-group reduction in HbA1c was –0.79 (95% CI –1.02 to –0.55), with no significant difference between RCTs (–0.69, 95% CI –1.13 to –0.24) and non-RCTs (–0.87, 95% CI –1.16 to –0.57; P<.01, differences between RCTs and RCTs P=.44). A pragmatism rating showed that both study types were on average (very) pragmatic, that is, close to usual care. However, the overall quality of evidence was low to very low. Conclusions This systematic review shows that digital therapies that mainly meet the DiGA criteria can effectively improve HbA1c in patients with T2DM. The integration of digital health care into usual care holds great potential and should be considered as a complementary option to usual care in the future. Trial Registration PROSPERO CRD42023440203; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=440203

Introduction and Objective: Prediabetes remission has beneficial effects for type 2 diabetes (T2D) prevention. However, it is unknown whether early remission is superior to late remission. Thus, we investigated if reaching prediabetes remission early during a lifestyle intervention is associated with lower T2D risk compared to reaching remission later on. Methods: We studied 865 individuals with prediabetes from the German multi-center Prediabetes Lifestyle Intervention Study (PLIS) who could be classified into early remission at 6 months of a lifestyle intervention (ER, n = 217), late remission at 12 months (LR, n = 110), or no remission (NR, n = 538). Prediabetes remission was defined as return to normal glucose regulation and normalized HbA1c according to ADA criteria. Cox regression models were fit with age, sex, intervention intensity, T2D risk and weight loss as covariates. Results: The ER group (n=217) was comparable in age (p=0.24), sex distribution (p = 0.07), BMI (p > 0.99), insulin sensitivity (p = 0.2) and insulin secretion (p = 0.48) vs the LR group (n=110). Fasting glucose (5.70 ±0.43 mmol/L vs 5.83 ±0.44, p = 0.016) and HbA1c (5.53 ±0.29 % vs 5.68 ±0.31, p < 0.001) was slightly lower in ER compared to LR, while 2h glucose was similar (p = 0.77). Overall, T2D risk was lower in both ER and LR compared to NR (n=538; RR 0.15 [95% CI: 0.07-0.31], p < 0.001 and 0.44 [0.23-0.82], p = 0.009, respectively). However, ER provided a more pronounced T2D risk reduction than LR (0.31 [0.12-0.79], p = 0.01). Conclusion: Achieving early remission of prediabetes to NGR during lifestyle intervention may provide additional benefits for T2D prevention compared with late remission. A. Sandforth: None. L. Sandforth: None. S. Katzenstein: None. J. Seissler: None. N. Perakakis: Other Relationship; Novo Nordisk, Lilly Diabetes. Advisory Panel; Bayer Pharmaceuticals, Inc. Other Relationship; APOGEPHA, Transmedac Innovations AG, GWT-TUD, Elbe-Gesundsheintszentrum GmbH, Open Exploration. R. Wagner: Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. A. Peter: None. R. Lehmann: None. H. Preissl: None. I. Yurchenko: None. J. Szendroedi: Advisory Panel; Novo Nordisk, Lilly Diabetes, Novartis AG, Boehringer-Ingelheim. M. Blüher: Advisory Panel; AstraZeneca. Speaker's Bureau; Amgen Inc. Advisory Panel; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Eli Lilly and Company, Novo Nordisk, Nestlé Health Science, Sanofi-Aventis Deutschland GmbH. A. Schürmann: None. S. Kabisch: Research Support; Almond Board California, California Walnut Commission. Other Relationship; JuZo-Akademie, Boehringer-Ingelheim. Research Support; J. Rettenmaier & Söhne. Other Relationship; Lilly Diabetes. Research Support; Wilhelm-Doerenkamp-Foundation. K. Mai: None. P.E. Schwarz: None. M. Heni: Advisory Panel; Amryt Pharma. Speaker's Bureau; Amryt Pharma, AstraZeneca, Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Lilly Diabetes, Novartis AG, Novo Nordisk, Sanofi. M. Roden: Research Support; Boehringer-Ingelheim. Advisory Panel; Echosens. Speaker's Bureau; Madrigal Pharmaceuticals, Inc. Advisory Panel; MSD Life Science Foundation. Board Member; Novo Nordisk. Advisory Panel; TARGET PharmaSolutions, Inc. N. Stefan: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim. Consultant; Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Consultant; Pfizer Inc. Speaker's Bureau; Sanofi. Research Support; Sanofi. Speaker's Bureau; Novo Nordisk, GlaxoSmithKline plc. Consultant; GlaxoSmithKline plc. A. Fritsche: Advisory Panel; Abbott. Speaker's Bureau; AstraZeneca. A.L. Birkenfeld: None. R. Jumpertz von Schwartzenberg: None.

Es wird über einen Fall einer entgleisten diabetischen Stoffwechsellage berichtet, bei dem die nichtmedikamentöse Basistherapie mittels Nutzung einer digitalen Gesundheitsanwendung (DiGA) zur Therapie herangezogen wurde. Durch die Verwendung einer DiGA konnte hier eine medikamentöse Therapieeskalation verhindert werden.