BACKGROUND:Despite recent progress in insulin delivery, carbohydrate counting is essential for determining prandial insulin doses and is, therefore, a key component of education for patients living with type 1 diabetes (PwT1D). This study aimed to evaluate the relationship between carbohydrate knowledge and glycemic control. METHODS:Carbohydrate knowledge was assessed using GluciQuizz, a validated, self-administered questionnaire totaling 36 points submitted to participants in the SFDT1 cohort. Glycemic control was determined using a 14-day time in range (TIR, 70-180 mg/dl) RESULTS: The median age of the 635 participants was 43 [interquartile range 32-54] years, T1D duration of 25 [14-36] years. The median overall GluciQuizz score was 25 [23-28]: 24 [21-27] among the 158 participants treated with multiple daily injections (MDI), 25 [22-27] among the 255 users of continuous subcutaneous insulin infusion (CSII), and 26 [24-29] among the 222 users of automated insulin delivery (AID) systems. Median TIR (%) was: overall 68 [55-77]; MDI 60 [50-72]; CSII 62 [49-73]; AID 75 [69-82]. The total GluciQuizz score was positively associated with TIR in the overall population (β ± SE = 0.04 ± 0.01; P < 0.001). This association remained significant in CSII users (β ± SE = 0.06 ± 0.02; P < 0.001) but not in those treated with MDI (P = 0.19) or AID (P = 0.55). CONCLUSIONS:Better carbohydrate knowledge is independently associated with improved glycemic control in PwT1D using CSII but not among those using MDI or AID systems. These findings highlight the continued relevance of structured nutritional education, particularly for individuals using CSII.

BACKGROUND:Automated insulin delivery (AID) systems improve glycemic control in people with type 1 diabetes (PwT1D), but evidence on effectiveness and safety in routine clinical practice over 2 years remains limited. METHODS:The Observatoire de la Boucle Fermée en France is a nationwide prospective observational study evaluating AID use in children and adults with type 1 diabetes in real-world conditions. The primary end point was time in range (TIR, 70-180 mg/dL). The primary analysis assessed the noninferiority of TIR at 24 months (M24) compared with 12 months (M12). Secondary analyses evaluated the superiority of glycemic outcomes at M24 versus baseline (M0), including HbA1c, continuous glucose monitoring metrics, and safety outcomes. A sensitivity analysis using a linear mixed-effects model examined the change in TIR between M0 and M24 according to baseline HbA1c (<8% vs. ≥8%), adjusted for age, sex, and diabetes duration. RESULTS:Among 2741 PwT1D who initiated AID therapy, 2225 (81.1%) had available data at M24. Median TIR at M24 was noninferior to M12 (70.0% [62.0-77.0] at both time points). Compared with baseline, TIR increased by 12.0% at M24 (P < 0.0001), and HbA1c decreased from 7.6% to 7.1% (P < 0.0001). AID discontinuation at M24 occurred in 2.1% of participants. The proportions of participants experiencing at least one episode of severe hypoglycemia over the preceding 12 months were lower at M24 than at baseline. A significant interaction between time of AID use and baseline HbA1c category was observed (P < 0.001). At M24, TIR remained higher in participants with baseline HbA1c <8% compared with those with baseline HbA1c ≥8% (P < 0.0001). CONCLUSIONS:In a large nationwide real-world cohort, AID use was associated with noninferior TIR between 12 and 24 months, significant improvements in glycemic outcomes compared with baseline, low rates of treatment discontinuation, and favorable safety outcomes.

Type 1 diabetes (T1D) remains associated with suboptimal glycemic control in a substantial proportion of individuals despite major advances in insulin formulations and technological systems. This apparent paradox highlights that glucose control in T1D is not determined by technology alone, but rather emerges from the interaction of multiple biological, therapeutic, behavioral, and psychosocial factors. In this structured narrative review, we examine the main determinants that continue to limit optimal control in real-world practice. Pathophysiological barriers include residual or absolute insulin deficiency, impaired counterregulatory responses, chronic or acute insulin resistance, hormonal changes related to puberty, the menstrual cycle and pregnancy, physical activity, and advanced renal disease. Therapeutic and technological progress has improved glycemic safety and time in range, yet insulin therapy remains an imperfect substitute for physiological insulin secretion, and the effectiveness of CGM, insulin pumps, connected pens, and AID systems remains highly dependent on sustained and appropriate use. Behavioral determinants such as treatment adherence, meal bolusing, carbohydrate estimation, physical activity management, and technology misuse continue to be major drivers of glycemic variability. In parallel, psychosocial factors, including eating disorders, anxiety, depression, stress, socioeconomic vulnerability, and shift work, strongly influence self-management and may further aggravate metabolic instability. These determinants interact dynamically and bidirectionally, creating self-reinforcing cycles that help explain why recommended glycemic targets remain difficult to achieve. Improving outcomes in T1D therefore requires moving beyond a technology-centered model toward an integrated, patient-centered approach that combines technological innovation with therapeutic education, psychosocial screening, and individualized care across the life course.

L’insulinothérapie automatisée est actuellement en plein essor. Elle représente une évolution majeure dans la prise en charge du diabète type 1. Cette modalité thérapeutique combine trois éléments: une pompe à insuline, une mesure continue du glucose en temps réel et un système algorithmique d’ajustement automatisé de l’administration d’insuline visant à optimiser le contrôle glycémique. De nombreuses questions se posent sur ses bénéfices et limites à long terme en routine clinique, son développement technologique, l’extension de ses indications, son rapport coût–efficacité, auxquelles des études vont devoir répondre. Le Réseau d’Investigation de l’Insulinothérapie Automatisée (R2IA) a été créé en France afin d’atteindre ces objectifs. Soutenu par le programme France 2030 et financé par l’Agence Nationale de la Recherche (ANR), coordonné par l’Assistance publique–Hôpitaux de Paris (AP–HP), ce réseau national vise à aider à faire travailler ensemble des centres hospitaliers publics et privés, des diabétologues d’exercice libéral, des chercheurs et des partenaires industriels, pour mettre en place des études et des observatoires multicentriques, générer des données de santé en vie courante et évaluer l’impact médicoéconomique de l’insulinothérapie automatisée.

Background: Effective glycemic control in diabetes management relies heavily on dietary carbohydrate knowledge. This study aimed to assess carbohydrate knowledge in individuals with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (itT2D) using the GluciQuizz tool. Methods: A total of 465 persons (96 with T1D, 153 with itT2D; 89 and 127 matched controls without diabetes, respectively) from the French NutriNet-Santé prospective cohort were included. Participants completed the GluciQuizz questionnaire, which evaluates carbohydrate knowledge across five domains: carbohydrate food recognition; carbohydrate food content; nutrition label reading; glycemic targets and hypoglycemia prevention and treatment; and carbohydrate content of meals. Results: The mean age ± standard deviation of participants with diabetes was 65.8 ± 11.2 years, 44.2% male, with a diabetes duration of 23.3 ± 12.9 years. T1D participants scored significantly higher on the GluciQuizz compared to those with itT2D (23.9 ± 5.0 vs. 17.5 ± 5.6, p < 0.001). In secondary analysis, T1D participants showed superior knowledge to their matched controls without diabetes, whereas itT2D participants showed similar knowledge to their matched controls without diabetes. Conclusions: T1D participants demonstrated the best carbohydrate knowledge compared to those with itT2D. Targeted educational interventions in itT2D populations may improve dietary management and clinical outcomes.

By 2050, diabetes care should transform into a human-centered, technology-augmented ecosystem focused on well-being. In this work, we first present a deliberately idealistic, forward-looking scenario in which advanced treatments, artificial intelligence, and integrated care delivery significantly reduce the burden on individuals with diabetes and enable person-centered health care. In this utopian vision, success in diabetes care is measured not only by glycemic metrics such as time in range but also by time in happiness, i.e., the quality time people spend unencumbered by their condition. Second, while such idealistic progress may be unlikely by 2050 due to socioeconomic and public health constraints, we build on existing gaps to outline a road map of co-designed solutions, health system reforms, and ethical innovations to move closer to it. This work serves as a "North Star" document that articulates the key values and next steps for achieving an ideal diabetes prevention and care ecosystem.

L’association entre diabète de type 2 et insuffisance cardiaque est aujourd’hui reconnue comme un enjeu majeur de santé publique. De nombreuses données indiquent que l’hyperglycémie chronique exerce des effets délétères directs sur le myocarde. Cette toxicité du glucose, ou glucotoxicité cardiaque, contribue au remodelage structurel et fonctionnel du cœur diabétique en favorisant l’hypertrophie myocardique, l’altération des protéines contractiles, et les anomalies de relaxation. Les travaux expérimentaux récents ont permis de mieux comprendre les mécanismes moléculaires impliqués, allant du stress oxydant à l’activation de voies métaboliques spécifiques. Cet article propose une mise au point sur les connaissances actuelles concernant le rôle de l’hyperglycémie dans la cardiomyopathie diabétique.

In preclinical PET/MR, attenuation correction (AC) uses a mix of pre-calculated attenuation maps accounting for animal cradles and organ segmentation obtained from whole-body MR using a volume coil. The development of X-nuclei such as 23Na or 31P may benefit from high sensitivity surface coils, which could lead to inaccurate PET quantification. In this study, we evaluated the benefit of a pre-calculated attenuation map including a dedicated cradle enclosing a surface coil for 18F-FDG PET imaging. We developed a 3D-printed cradle (DC) embedding a 20 mm surface coil made of PLA (polylactic acid), coated with a thin cap of epoxy. An attenuation map was generated using computed tomography and integrated into PET reconstruction. To validate AC, we compared PET images to those obtained using a conventional cradle and a volume coil (CC). Various image quality metrics were evaluated in various phantoms including a NEMA NU-4 (recovery coefficients (RC), uniformity (

Abstract Heart failure with preserved ejection fraction (HFpEF) accounts for nearly half of heart failure cases and is characterized by diastolic dysfunction, myocardial fibrosis, inflammation, and endothelial alterations. However, relevant preclinical models remain limited. As chest radiotherapy induces cardiac fibrosis and endothelial injury, we hypothesized that targeted cardiac irradiation could reproduce key features of HFpEF. Male Sprague‐Dawley rats were randomized to receive cardiac irradiation at 10 or 20 Gy, or no irradiation. Cardiac structure and function were assessed longitudina ly by echocardiography and invasive pressure‐volume Disclaimer: This is a confidential document. analysis, and exercise capacity was evaluated using a treadmil test. Myocardial fibrosis, inflammation, and oxidative stress were analyzed by histological and biochemical methods. Irradiated rats preserved systolic function but developed significant diastolic dysfunction, with increased left ventricular end‐diastolic pressure and prolonged relaxation time constant. Exercise tolerance was reduced by approximately 25% at 5 months. Histological analyses revealed increased interstitial and perivascular fibrosis with elevated co lagen I expression. CD68‐positive macrophage infiltration was markedly increased, whereas CD163‐positive ce ls were unchanged. Oxidative stress was evidenced by reduced superoxide dismutase activity and increased protein carbonylation. Targeted cardiac irradiation therefore induces a reproducible HFpEF‐like phenotype, providing a relevant model to investigate HFpEF pathophysiology and radiation‐induced cardiotoxicity.

Background The incidence of heart failure is approximately 2.5-fold higher in patients with type 2 diabetes than in non-diabetic individuals. Diabetic cardiomyopathy is characterized by diastolic dysfunction and left ventricular hypertrophy. The diabetic heart exhibits insulin resistance, leading to impaired glucose uptake and oxidation and, consequently, to intracellular glucose overload and glucotoxic stress. To further investigate the mechanisms underlying diabetic cardiomyopathy, we have previously examined the cardiac phenotype of lipodystrophic and severely insulin-resistant seipin knockout mice. These mice developed left ventricular hypertrophy associated with chronic activation of the hexosamine biosynthetic pathway, which promotes over-O-GlcNAcylation of cardiac proteins. Methods To assess the causal role of chronic activation of the hexosamine biosynthetic pathway in cardiac dysfunction in seipin knockout mice, we used adeno-associated virus-mediated cardiac overexpression of O-GlcNAcase, the enzyme responsible for removing O-GlcNAc moieties. Cardiac properties were evaluated by echocardiography. O-GlcNAcylated proteins were enriched using wheat germ agglutinin pull-down followed by proteomic analysis. Pharmacological inhibition and genetic modulation were used to investigate the role of β-catenin signalling. Results Cardiac overexpression of O-GlcNAcase corrected cardiac hypertrophy, as assessed by echocardiography, and improved insulin sensitivity in seipin knockout hearts. Proteomic analyses identified 28 proteins with increased O-linked N-acetylglucosamine modification in seipin knockout mice. Among these, β-catenin emerged as a candidate mediator, as expression of its target genes was increased in seipin knockout mice and normalized upon O-GlcNAcase overexpression. Increased β-catenin activity was associated with enhanced O-GlcNAcylation. Pharmacological inhibition of β-catenin using ICG-001 prevented cardiac hypertrophy in seipin knockout mice. Increased β-catenin O-GlcNAcylation and activity were also observed in two other murine models of diabetic cardiomyopathy. Selective enhancement of β-catenin O-GlcNAcylation was sufficient to induce hypertrophy in cultured cardiomyocytes. Conclusion These findings indicate that β-catenin O-GlcNAcylation contributes to cardiac remodelling in insulin-resistant states. This mechanism may extend beyond the seipin deficient specific model and could represent a potential target for the treatment of cardiac hypertrophy associated with type 2 diabetes.

BACKGROUND:Drug clinical trial participants should be fairly compensated for inconvenience while avoiding undue inducement. However, European regulations provide limited operational guidance on when compensation should be offered and how it should be determined. The INDEM3 project aims to develop a decision-support tool for sponsors and ethics committees, beginning with an assessment of exploratory public perspectives in France. METHODS:An exploratory population-based survey was conducted using a self-administered questionnaire with an estimated completion time of approximately seven minutes. A minimum of 60 individuals was targeted, with or without chronic condition or prior research experience. The questionnaire assessed general views on financial compensation and responses to three fictional drug clinical trial scenarios varying in risk, burden, and direct benefit. Distribution occurred in healthcare settings and via social networks. RESULTS:Between March 2024 and January 2025, 65 responses were collected. Most respondents (75%) supported financial compensation for participation in drug clinical trials. The most frequently cited criteria were exposure to a new investigational drug (65%), invasive procedures (63%) and experiencing income lost (63%), and absence of direct personal benefit (28%). For the scenarios, the proportion of respondents supporting compensation and the median proposed amounts were as follows: Scenario 1 (no direct benefit, high burden, low uncertainty): 81%; €300 [€140-300]. Scenario 2 (some direct benefit, low burden, moderate uncertainty): 48%; €150 [€100-200]. Scenario 3 (some direct benefit, high burden and high uncertainty): 60%; €500 [€200-1,000]. CONCLUSION:Respondents in this exploratory French survey largely supported financial compensation for participation in drug clinical trials. Compensation is perceived as a proportionate ethical and social recognition of uncertainty, burden and lack of direct benefit. These findings provide an empirical basis for developing transparent tools to guide compensation decisions.

AIMS:Diabetes distress (DD) is common and evolves heterogeneously over time. We aimed to estimate minimal clinically important differences (MCID) for the Problem Areas in Diabetes (PAID) scale and its sub-dimensions and to identify predictors of worsening over 1 year in people with type 1 diabetes (PwT1D). MATERIALS AND METHODS:MCIDs for PAID total and sub-dimension scores were derived using the standard error of measurement approach. One-year DD worsening, stability and improvement were defined using the ±1 MCID threshold. We analysed changes in DD using data from the SFDT1 cohort. Logistic regression models identified predictors of worsening for PAID total and sub-dimensions, adjusting for baseline DD, age, sex and social vulnerability. RESULTS:We analysed data from 2457 adults with type 1 diabetes (51.6% female, 41.3 years old (SD 14.0), 23.7 years diabetes duration (SD 14.0)). The MCID for PAID total was 5.0 points, with sub-scale MCIDs ranging from 8.5 (emotional distress) to 14.3 points (management distress). Over 1 year, 42% experienced clinically meaningful worsening of total DD, 30% improved and 28% remained stable. Worsening of total PAID was associated with social vulnerability, HbA1c increase and treatment burden. Distress trajectories varied across sub-dimensions, with emotional distress and burnout most frequently worsening (34% each); initiation of automated insulin delivery was solely associated with lower odds of worsening on the burnout dimension. CONCLUSIONS:This study provides MCID values for the PAID scale and its sub-dimensions. The high temporal variability observed suggests a need for regular monitoring and clinically informed assessment of DD in PwT1D.

BACKGROUND:Automated insulin delivery (AID) systems have been shown to improve glycaemic outcomes in people with type 1 diabetes managed with insulin pump therapy. No randomised studies have evaluated the benefits of tubeless AID in both adults and children with suboptimal glycaemia compared with multiple daily injections. We aimed to evaluate the safety and efficacy of a tubeless AID system compared with multiple daily injections in this population. METHODS:RADIANT was a multicentre, international, parallel-group, open-label, randomised, controlled trial done in 19 hospitals in the UK, Belgium, and France. Participants aged 4-70 years with type 1 diabetes managed with multiple daily injections and continuous glucose monitoring and who had HbA1c levels of 7·5-11% (58-97 mmol/mol) were randomly assigned (2:1) to tubeless AID or control (multiple daily injections) using a permuted-block design. Participant and study teams were not masked to group allocation. The primary outcome was the adjusted between-group difference in HbA1c at 13 weeks assessed for superiority. Primary and safety outcomes were assessed in the modified intention-to-treat population (all randomly assigned participants). The study is registered with ClinicalTrials.gov, NCT05923827, and has been completed. FINDINGS:Between Sept 11, 2023, and April 26, 2024, 188 participants were randomly assigned to the AID group (n=125) or the control group (n=63). The AID group had a greater reduction in HbA1c, from 8·1% (SD 0·7; 65 mmol/mol [SD 7·7]) at baseline to 7·2% (0·6; 55 mmol/mol [6·6]) at 13 weeks, compared with the control group, from 8·1% (0·6; 65mmol/mol [6·6]) at baseline to 8·0% (0·7; 64 mmol/mol [7·7]) at 13 weeks, with an adjusted mean difference of -0·8% (95% CI -1·0 to -0·6; -8·7 mmol/mol [95% CI -10·9 to -6·6]; p<0·0001). During the 13-week trial, no episodes of severe hypoglycaemia or diabetic ketoacidosis occurred in either treatment group. 39 adverse events were reported among 28 participants in the AID group, and three adverse events among three participants in the control group. Two serious adverse events (Kawasaki disease and acute coronary syndrome) occurred in the AID group unrelated to the study device or procedure. INTERPRETATION:Results from this trial show the clinical efficacy of direct transition from multiple daily injections to tubeless AID in adults and children with type 1 diabetes, with no safety concerns, supporting AID as a therapeutic option within standard of care for people with type 1 diabetes. FUNDING:Insulet Corporation.

L’activité physique, essentielle pour la prise en charge du diabète de type 1, reste un défi en raison de la variabilité glycémique et du risque d’hypoglycémie. Si les systèmes de délivrance automatisée d’insuline améliorent le contrôle glycémique au repos, ils peinent lors de l’effort à cause de la cinétique lente de l’insuline sous-cutanée et du délai de mesure des capteurs. La gestion optimale repose sur l’anticipation : il est recommandé d’élever la cible glycémique 1 à 2heures avant l’effort pour réduire l’insuline active. Si l’exercice suit un repas, une réduction du bolus de 25 à 33 % est nécessaire. Un enjeu crucial est d’éviter la « contre-attaque » insulinique : des apports glucidiques excessifs ou des corrections manuelles peuvent pousser l’algorithme à délivrer de l’insuline par erreur, provoquant une hypoglycémie réactionnelle. Dans la vie courante, l’adhésion aux recommandations est faible, avec moins de 10 % des patients ajustant proactivement leur traitement. L’éducation doit donc porter sur un « désapprentissage » des anciens réflexes au profit d’une stratégie adaptée à la logique algorithmique. L’objectif est de maintenir le patient dans une zone de « performance et de sécurité » individualisée, en calibrant finement les apports glucidiques selon les flèches de tendance du capteur.

Diabetes remains a global public health concern, with increased prevalence and a significant economic burden. Yet, most studies do not differentiate between type 1 (T1D) and type 2 diabetes (T2D), despite distinct clinical trajectories and care needs. This study aimed to provide up-to-date data on healthcare use and diabetes care-related costs in France from 2011 to 2019 by type of diabetes. A 10-year retrospective study was conducted on adults identified with T1D and T2D between 1 January 2010, and 31 December 2019, in a 1/10th sample of the French nationwide claims database (SNDS, Système National des Données de Santé). In the hospital database, reimbursement data are available only from 2011, so resource use and cost analyses were conducted starting from 2011 to 2019 in this present study. Diabetes was identified through hospital diagnoses, specific treatments, and/or long-term conditions. A machine-learning algorithm was specifically developed through a linkage between SNDS data and primary data from a network of French general practitioners to better predict the type of diabetes. In 2019, among the 290,486 treated individuals, 12,102 (4.2

Introduction and Objective: Hybrid closed loop (HCL) therapy is now standard of care for people with type 1 diabetes, yet important differences in healthcare systems and roll-out policies exist across countries. The EUROLOOP project aims to harmonise real-world data (RWD) collection and describe clinical and patient-reported outcomes (PROs) of HCL across Europe. Methods: Representatives from European databases contributed available aggregated clinical and PROs data from their national or regional HCL cohorts. Outcomes were reported by country. Results: A total of 5,376 HCL users from 5 countries (Belgium, England, France, Italy, Spain) were included. Country-specific median ages ranged 40-43 years, diabetes duration 20-26 years; most users were female. France contributed cross-sectional post-HCL data, while others provided pre- and post-HCL data (country-specific median HCL duration ranged 6-36 months). UK HCL users had higher baseline HbA1c and lower achieved time in range compared with other countries (Table 1). Conclusion: The EUROLOOP project shows a practical approach to harmonising RWD for HCL across Europe. This framework enables cross-country comparisons and real-world insights into glycaemic outcomes and PROs. Integrating RWD across databases maximises their value, improves transferability of findings, and supports research across diverse populations and healthcare systems. Disclosure A. Liarakos: Other - Speaker fees; Current; Dexcom, Inc. Advisory Panel; Current; Dexcom, Inc. Research Support; Current; Association of British Clinical Diabetologists. C. Quiros: Speaker's Bureau; Ended; Medtronic. Advisory Panel; Ended; Insulet Corporation. Speaker's Bureau; Ended; Roche Diabetes Care. J. De Meulemeester: Research Support; Current; Dexcom, Inc., Medtronic, Tandem Diabetes Care, Inc. Speaker's Bureau; Ended; Dexcom, Inc. R. Hilbrands: Advisory Panel; Current; Lilly Diabetes. Speaker's Bureau; Ended; Medtronic, Menarini Group. L. Valgaerts: Research Support; Current; Vertex Pharmaceuticals Incorporated, Medtronic. D. Pitocco: None. E. Cosson: Consultant; Current; AstraZeneca, Abbott Diabetes, Boehringer Ingelheim International GmbH, Lilly, Dexcom, Inc., Medtronic, Novo Nordisk, Novartis AG, Roche Diagnostics. G. Fagherazzi: Consultant; Ended; Lilly Diabetes. Board Member; Ended; Sanofi. Consultant; Ended; AbbVie Inc., AstraZeneca. M. Joubert: Advisory Panel; Current; Abbott. Consultant; Current; Tandem Diabetes Care, Inc. Advisory Panel; Current; Ypsomed AG. Consultant; Current; Roche Diabetes Care. Advisory Panel; Current; Dexcom, Inc. Consultant; Current; Glooko, Inc. Advisory Panel; Current; Insulet Corporation. Consultant; Current; LifeScan. Advisory Panel; Current; Lilly Diabetes, Medtronic. G. Aguayo: 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. F. Giorgino: Advisory Panel; Current; Abbott. Consultant; Current; Lilly, Novo Nordisk. Advisory Panel; Current; AstraZeneca, Medtronic. Research Support; Current; Roche Diabetes Care. Advisory Panel; Current; LifeScan, Sanofi. Advisory Panel; Ended; Merck Sharp & Dohme Corp. Advisory Panel; Current; Boehringer Ingelheim International GmbH. E. Renard: None. C. Irace: Board Member; Ended; Abbott Diabetes. Board Member; Current; Novo Nordisk. Speaker's Bureau; Ended; Medtronic. Board Member; Current; Roche Diabetes Care. Speaker's Bureau; Ended; Novo Nordisk, Lilly. C. De Block: Advisory Panel; Current; Abbott Diagnostics, Eli Lilly and Company. Speaker's Bureau; Current; Eli Lilly and Company, Insulet Corporation, Medtronic. Advisory Panel; Current; Indigo Diabetes. Research Support; Current; Medtronic, Indigo Diabetes. Advisory Panel; Current; Novo Nordisk A/S. P. Gillard: Speaker's Bureau; Current; Medtronic. Speaker's Bureau; Ended; Abbott. Research Support; Current; Insulet Corporation, Tandem Diabetes Care, Inc. Advisory Panel; Ended; Tandem Diabetes Care, Inc. Advisory Panel; Current; Ypsomed AG, Dexcom, Inc. Speaker's Bureau; Current; Ypsomed AG. Speaker's Bureau; Ended; Insulet Corporation, Bayer AG. P. Beato Vibora: None. E. Wilmot: Advisory Panel; Current; Abbott Diabetes. Speaker's Bureau; Current; Dexcom, Inc., Eli Lilly and Company. Advisory Panel; Current; Insulet Corporation. Consultant; Current; Roche Diabetes Care. Speaker's Bureau; Ended; Sanofi, Novo Nordisk. Advisory Panel; Ended; Medtronic. Speaker's Bureau; Ended; Ypsomed AG. Advisory Panel; Ended; Tandem Diabetes Care, Inc. Speaker's Bureau; Current; Abbott Diabetes. Research Support; Current; Abbott Diabetes. Speaker's Bureau; Current; Insulet Corporation. Research Support; Current; Insulet Corporation. Advisory Panel; Ended; Sanofi. Research Support; Current; Novo Nordisk. Consultant; Ended; Embecta. Research Support; Ended; Embecta. Advisory Panel; Ended; Sinocare. P. Choudhary: Advisory Panel; Current; Medtronic. Speaker's Bureau; Current; Medtronic. Research Support; Current; Medtronic. Consultant; Current; Medtronic. Speaker's Bureau; Current; Abbott. Advisory Panel; Current; Abbott. Research Support; Current; Abbott. Speaker's Bureau; Current; Dexcom, Inc. Advisory Panel; Current; Dexcom, Inc. Research Support; Current; Dexcom, Inc. Consultant; Current; Dexcom, Inc. Speaker's Bureau; Current; Insulet Corporation. Advisory Panel; Current; Insulet Corporation. Research Support; Current; Insulet Corporation. Consultant; Current; Insulet Corporation. Speaker's Bureau; Current; Sanofi. Consultant; Current; Sanofi. Speaker's Bureau; Current; Lilly. Advisory Panel; Current; Lilly. Consultant; Current; Lilly. Advisory Panel; Current; Ypsomed AG. Advisory Panel; Ended; Embecta. Speaker's Bureau; Current; Roche Diabetes Care. Research Support; Current; Roche Diabetes Care. Consultant; Current; Roche Diabetes Care. Advisory Panel; Current; Vertex Pharmaceuticals Incorporated. Consultant; Current; Glooko, Inc., Cambridge Mechatronics Ltd, vTv Therapeutics. J. Riveline: Consultant; Current; Abbott, Ypsomed AG, Lilly, Novo Nordisk, Sanofi, Dexcom, Inc., Insulet Corporation, Medtronic, Air Liquide International.