搜索结果：Journal of diabetes science and technology

Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (-11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by -29·9% (-33·6 to -25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Gates Foundation, St Jude Children's Research Hospital.

Usage of diabetes technology by people living with diabetes does help them a lot with their daily diabetes management burden. Evidence for the efficacy of using systems for continuous glucose monitoring, automated insulin delivery, and so on has largely been derived from randomized controlled trials, which are pivotal for regulatory approvals and reimbursement decisions. However, evidence obtained from real-world usage of technology is crucial as it confirms the benefits also under such conditions. Data obtained from a detailed survey answered by health care professionals and people living with diabetes provides further insights into the reality of usage. They also help to understand the hurdles in daily life and what can be done to overcome these. In this special theme issue, a set of specific topics is addressed that are of academic and clinical importance: dropouts from automated insulin delivery systems, technology use in people living with type 2 diabetes, technology and aging, smart insulin pens, and green diabetes. The data basis for the analysis presented in these manuscripts is from Germany, Austria, and Switzerland. In the future, data from other European Countries will complement the insights gained. This will help to understand the similarities and differences between these countries, which have specific differences in their health care systems. This can lead to subsequent activities in the different countries to improve the clinical care of people living with diabetes.

Diabetes technologies, such as continuous glucose monitoring (CGM), insulin pumps, and automated insulin delivery (AID) systems, are increasingly used by people with type 2 diabetes (PWT2D), with growing clinical evidence supporting their therapeutic benefit. To describe the extent of adoption, perceived benefits, and future expectations, both health care professionals (HCPs) and PWT2D data from the dt-report 2025 were analyzed. From November to December 2025, HCP and PWT2D participated in the dt-report providing their attitudes, expectations, and predictions regarding the use of diabetes technology in type 2 diabetes. Frequencies from specific responses were analyzed. Data from 1078 HCPs and 450 PWT2D from the DACH region were analyzed for questions regarding the use of technology in type 2 diabetes. Continuous glucose monitoring was the most widely endorsed technology across both groups, with 58% of the survey participants using a CGM, and 1% using a pump. Health care professionals estimated 87% of PWT2D on intensive insulin therapy would benefit from CGM and saw indications among non-intensive insulin users (62%) and those on oral therapies (55%). Future use of CGM and AID systems was anticipated by both HCPs and PWT2D, including many currently not using such systems. Smart pens and stand-alone insulin pumps were viewed less favorably. Reported barriers included lack of awareness, reimbursement limitations, digital literacy, and usability concerns. The findings indicate growing openness toward diabetes technologies among PWT2D and broader perceived indications among HCPs. However, uptake remains limited, particularly outside of intensive insulin therapy. These insights are of relevance for future clinical guidance, access strategies, and patient education.

Quantifying the effect of meal composition (MC) on postprandial glucose excursions would allow optimizing insulin therapy, accounting for fat and protein that can affect gastric retention (GR), glucose rate of appearance (Ra), and insulin sensitivity (SI). Such variables can be estimated from continuous glucose monitor (CGM) and continuous subcutaneous insulin infusion (CSII) data using the Minimally-Invasive Oral Minimal Model (MI-OMM). In this work, we aim to quantify the effect of MC on those variables by applying the MI-OMM on a data set of prandial CGM and CSII profiles where MC information was available. A total of 120 individuals with type 1 diabetes (age = 15.5&#x2009;&#xb1;&#x2009;11.5 years, weight = 51.3&#x2009;&#xb1;&#x2009;28.0 kg) were monitored under free-living conditions while using CGM and CSII, and MC was carefully recorded. We extracted 353 CGM and CSII traces using predefined criteria and classified them into low or high fat content and low or high protein content. Finally, the MI-OMM was used to estimate GR, Ra, and SI in each meal. MI-OMM was able to fit CGM profiles and provided precise and physiologically plausible parameter estimates. Comparison among different classes of meals showed that a high content of fat and protein in the meal significantly slowed both GR (P < .01) and Ra (P < .01), and reduced SI (P < .05). In this work, the effect of MC on postprandial glucose excursion was quantified in real-life conditions with the help of a model-based methodology. These results are usable for redesigning current insulin therapies, accounting for the presence of fat and protein in meals.

Reducing health disparities in later life is an important yet challenging agenda, particularly in urban areas. The objective of this study was to examine the effectiveness of the Health and Wellness Program for Seniors (HWePS), a technology-enhanced, multilevel, integrated health equity intervention, on the health and well-being of older adults residing in urban, low-income communities. HWePS was a prospective, non-randomized, cluster-allocated quasi-experimental study conducted over 12&#xa0;months in an intervention and a control neighborhood in Seoul, South Korea. Guided by proven models, the HWePS intervention includes four key components: a health literacy program tailored to individual and community needs, personalized self-care management with nurse coaching and peer support, a community initiative promoting healthy living and aging, and information and communication technology systems. Data were collected before and after the 12-week self-care program, which was the core component of a broader 12-month intervention period. The primary outcomes were self-reported health status and health-related quality of life. Secondary outcomes included walking practice, diabetes awareness, stress perception, and self-efficacy, among others. The analysis revealed a significant group-by-time interaction for self-rated health (B&#xa0;=&#xa0;0.48, SE&#xa0;=&#xa0;0.22, p&#xa0;=&#xa0;0.0277), favoring the intervention group. For quality of life, only the main effects of group and time were significant, with no significant interaction (p&#xa0;=&#xa0;0.8474). Among the secondary outcomes, walking practice rate (B&#xa0;=&#xa0;0.93, SE&#xa0;=&#xa0;0.19, p&#xa0;<&#xa0;0.0001), diabetes awareness (B&#xa0;=&#xa0;0.39; SE&#xa0;=&#xa0;0.17; p&#xa0;=&#xa0;0.0227), stress perception (B&#xa0;=&#xa0;-1.24, SE&#xa0;=&#xa0;0.31, p&#xa0;<&#xa0;0.0001), and self-efficacy (B&#xa0;=&#xa0;1.91, SE&#xa0;=&#xa0;0.90, p&#xa0;=&#xa0;0.0354) were significantly improved compared to those of the control group. HWePS, an older people-empowering, community-mobilized health equity intervention, showed improvements in self-rated health and key health behaviors, providing preliminary evidence of its potential to reduce health disparities in a low-income urban community during the coronavirus disease 2019 pandemic. ISRCTN29103760; Ethical approval: SNU IRB No. 2011/002-016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395967/.

The number of adults with diabetes and older age is increasing, yet little is known about age-related differences in real-world diabetes technology use. This analysis examines how uptake, clinical outcomes, and user experience vary across age groups in people with type 1 or type 2 diabetes. Self-reported data from 2056 individuals with diabetes in Germany, Austria, and Switzerland who completed the diabetes technology report 2024/2025 survey were analyzed. Age-related trends in the use of continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII), and automated insulin delivery (AID) were assessed using generalized additive and segmented logistic regression models. Outcomes included HbA1c, diabetes distress (PAID-5), severe hypoglycemia (SH), and, among AID users, satisfaction. Among people with type 1 diabetes, CGM usage was consistently high across age groups (eg, 94% in 20-29 years; 92% in &#x2265;70 years). Automated insulin delivery usage peaked in adolescents (81% in 10-19 years) and declined to 36% in adults &#x2265;70 years. In type 2 diabetes, CGM use increased with age (48% in 35-44 years; 72% in &#x2265;70 years). The HbA1c remained stable over the age span (&#xb1;0.25%). Diabetes distress declined with age (Problems Areas in Diabetes Ouestionnaire - 5 Items (PAID-5): 7.8 in <30 vs 4.2 in &#x2265;70 years). The risk of SH did not increase with age; among CSII users, older participants had lower odds of SH (OR 0.03, p = .001). Automated insulin delivery satisfaction was highest in adults aged 60 to 69 years (88.7/100) and lowest in adolescents (79.1/100). Diabetes technologies are widely used and well tolerated across age groups. Older adults benefit comparably, but barriers to AID use remain.

Diabetes technology use is increasing, yet disparities remain in adoption among people with type 1 diabetes (PwT1D). The Best Practice Advisories to Reduce Inequities in Technology Use ("BPA-TECH") study is a multi-site quality improvement (QI) initiative that seeks to develop, deploy, and evaluate a BPA aimed at standardizing prescribing of diabetes technologies. This mixed-methods study sought feedback from PwT1D, their care partners, and diabetes clinicians through the T1D Exchange QI Collaborative (T1DX-QI) using electronic surveys, interviews, and focus groups to develop and refine the BPA. The T1DX annual survey with general BPA questions was completed by 56 participating centers (38 pediatric and 18 adult), and 31 diabetes care clinicians from eight T1DX-QI centers (five pediatric and three adult) participated in focus groups. An online survey was completed by 101 PwT1D and/or their care partners, followed by structured interviews with nine adult PwT1D and ten care partners. In response to the annual survey, 48% of pediatric and 28% of adult centers thought a BPA would be useful for increasing automated insulin delivery (AID) use. During focus groups, clinicians expressed concerns about workflow integration and alert fatigue. On surveys, most PwT1D and care partner stakeholder groups (96%) said a BPA would help remind diabetes clinicians to discuss technology with patients, and 77% agreed that a BPA could help PwT1D use these technologies, recommending a cadence of every three months. Successful BPA development and implementation requires addressing clinician concerns about workflow and alert fatigue, while aligning with PwT1D and care partners' expectations for the cadence of conversations on AID systems.

Automated identification of postprandial glucose responses (PPGR) from continuous glucose monitoring (CGM) profiles may detect early dysglycemia in people without diabetes. However, no standard approach for this task currently exists. We developed a wavelet transform-based AI algorithm to identify PPGRs using only CGM data. The algorithm was evaluated on a public CGM dataset of 25 normoglycemic adults and three independent validation cohorts (n = 65 total) with a mix of normoglycemia and prediabetes. Performance metrics included mealtime prediction error and total and incremental areas under the PPGR curve (tAUC and iAUC, respectively). Associations between AI-derived PPGR parameters and clinical markers such as HbA1c and fasting glucose were also examined. In the public dataset, 25 participants (age 40 &#xb1; 14 years, BMI 26 &#xb1; 6 kg/m2, HbA1c 5.4 &#xb1; 0.4%) provided 3 &#xb1; 1 days of paired CGM data and ground-truth mealtimes. The algorithm predicted PPGR start time with a median error of 10 [IQR: 4, 19] minutes relative to ground-truth mealtimes. Postprandial glucose response parameters including tAUC and iAUC derived using ground-truth mealtimes versus AI-predicted mealtime were similar (all P > .1), indicating the algorithm faithfully captured PPGR characteristics. In adjusted analysis, AI-derived PPGR iAUC was independently associated with laboratory markers including HbA1c (&#x3b2; = 0.57 [95% CI: 0.19, 0.95], P = .006) and fasting glucose (&#x3b2; = 0.52 [95% CI: 0.12, 0.92], P = .013). Algorithm performance remained consistent across the three validation cohorts. The wavelet AI algorithm accurately identified PPGRs from CGM data in people without diabetes, offering a novel automated approach to monitor early signs of postprandial dysglycemia in this population.

Type 2 diabetes mellitus (T2DM) has emerged as a pressing global health challenge, and tobacco exposure constitutes a major modifiable risk factor-particularly among middle-aged and elderly populations. However, a comprehensive understanding of the global burden of tobacco exposure-attributable mortality and disability-adjusted life years (DALYs) in this demographic remains limited. This study is aimed at quantifying the global burden of deaths and DALYs attributable to tobacco exposure among middle-aged and elderly individuals with T2DM from 1990 to 2021. Simultaneously, this study assesses disparities across regions, genders, and age groups; examines associations with the sociodemographic index (SDI); and forecasts trends in disease burden from 2022 to 2042. Utilizing data from the Global Burden of Disease (GBD) 2021 study, which encompasses 204 countries and territories, we assessed burden counts and rates per 100,000 population among individuals aged 55 years and older. We further computed the estimated annual percentage changes (EAPCs) and applied age-period-cohort (APC) modeling, frontier analysis, and Bayesian age-period-cohort (BAPC) forecasting models to enable comprehensive analysis. Globally, tobacco exposure-attributable T2DM deaths increased by 101.10%, and DALYs rose by 140.38%, though the mortality rate decreased by 9.13%. Regions with middle and low-middle SDI shouldered the highest burden of tobacco-attributable T2DM. High-SDI regions demonstrated the most substantial declines in burden rates, whereas low-middle-SDI regions experienced the largest relative increases. Males exhibited greater mortality and DALYs counts compared to females, while females showed more pronounced reductions in burden trends. APC modeling further indicated that advancing age correlated with elevated risks of tobacco exposure-attributable T2DM outcomes, whereas younger birth cohorts showed lower risks. Frontier analysis identified middle-SDI countries (e.g., Kiribati) as exhibiting the greatest deviation from optimal performance. Projections through 2042 indicate that the mortality rate is projected to continue declining: It will reach 9.25 per 100,000 aged &#x2265;&#x2009;55 population in 2030 (95% CI: 8.64-9.85) and 8.57 per 100,000 aged &#x2265;&#x2009;55 population in 2042 (95% CI: 6.89-10.24). Tobacco exposure-attributable T2DM burden demonstrates marked variations across geographic regions, sex, and age groups, highlighting the imperative for targeted interventions in low-middle-SDI regions. Tobacco control strategies from high-SDI regions serve as scalable models for mitigating this preventable disease burden.

Heart failure (HF) is a complex clinical condition requiring resource-intensive management and substantial health expenditure. The adverse economic impact of medical care on patients or financial burden is increasingly recognised as a significant non-clinical entity affecting HF management in low- and middle-income countries (LMIC). We explored the factors associated with Financial Burden (FB) in HF patients in India. We recruited HF patients from 21 hospitals across India, selected to reflect regional diversity and varying stages of epidemiological transition. Trained personnel collected clinical and economic data using a validated and structured questionnaire. Expenditures were recorded in Indian rupees (INR) and converted to international dollars (INT$). We recruited 1,859 participants. Nearly one-third of participants (30.2%) were women. The mean age was 55.9 (11.3) years, and the mean duration of formal education was 11.3 (3.8) years. Health insurance coverage was reported in one-third (32.2%) of the study population. The average annual out-of-pocket (OOP) expenditure was INR 1,06,566 (INT$ 4,709.10), constituting 92.6% (95% CI: 92.5-92.7) of the total health expenditure. Compared to the previous year, a decline in monthly income was reported by 32.3% of individuals and 36.2% of households. Catastrophic health spending (CHS) and distress financing (DF) were observed in 37.7% (35.5-39.9) and 17.7% (15.9-19.4) of the households, respectively. However, CHS and DF were lower [30.8% (26.2-35.4) and 13.6% (10.2-17.0), respectively] among those with health insurance compared to the uninsured [40.3% (37.6-43.0) and 18.9% (16.7-21.1), respectively]. Seven out of 10 HF patients in India lack financial health protection. OOP expenditures, accounting for over 90% of total health spending, contribute significantly to economic distress in HF patients. Financial burden, affecting more than one-third of HF patients, carries profound implications for individual well-being. Addressing this financial burden, including CHS and DF, is essential for improving clinical outcomes and ensuring health equity.

The rapid rise of diabetes technology has markedly improved glycemic outcomes, quality of life, and empowerment of people living with diabetes (PwD). However, the increased use of devices such as continuous glucose monitoring systems, insulin pumps, and smart pens has also introduced significant environmental concerns, contributing to waste from plastic, electronics (e-waste) and packaging, and greenhouse gas emissions. In this paper, we describe results from an online survey conducted in Germany, Austria, and Switzerland, between November and December 2024, focused on the level of concern PwD have, regarding the environmental impacts of single-use medical device, supplies and packaging, resulting from diabetes treatment, and whether these considerations influence technology choices. Among 1934 PwD surveyed, 1332 (69%) favored more reusable devices, and 865 (45%) expressed concern about packaging waste. However, environmental factors ranked far below safety, effectiveness, and usability when selecting diabetes technologies. Expecting PwD to drive substantial environmental improvements is therefore neither realistic nor fair given prevailing priorities on safety and outcomes. Meaningful progress toward greener diabetes care will depend on manufacturers, policymakers, and healthcare systems embracing eco-design, establishing recycling infrastructure, and integrating sustainability into regulatory and reimbursement frameworks. Only through coordinated efforts can optimal diabetes management be achieved alongside environmental stewardship.

Diabetes poses a major global public health challenge, carrying significant economic implications worldwide. In China, the ongoing implementation of Diagnosis Related Groups (DRG) payment reforms, especially within Traditional Chinese Medicine (TCM) contexts, is critical in improving diabetes patient care and alleviating associated economic burdens. We examined 2,804 hospitalized diabetes patients at Qingyang City Hospital of Chinese Medicine in Gansu Province from 2017 to 2022. Using univariate and interrupted time-series (ITS) analyses, we compared patient visit data, healthcare-related costs, and length of stay pre- and post-DRG reform. Following DRG reform at Qingyang City Hospital of Chinese Medicine, significant differences were noted in patients' age, visit times, type of diabetes, complications and comorbidities, use of Chinese medicine diagnostic and therapeutic equipment, and surgeries and operations, compared with the pre-reform period (p&#x202f;<&#x202f;0.05). Post-reform, there was a noteworthy decrease in hospitalization cost and Western medicine cost, and TCM treatment cost (p&#x202f;<&#x202f;0.05), while Chinese medicine cost remained stable but the overall cost level increased (p&#x202f;>&#x202f;0.05). Additionally, there was a slight reduction in length of stay after the reform, although this change did not reach statistical significance (p&#x202f;>&#x202f;0.05). DRG reform significantly reduces hospitalization cost, TCM treatment cost, and Western medicine cost for diabetes patients in TCM hospitals. However, its impact on Chinese medicine cost and length of stay is limited. Future reforms should capitalize on the unique strengths of TCM treatment, enhance cost management strategies, and focus on minimizing length of stay and medical expenses while ensuring effective patient care.

To develop a chain mediation model to elucidate the relationship among physical performance, instrumental activities of daily living (IADL), regular exercise, and cognitive function among older adults who are comorbid with diabetes mellitus and hypertension (OA-DM&HTN). A total of 656 participants were investigated with the Mini-Mental State Examination, the Short Physical Performance Battery, the Instrumental Activities of Daily Living, and a questionnaire on regular exercise frequency between January and September 2022. Sequential multiple mediation models were conducted to analyze the data. The average age of the participants was 73.47 &#xb1; 7.40 years, and 49.24% (n = 323) of participants were female. The average cognitive function score was 22.36 &#xb1; 6.14, and 32.62% (n = 214) of participants exhibited cognitive impairment. Cognitive performance exhibited significant associations with demographic factors, including gender, age, marriage status, educational background, and income level (p < 0.05). Chain mediation analysis indicated that physical performance directly predicted cognitive function (&#x3b2; = 0.525, 95% CI: 0.000-1.050); physical performance had indirect effects mediated by IADL (&#x3b2; = 0.917, 95% CI: 0.635-1.230) and regular exercise (&#x3b2; = 0.076, 95% CI: 0.003-0.180). A significant chain-mediating effect involving both IADL and regular exercise was also observed on the relationship between physical performance and cognitive function (&#x3b2; = 0.034, 95% CI: 0.002-0.071). Physical performance is a significant predictor of cognitive function, and it can also affect cognitive function through the independent or chain-mediating effects of IADL and regular exercise among OA-DM&HTN. Therefore, to delay cognitive decline among OA-DM&HTN, it is essential to provide tailored functional training, encourage improvement in IADL, and promote regular exercise among OA-DM&HTN.

Suboptimal inpatient glycemia is associated with adverse outcomes, including infection, length of stay, and hospitalization costs. Interventions to improve inpatient glycemia may benefit from standardization of in-hospital glycemic measurement and reporting."Glucometrics," as coined by Goldberg et al (2006), proposes models and metrics that allow quantitative inpatient glycemic data analysis. This systematic review investigates the actual use of "glucometric" terminology and its derivations since conception. Original research articles on "glucometrics" and its derivations in inpatient contexts, published between 2006 and 2023, were searched in five databases. Studies were screened and extracted through PRISMA-compliant review software (Covidence&#xae;) and systematically reviewed. Of 767 studies identified, 44 were included for final review. Study settings included non-critical care wards (n=19), critical care (n=6), and both (n=13). Of the Goldberg models, "patient-day" was most used (n=33). Most studies (n=30) referred to "glucometrics" per the original description. An increase in the introduction of new metrics (e.g., time-weighted averages, adverse glycemic days, and glucose excursions) was seen over the study period, as well as an increase in the use of "glucometric" to refer to glycemic measurement/reporting in general.Significant variation in thresholds defining hyperglycemia/hypoglycemia existed between studies, where hyperglycemia ranged between 140 and 432 mg/dL (most commonly 300 mg/dL), while the hypoglycemia ranged between 40 and 70 mg/dL (most commonly 70 mg/dL). This systematic review provides insights into contemporary use of glucometric terminology, highlighting the lack of consensus on a standardized approach toward analyzing inpatient glycemia, and the need for glucometric harmonization to improve inpatient glycemia and diabetes care.

Automated insulin delivery (AID) systems can significantly improve glycemic outcomes in people with type 1 diabetes (PWT1D). Despite their clinical efficacy, little is known about their uptake in clinical care, or about the perspectives and experiences of health care professionals (HCPs) and people with diabetes (PWD) with this relatively new technology.Furthermore, research is limited on broader populations, such as people with type 2 diabetes (PWT2D) and cross-country comparisons. This study analyzes data from the dt-report 2025, a multinational online survey of PWD and HCPs from Germany, Austria, Switzerland, and Spain, which was conducted at the end of 2024. The surveys assessed, among others, AID relevance, indications, barriers, daily use, satisfaction, and support needs. In total, 1294 HCPs and 2535 PWD from Germany, Austria, Switzerland, and Spain took part. Health care professionals identified fully closed-loop systems as the most promising future therapy. While 90% of PWT1D were seen as candidates for AID therapy, 55% of PWT2D on intensified insulin therapy were also considered likely to benefit. Nonetheless, 22% of eligible PWD rejected AID, citing device burden and general concerns regarding its usability. Health care professionals reported a discontinuation rate of 8%. Satisfaction with AID was generally high. Regression analyses identified technical problems, lack of trust, and unrealistic expectations as significant predictors of lower satisfaction and poorer management in PWD. Despite increasing user rates of AID systems, a significant proportion reports problems in managing this technology or aborting its use. Addressing reasons for this may increase the uptake in clinical care. There was also a positive view of HCPs on AID use in PWT2D.

Automated insulin delivery (AID) systems improve glycemic control and reduce treatment burden in people with type 1 diabetes, yet their uptake remains suboptimal in many countries. Understanding barriers to the usage of AID systems from the health care professional (HCP) perspective is essential to support wider adoption. Physicians and diabetes educators/nurses were invited to complete an online survey assessing the current use of diabetes technology as well as attitudes and barriers to AID and insulin pump therapy. The survey was conducted from October to December 2022. A network analysis was conducted to analyze the associations between different barriers to usage of AID systems and insulin pumps. Data from 594 HCPs (220 physicians and 374 diabetes educators/nurses) were analyzed. In 2022, HCPs estimated that 11% of their patients with type 1 diabetes used an AID system. They reported that 20% to 27% of eligible patients refuse to use an AID system; 5% to 7% of the users of an AID system stopped using it. The majority of HCPs (68.4% and 60.7%) reported an increased need for education. The most important barriers to start using an AID system were a lack of training and education materials, body image issues, overload, and insufficient training of the members of the diabetes team. There was a sharp increase in AID use in Germany from 2019 to 2022. The results highlight the need for adequate training materials for people with diabetes and the diabetes team.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

Gestational diabetes mellitus (GDM) often requires pharmacological intervention beyond lifestyle modification to achieve optimal glycemic control. This study aimed to develop machine learning models that integrate clinical and gut microbiome data to predict the need for insulin therapy (IT) in women with GDM. We characterized 205 pregnant women with GDM from the Genetic and Epigenetic Mechanisms of Developing Gestational Diabetes Mellitus and its Effects on the Fetus study, collecting clinical parameters, lifestyle questionnaires, self-monitored blood glucose records, and gut microbiome profiles based on 16S rRNA gene sequencing. Gradient-boosting models were trained to predict IT, basal insulin (BI), and prandial insulin (PI) requirements. Model discrimination was assessed using repeated stratified five-fold cross-validated area under the curve-receiver operating characteristic (AUC-ROC) (nested cross-validation). Feature importance and interpretability were evaluated with SHapley Additive exPlanations and permutation analyses. Differential microbial abundance was analyzed by ANCOM-BC2 (analysis of composition of microbiomes with bias correction, version 2), and metabolic pathways were predicted via PICRUSt2. Women requiring insulin were older and had higher pre-pregnancy body mass index (BMI), fasting plasma glucose, 1-hour oral glucose tolerance test glucose, and glycated hemoglobin than diet-treated women (P < .05 for all). Adding microbiome data improved AUC-ROC for IT prediction from 0.63 (95% CI = 0.43, 0.83) to 0.70 (0.50, 0.89), BI from 0.77 (0.59, 0.95) to 0.82 (0.65, 0.99), and for PI from 0.69 (0.50, 0.88) to 0.70 (0.51, 0.89). Key influential features included glycemic markers, BMI, and microbial taxa, such as Phascolarctobacterium faecium, Alistipes ihumii, Cloacibacillus evryensis, Ruthenibacterium lactatiformans, and Methanosphaera stadtmanae, and the predicted microbial metabolic pathway PWY-5823. Our findings demonstrate that integrating gut microbiome characteristics with clinical data improves the prediction of insulin treatment needs in GDM, particularly for BI initiation.

Discrepancies between HbA1c and glucose management indicator (GMI) may reflect individual variations in glycation rate, independent of mean glycemia, and could influence complication risk stratification in type 1 diabetes (T1D). We evaluated the phenotype of individuals with T1D using continuous glucose monitoring (CGM), identified as high glycators based on HbA1c/updatedGMI ratio, and assessed retrospectively their risk of diabetic retinopathy (DR) and the time to DR diagnosis. The secondary aim was to identify clinical correlates of high glycation. time to first diagnosis of DR. clinical factors associated with high glycation. A retrospective study of 411 individuals with T1D using CGM and concurrent HbA1c values. Patients with conditions affecting red blood cell (RBC) lifespan were excluded. Participants were divided into 3 subgroups based on current HbA1c/updatedGMI ratio &#x2264;0.95 (low glycators), >0.95 and <1.05 (concordant glycators), and &#x2265;1.05 (high glycators). Time to diagnosis of DR was retrieved. High glycation is associated with shorter time to first diagnosis of DR (adjusted hazard ratio 1.60). Non-HDL-C, RBC indices, and metformin were associated with high glycation. Among individuals with T1D, an HbA1c/updatedGMI ratio &#x2265;1.05 is associated with higher odds of DR. Non-HDL-C and RBC indices are correlates of high glycation. These results underscore the relevance of HbA1c and updatedGMI discrepancy in cardiometabolic risk assessment, but cutoffs remain to be set.