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The digital education platform Doctorvice (iKooB Inc.) offers face-to-face physician-patient education during outpatient clinic visits, remote glucose monitoring, and the delivery of educational messages, and is expected to be effective for personalized diabetes care. This study aims to evaluate the effectiveness of the digital education platform for diabetes care by comparing cases that included both face-to-face education and remote monitoring with those that included only face-to-face education. This was a randomized clinical study conducted at the Diabetes Center of Seoul St. Mary's Hospital. Participants were aged ≥19 years and had glycated hemoglobin (HbA ) levels between 7.5% and 9.5%. In the intervention group, physicians used the digital education platform to provide face-to-face education at enrollment and at the 3- and 6-month visits, along with remote monitoring during the first 3 months of the 6-month study period. The control group received conventional outpatient education. Both groups completed questionnaires-assessing satisfaction with diabetes treatment, diabetes-related stress, and adherence to diabetes medication-at the beginning and end of the study. The primary endpoint was the change in HbA levels. A total of 66 participants were enrolled between August 1, 2022, and August 31, 2023. Of these, 26 in the intervention group and 30 in the control group were analyzed, excluding 10 participants who dropped out of the study. The mean baseline HbA levels were 8.3% (SD 0.6%) in the intervention group and 8.0% (SD 0.5%) in the control group. At the 3-month follow-up, mean HbA decreased by 0.5%-7.8% (SD 0.9%; P=.01) in the intervention group and by 0.2%-7.8% (SD 0.7%) in the control group. HbA levels substantially improved during the first 3 months with both face-to-face education and remote glucose monitoring. However, HbA tended to increase during the 3- to 6-month follow-up in the intervention group without the remote monitoring service. Satisfaction with diabetes treatment significantly improved at the end of the study compared with baseline in the intervention group (mean change +3.6 points; P=.006). Medication adherence improved in both groups, with no significant difference at 6 months (P=.59), although the intervention group showed a greater increase from baseline. Subgroup analysis indicated that the reduction in HbA was greater for patients with baseline HbA levels ≥8.0%, those aged ≥65 years, smokers, drinkers, and those with obesity in the intervention group. The digital education platform for personalized diabetes management may be beneficial for glycemic control in type 2 diabetes mellitus. Its effectiveness appears to be enhanced when physicians provide personalized face-to-face education combined with remote feedback. Clinical Research Information Service (CRiS) of Republic of Korea KCT0007953; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=23507&search_page=L.

Nicotine inhibits glucose metabolism. In this national cross-sectional analysis of 388 young adults with type 1 diabetes and above target glycemic control, vaping was the most common route of nicotine use, and heavy nicotine use plus higher type 1 diabetes distress was related to worse objective measures of glycemic control. Trial registration: ClinicalTrials.govNCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473. •Young adults with type 1 diabetes and high HbA1c are a vulnerable population.•One in four young adults with type 1 diabetes reported nicotine use in past month.•Young adults with type 1 diabetes primarily reported only nicotine vaping.•Higher nicotine use was significantly associated with worse glycemic control.•Heavy nicotine use plus higher diabetes distress related to worse glycemic control.

Diet is a factor which can influence both glycaemic variables and body mass. The aim of this study was to compare the influence of a 12-week, well-planned, low-calorie ketogenic diet (LCKD) on hyperglycaemic, hyperinsulinemic and lipid profile in adult, overweight or obese females. Ninety-one females who participated in the study were divided into two groups: a LCKD group who followed a hypocaloric ketogenic diet (8% of carbohydrate, 72% of fat and 20% of proteins) (n = 46), and a control group (CG) (n = 45) who continued their typical diet (50% of carbohydrates, 32% of fat and 18% of proteins). Methods: Baseline and post-intervention glucose (Gl), insulin (I), glycated haemoglobin (HbA1c), Homeostatic model assessment HOMA-IR, triglycerides (TG) and high-density cholesterol (HDL-C) were evaluated. Also, body mass (BM), waist circumference (WC), hip circumference (HC) and thigh circumference (TC) were measured. Results: Compared with the CG, there were significant changes observed in the LCKD group regarding all biochemical variables. Also, BM, TC, WC and AC changed significantly in the LCKD group compared with the CG. Conclusions: The 12-week LCKD intervention changed the glucose control variables, body mass, as well as waist, hip and thigh circumferences. A low-calorie ketogenic diet may be recommended for adult females with glucose control variables disturbance and excess body mass.

Background Recent evidence suggests a potentially protective effect of increasing ketone body availability via accepting low macronutrient intake early after onset of critical illness. The impact of blood glucose control with insulin on circulating ketones is unclear. Whereas lowering blood glucose may activate ketogenesis, high insulin concentrations may have the opposite effect. We hypothesized that the previously reported protective effects of tight glucose control in critically ill patients receiving early parenteral nutrition may have been mediated in part by activation of ketogenesis. Methods This is a secondary analysis of 3 randomized controlled trials on tight versus liberal blood glucose control in the intensive care unit, including 700 critically ill children and 2748 critically ill adults. All patients received early parenteral nutrition as part of the contemporary standard of care. Before studying a potential mediator role of circulating ketones in improving outcome, we performed a time course analysis to investigate whether tight glucose control significantly affected ketogenesis and to identify a day of maximal effect, if any. We quantified plasma/serum 3-hydroxybutyrate concentrations from intensive care unit admission until day 3 in 2 matched subsets of 100 critically ill children and 100 critically ill adults. Univariable differences between groups were investigated by Kruskal-Wallis test. Differences in 3-hydroxybutyrate concentrations between study days were investigated by Wilcoxon signed-rank test. Results In critically ill children and adults receiving early parenteral nutrition, tight glucose control, as compared with liberal glucose control, lowered mean morning blood glucose on days 1–3 ( P  < 0.0001) via infusing insulin at a higher dose ( P  < 0.0001). Throughout the study period, caloric intake was not different between groups. In both children and adults, tight glucose control did not affect 3-hydroxybutyrate concentrations, which were suppressed on ICU days 1–3 and significantly lower than the ICU admission values for both groups ( P  < 0.0001). Conclusion Tight versus liberal glucose control in the context of early parenteral nutrition did not affect 3-hydroxybutyrate concentrations in critically ill patients. Hence, the protective effects of tight glucose control in this context cannot be attributed to increased ketone body availability.

Aims/Introduction Diabetes patients develop a variety of metabolic abnormalities in addition to hyperglycemia. However, details regarding change in various metabolites after comprehensive diabetes treatment remain unknown. This study aimed to identify the short‐term change in metabolome in inpatients who were subject to comprehensive diabetes treatment, using gas chromatography/mass spectrometry‐based non‐target metabolomics techniques. Materials and Methods Participants of the present study were randomly recruited from the patients with type 2 diabetes hospitalized due to problems with glycemic control (n = 31) and volunteers without diabetes (n = 30), both of whom were aged between 20 and 75 years. A metabolomic analysis of fasting plasma samples on the 2nd (pre‐treatment) and 16th hospital (post‐treatment) day with gas chromatography/mass spectrometry using a multiple reaction monitoring mode was carried out. Results A principal component analysis showed that metabolome of fasting plasma was different between individuals with and without diabetes. The metabolome of fasting plasma in diabetes patients after treatment was different from that of pre‐treatment, as well as individuals without diabetes. Many amino acids (proline, glycine, serine, threonine, methionine, pyroglutamic acid, glutamine and lysine) were significantly increased by >10% after administering the inpatient diabetes treatment. A hierarchical clustering analysis showed that in the case of patients with markedly decreased monosaccharide levels and increased 1,5‐anhydroglucitol, the levels of amino acids increased more significantly. Conclusions After a 2‐week comprehensive treatment, the plasma levels of various amino acids increased in conjunction with the reduction in monosaccharide levels in poorly controlled type 2 diabetes patients. This study aimed to identify the short‐term change in metabolome in inpatients who were subject to comprehensive diabetes treatment, using gas chromatography/mass spectrometry‐based non‐target metabolomics techniques. After a 2‐week comprehensive treatment, the plasma levels of various amino acids increased in conjunction with the reduction in monosaccharide levels in poorly controlled type 2 diabetes patients.

The number of older patients with type 2 diabetes (T2D) is increasing, and effective self-management is crucial for controlling disease progression and its complications. We designed a home telemedicine intervention that combines telemedicine with health education based on the Health Belief Model (HBM). This study evaluated its effectiveness on self-management in older patients with T2D. Between March and April 2022, we recruited 198 community-dwelling patients with T2D aged 65 years and older. Patients were randomly assigned to either a control group, which received a conventional diabetes management program, or an intervention group, which received a home telemedicine intervention with a health education program based on the HBM. The intervention lasted 6 months. The primary outcome measured was glycosylated hemoglobin (HbA1c); secondary outcomes included diabetes self-management capacity, self-efficacy, and health beliefs. We collected outcome metrics at baseline, 3 months, and 6 months. Generalized estimating equations were used to compare changes in outcomes. A total of 96.5% (191/198) of patients completed the study. From baseline to 6 months, HbA1c decreased by mean -0.99% (95% CI -1.60% to -0.60%) in the intervention group and mean -0.42% (95% CI -0.90% to 0.90%) in the control group. The intervention group experienced a significantly greater reduction of 0.42% compared to the control group (95% CI 0.12%-0.73%). Furthermore, compared to the control group, the intervention group showed significant improvements in diabetes self-management skills (mean 5.88, 95% CI 4.98-6.79), self-efficacy (mean 9.40, 95% CI 8.15-10.66), and health beliefs (mean 19.54, 95% CI 17.71-21.36) at both 3 and 6 months. Home telemedicine interventions incorporating health education based on the HBM can provide significant benefits for community-dwelling older patients with T2D, potentially offering new avenues for chronic disease prevention and management. However, future large-scale studies are required to further assess their effectiveness and feasibility.

The exposome represents the array of dietary, lifestyle, and demographic factors to which an individual is exposed. Individual components of the exposome, or groups of components, are recognized as influencing many aspects of human physiology, including cardiometabolic health. However, the influence of the whole exposome on health outcomes is poorly understood and may differ substantially from the sum of its individual components. As such, studies of the complete exposome are more biologically representative than fragmented models based on subsets of factors. This study aimed to model the system of relationships underlying the way in which the diet, lifestyle, and demographic components of the overall exposome shapes the cardiometabolic risk profile. The current study included 36,496 US Veterans enrolled in the VA Million Veteran Program (MVP) who had complete assessments of their diet, lifestyle, demography, and markers of cardiometabolic health, including serum lipids, blood pressure, and glycemic control. The cohort was randomly divided into training and validation datasets. In the training dataset, we conducted two separate exploratory factor analyses (EFA) to identify common factors among exposures (diet, demographics, and physical activity) and laboratory measures (lipids, blood pressure, and glycemic control), respectively. In the validation dataset, we used multiple normal regression to examine the combined effects of exposure factors on the clinical factors representing cardiometabolic health. The mean ± SD age of participants was 62.4 ± 13.4 years for both the training and validation datasets. The EFA revealed 19 Exposure Common Factors and 5 Physiology Common Factors that explained the observed (measured) data. Multivariate regression in the validation dataset revealed the structure of associations between the Exposure Common Factors and the Physiology Common Factors. For example, we found that the factor for fruit consumption was inversely associated with the factor summarizing total cholesterol and low-density lipoprotein cholesterol (LDLC, p = 0.008), and the latent construct describing light levels of physical activity was inversely associated with the blood pressure latent construct (p < 0.0001). We also found that a factor summarizing that participants who frequently consume whole milk are less likely to frequently consume skim milk, was positively associated with the latent constructs representing total cholesterol and LDLC as well as systolic and diastolic blood pressure (p = 0.0006 and <0.0001, respectively). Multiple multivariable-adjusted regression analyses of exposome factors allowed us to model the influence of the exposome as a whole. In this metadata-rich, prospective cohort of US Veterans, there was evidence of structural relationships between diet, lifestyle, and demographic exposures and subsequent markers of cardiometabolic health. This methodology could be applied to answer a variety of research questions about human health exposures that utilize electronic health record data and can accommodate continuous, ordinal, and binary data derived from questionnaires. Further work to explore the potential utility of including genetic risk scores and time-varying covariates is warranted.

The eHealth app introduced a chronic disease management module to enhance the efficiency of clinical consultations and facilitate self-health management. However, there was limited information on health outcomes after using the module. This study aimed to examine the health outcomes of individuals who used the health management module of an individualized electronic application compared to those who did not. A randomized controlled trial was conducted with 165 participants, with 82 participants assigned to the control group and 83 participants assigned to the intervention group. Randomization was done via a computer randomizer to evaluate the impact of the eHealth chronic disease management module installation on clinical outcomes such as blood pressure, hemoglobin A1c (HbA1c), renal function tests, estimated glomerular filtration rate, and urine albumin/creatinine ratio. Data were collected at baseline and at follow-up visits at 4 and 8 months. Student t tests and chi-square tests were performed to analyze the difference between the intervention and control groups and examined the potential impact of the use of the eHealth chronic disease management module on various health outcomes. In total, 161 participants were included in the analysis, with an average age of 66.58 (SD 9.75) and 66.49 (SD 8.45) years in control and intervention group respectively. After 4 months, the intervention group showed better glycemic control, with significantly lower mean HbA1c levels (mean 6.76%, SD 0.64%) compared to the control group (mean 7.09%, SD 0.82%, P=.007). Also, more participants in intervention group achieved optimal HbA1c levels (n=58, 73.4%; P=.004) compared to the control group (n=36, 49.3%) in month 4. App usage had significantly decreased when comparing the usage after 4 months (mean 1.88 points, SD 0.81 points) and month 8 (mean 1.39 points, SD 0.72 points; P<.001). The results indicated better glycemic control for participants using the module in a relatively shorter period of time, and app adherence was the key for the continuous optimal glycemic control. These findings support the potential of the module for clinical application in patients with suboptimal glycemic control. The long-term benefit of the module may be affected by the compliance of participants to the module. Chinese Clinical Trial Register ChiCTR2500108895; https://www.chictr.org.cn/showprojEN.html?proj=214865.

Objectives Patients with diabetes are - compared to people without diabetes - at increased risk of worse outcomes from COVID-19 related pneumonia during hospitalization. We aim to investigate whether telemetric continuous glucose monitoring (CGM) in quarantined hospitalized patients with diabetes and confirmed SARS-CoV-2 infection or another contagious infection can be successfully implemented and is associated with better glycaemic control than usual blood glucose monitoring (finger prick method) and fewer patient-health care worker contacts. Furthermore, we will assess whether glucose variables are associated with the clinical outcome. The hypothesis is that by using remote CGM to monitor glucose levels of COVID-19 infected patients and patients with other contagious infections with diabetes, we can still provide satisfactory (and maybe even better) in-hospital diabetes management despite patients being quarantined. Furthermore, the number of patient-personnel contacts can be lowered compared to standard monitoring with finger-prick glucose. This could potentially reduce the risk of transmitting contagious diseases from the patient to other people and reduces the use of PPE’s. Improved glucose control may reduce the increased risk of poor clinical outcomes associated with combined diabetes and infection. Trial Design This is a single centre, open label, exploratory, randomised, controlled, 2-arm parallel group (1:1 ratio), controlled trial. Participants The trial population is patients with diabetes (both type 1 diabetes, type 2 diabetes, newly discovered diabetes that is not classified yet, and all other forms of diabetes) admitted to Nordsjællands Hospital that are quarantined due to COVID-19 infection or another infection. Inclusion criteria: 1. Hospitalized with confirmed COVID-19 infection by real-time PCR or another validated method OR hospitalized with a non-COVID-19 diagnosis and quarantined at time of inclusion. 2. A documented clinically relevant history of diabetes or newly discovered during hospitalization as defined by The World Health Organizations diagnostic criteria for diabetes. 3. Written informed consent obtained before any trial related procedures are performed. 4. Male or female aged over 18 years of age. 5. Must be able to communicate with the study personnel. 6. The subject must be willing and able to comply with trial protocol. Exclusion criteria: 1. Known hypersensitivity to the band-aid of the Dexcom G6 sensors Intervention and comparator Participants will be randomized to either real-time CGM with the Dexcom G6, a CGM system that does not need to be calibrated, or finger-prick glucose monitoring. Blinded CGM will be mounted in the finger-prick group. In the open CGM group, the glucose values will be transmitted to a Smartdevice in the nurse office where glucose levels can be monitored remotely. Main Outcomes The primary endpoint is the difference between groups in distribution of glucose values being in time in range (TIR), defined as 3.9 to 10 mmol/l. In addition, the primary endpoint is reported as the percentage of days of the whole admission, the patient reaches TIR. Secondary endpoints are the estimated number of saved patient-personnel contacts related to blood glucose measurements, incl. time healthcare providers spent on diabetes related tasks and PPE related tasks, during the patients’ hospitalization. Furthermore, we will assess additional glucose outcomes and associations of glucose variables and patient outcomes (As specified in the protocol). Randomisation The service used for generating the randomization lists is www.random.org . Randomization is stratified by COVID-19 status and an allocation ratio of 1:1 to either CGM or finger-prick groups. Blinding (Masking) The design of the trial is open, however blinded CGM is recorded in the finger-prick group. Numbers to be randomized (sample size) A sample size of N=72 is required for the primary endpoint analysis based on 80% power to detect a 10% difference between groups in TIR and to allow for a 15% dropout. The 72 participants will be randomized 1:1 to open CGM or finger-prick with 36 in each group. Trial status This structured protocol summary is based on the CGM-ISO protocol version 1.3, dated 13.05.2020. Date of first patient enrolled: 25.05.2020. Expected last recruiting is May 2021. Patients enrolled to date: 20 in total. 8 with confirmed COVID-19 infection and 12 with other infections. Trial registration ClinicalTrials.gov Identifier: NCT04430608 . Registered 12.06.2020 Full protocol The full protocol is attached as an additional file from the Trial website (Additional file 1 ). In the interest of expediting dissemination of this material, the familiar formatting has been eliminated; This Letter serves as a summary of the key elements of the full protocol.

Aims/hypothesisWe evaluated the secular trend of glycaemic control in individuals with type 2 diabetes in developing countries, where data are limited.MethodsThe International Diabetes Management Practices Study provides real-world evidence of patient profiles and diabetes care practices in developing countries in seven cross-sectional waves (2005–2017). At each wave, each physician collected data from ten consecutive participants with type 2 diabetes during a 2 week period. The primary objective of this analysis was to evaluate trends of glycaemic control over time.ResultsA total of 66,088 individuals with type 2 diabetes were recruited by 6099 physicians from 49 countries. The proportion of participants with HbA1c <53 mmol/mol (<7%) decreased from 36% in wave 1 (2005) to 30.1% in wave 7 (2017) (p < 0.0001). Compared with wave 1, the adjusted ORs of attaining HbA1c ≤64 mmol/mol (≤8%) decreased significantly in waves 2, 5, 6 and 7 (p < 0.05). Over 80% of participants received oral glucose-lowering drugs, with declining use of sulfonylureas. Insulin use increased from 32.8% (wave 1) to 41.2% (wave 7) (p < 0.0001). The corresponding time to insulin initiation (mean ± SD) changed from 8.4 ± 6.9 in wave 1 to 8.3 ± 6.6 years in wave 7, while daily insulin dosage ranged from 0.39 ± 0.21 U/kg (wave 1) to 0.33 ± 0.19 U/kg (wave 7) for basal regimen and 0.70 ± 0.34 U/kg (wave 1) to 0.77 ± 0.33 (wave 7) U/kg for basal–bolus regimen. An increasing proportion of participants had ≥2 HbA1c measurements within 12 months of enrolment (from 61.8% to 92.9%), and the proportion of participants receiving diabetes education (mainly delivered by physicians) also increased from 59.0% to 78.3%.ConclusionsIn developing countries, glycaemic control in individuals with type 2 diabetes remained suboptimal over a 12 year period, indicating a need for system changes and better organisation of care to improve self-management and attainment of treatment goals.