Beata Mianowska

Aims: This study aimed to investigate the associations between glycemic outcomes and a range of clinical and demographic factors, including treatment modality, sex, age, diabetes duration, and body mass index, in youth with type 1 diabetes in an international registry. Methods: This observational, cross-sectional cohort study included youth 50%), time in range (TIR >70%), and glycated hemoglobin (HbA1c) (≤6.5% [48 mmol/mol] and ≤7% [53 mmol/mol]) were assessed using mixed-effects fractional logistic and linear regression models. Sex, age (categorized), diabetes duration (categorized), body mass index standard deviation score (categorized), and treatment modality were included as covariates. Results: Data of 7691 individuals (mean [standard deviation] age of 13.7 [4.3] years, diabetes duration 6.3 [4.2] years, 47.8% female) were included. AID users were the most likely to achieve TITR target (adjusted mean [standard error of the mean] 41.2% [2.9]), followed by CGM with insulin pump (25.2% [2.3]) and CGM with injections (13.7% [1.5], P < 0.001). A similar association was observed for proportions of individuals achieving TIR and HbA1c targets ( P < 0.001). Age P < 0.001) and a lower probability of achieving both TITR and TIR targets compared with other age groups ( P < 0.001). In analyses of TITR associated with mean sensor glucose stratified by CV, a higher TITR for a lower CV was observed only at mean glucose levels below 150–160 mg/dL; above this threshold, the pattern reversed, with lower CV associated with lower TITR at a given mean glucose level. Conclusions: Children younger than 6 years and individuals not using glucose-responsive insulin therapy were less likely to meet the recommended glycemic targets. It is imperative to minimize these disadvantages.

PurposeAttention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that affects approximately 5% of the pediatric population, with increased prevalence among those with type 1 diabetes (T1D). Reports suggest that unrecognized and untreated ADHD impairs T1D control and that ADHD may be underdiagnosed in the Polish population. The International Society for Pediatric and Adolescent Diabetes recommends neurodevelopmental assessments in children with T1D, but specific guidelines on procedures and implementation are lacking. Therefore, we designed a systemic screening protocol aimed at assessing the prevalence of ADHD among Polish children with T1D that could be utilized in clinical practice.Materials and methodsThe protocol for the study was approved by the Bioethical Committee at the Medical University of Lodz (RNN/280/21/KE). Participation is offered to all children aged 8-16 y.o. with T1D duration >= 1 year and without significant comorbidities (epilepsy, intellectual disability). The protocol includes a first-line procedure using the short version of the Conners 3 questionnaire and on-site structured consultation with a psychologist for those with increased risk of ADHD (utilizing detailed Conners 3 and MINI-Kid questionnaires), followed by a video consultation with a psychiatrist.ResultsThe screening was initiated between January and May 2023 and is ongoing in four Polish reference pediatric centers. These centers provide diabetes care to approximately 2,500 youth with T1D. Over 36 months, our goal is to assist in diagnosing ADHD in approximately 190 children.ConclusionsComorbidity of ADHD and T1D remains an unrecognized challenge for pediatric psychiatric and diabetes care, and there is a need for effective diagnostic protocols.

Introduction: Advanced hybrid closed loop (AHCL) insulin delivery systems offer considerable benefits to individuals with type 1 diabetes (T1D) in terms of glucose control and quality of life. With increasing numbers of regular users, real-life long-term data on long-term AHCL effectiveness become available.Material and methods: This was a single-centre retrospective study. We included children and young adults (age 5–25 years ) with established T1D (≥ 3 m) who started MiniMed780G therapy between January 2021 and April 2022. We excluded those naïve to continuous glucose monitoring (CGM) or insulin pumps, as well as those without good-quality baseline CGM data. Included patients were followed for 12 months, with CGM and pump data retrieved from 14-day periods before transition and 3, 6, 9, and 12 months following the start of automode. Clinical data (body weight, height, glycated haemoglobin concentration) were recorded from the most recent outpatient visits.Results: Among 81 patients who started AHCL therapy, 46 met the criteria for analysis (mean age 11.5 ±4.4 years, diabetes duration 4.4 ±3.6 years, mean glycated haemoglobin 7.0 ±1.0%). Over the year following transition, we noted a significant improvement in time in target range 70–180 mg/dl (TIR, baseline: 69.1 ±12.0% to 12 m: 76.9 ±8.5%, p < 0.0001) and time in tight range 70–140 mg/dl (baseline: 45.3 ±14.2% to 12 m: 53.3 ±10.4%, p < 0.0001). Time below target range 70 mg/dl (TBR70 mg/dl) decreased significantly for 24-hour records (p = 0.0020). Importantly, those improvements were not accompanied by an increase in daily dose of insulin or body mass index.Conclusions: A prolonged 12-month-long observation in a routine care setting demonstrates that for young CGM- and pump users with T1D, switch AHCL offers sustained benefits in glucose variability.

Childhood obesity, with its metabolic complications, is a problem of public health. The International Diabetes Federation (IDF) has recommended glucose levels 1 h post oral glucose load (1h-PG) > 155–209 mg/dL as diagnostic for intermediate hyperglycemia (IH), while >209 mg/dL for type 2 diabetes (T2D). The aim of the study was to assess the occurrence of prediabetes, IH, and T2D in children and adolescents with simple obesity according to the criteria of American Diabetes Association (ADA) and of IDF, and the effect of COVID-19 pandemic on these disorders. Analysis included 263 children with simple obesity, screened either in prepandemic (PRE—113 cases) or post-pandemic period (POST—150 cases). All children underwent 2 h OGTT with measurements of glucose and insulin every 0.5 h, lipid profile, and other tests; indices if insulin resistance (IR): HOMA, QUICKI, Matsuda index, AUC (glu/ins) were calculated. The incidence of T2D, prediabetes, and IH was higher in POST with respect to PRE, with significant differences in the indices of IR, except for HOMA. Significant differences were observed in the assessed parameters of glucose metabolism among the groups with T2D, prediabetes, IH, and normal glucose tolerance (NGT), with some similarities between IH (based on 1h-PG) and prediabetes. Increased frequency of dysglycemia among children and adolescents with simple obesity is observed after COVID-19 pandemic. Metabolic profile of patients with IH at 1h-PG is “intermediate” between NGT and prediabetes.

Skin autofluorescence (sAF) measurement is a non-invasive method used to assess tissue advanced glycation end product (AGE) accumulation. This study aims to characterize sAF’s association with (1) glycated hemoglobin (HbA1c) values, (2) cardiovascular risk markers, and (3) common comorbidities (autoimmune thyroiditis, celiac disease) in children with type 1 diabetes (T1D). Materials and methods: A total of 348 children with T1D aged 3–18 years and 85 age- and gender-matched control subjects were enrolled. sAF was quantified using an AGE Reader (Diagnoptics BV, The Netherlands). The analysis covered HbA1c, blood lipid, and C-reactive protein (CRP) levels, ambulatory blood pressure monitoring records, and body composition parameters. The associations between variables and sAF were assessed using the Mann–Whitney U test and Spearman correlation. Results: We observed significantly higher sAF values in the T1D group compared to the control (1.40 [1.27–1.53] vs. 1.20 [1.07–1.30, AU]; p = 0.004), consistent across all tested age groups. In the T1D group, sAF was positively correlated with current HbA1c, mean of historical HbA1c values, and T1D duration (r values, respectively: 0.27, 0.22, 0.14, all p < 0.01). Percentage of body fat was positively correlated with sAF (r = 0.120; p = 0.044). No significant correlations were found between sAF and lipid fractions, Z-score of BMI, parameters from 24 h ambulatory blood pressure monitoring, or the amount of albumin excreted in urine. sAF was positively correlated with CRP (r = 0.17, p < 0.05). sAF was significantly higher in patients with concomitant celiac disease (1.53 [1.43–1.63] vs. 1.40 [1.27–1.53, AU], p = 0.001). Conclusion: Among young T1D patients with relatively brief diabetes duration, sAF effectively mirrors prior glycemic control, as presented by historical average HbA1c. However, associations with conventional CV risk markers are not evident. The higher sAF values in patients with celiac disease warrant further exploration.

Aim: The study aim was to evaluate the feasibility, safety and efficacy of automated insulin delivery (AID) assisted by home health care (HHC) services in people with type 2 diabetes unable to manage multiple daily insulin injections (MDI) at home on their own. Patients and Methods: This was an open label, multicentre, randomized, parallel group trial. In total, 30 adults with type 2 diabetes using MDI and requiring nursing support were randomly allocated to AID or kept their usual therapy over a 12‐week period. Both treatments were managed with the support of HHC services. The primary outcome was the percentage time in the target glucose range of 70‐180 mg/dl (TIR). Secondary outcomes included other continuous glucose monitoring metrics, glycated haemoglobin (HbA1c) levels, daily insulin doses, body weight, and of quality of life scores, fear of hypoglycaemia and satisfaction questionnaires. Results: Age (69.7 vs. 69.3 years) and HbA1c (9.25 vs. 9.0) did not differ in MDI and AID at baseline. Compared with MDI, AID resulted in a significant increase in TIR by 27.4% [95% CI (15.0‐39.8); p < .001], a decrease in time above range by 27.7% and an unchanged time below range of <1%. A between‐group difference in HbA1c was 1.3% favouring AID. Neither severe hypoglycaemia nor ketoacidosis occurred in either group. Patient and caregiver satisfaction with AID was high. Conclusions: AID combined with tailored HHC services significantly improved glycaemic control with no safety issues in people with type 2 diabetes previously under an MDI regimen with HHC. AID should be considered a safe option in these people when lacking acceptable glucose control.

Our aim was to compere diabetes-related distress (DD) in young patients with type 1 diabetes mellitus (T1DM) and in their parents before and during the national COVID-19-related lockdown when schools operated on-line. Problems Areas in Diabetes-Child (PAID-Ch), Teen (PAID-T) and Parent (P-PAID-Ch, P-PAID-T) questionnaires in paper version were used to evaluate DD before COVID-19 pandemic (November 2019–February 2020) and during the lockdown (April 2020) the same surveys were performed by phone. We enrolled 76 patients (median age (Q1–Q3): 13.6 (11.8–15.2) years; 21 children, 55 adolescents; T1DM duration 3.7 (1.7–6.8) years). Initial PAID score was lower in teenage boys than in girls (34.0 (24.0–42.0) vs. 44.5 (40.0–50.5), p = 0.003). In teens PAID score decreased significantly during the lockdown (−3.0 (−11.0–3.0), p = 0.018), more in girls than boys (p = 0.028). In children (−3.0 (−14.0–7.0), p = 0.131) and parents PAID did not change (teens’ parents: 3.0 (−9.0–10.0), p = 0.376; children’s parents: −5.0 [−9.0–1.0], p = 0.227). In the studied group COVID-19 pandemic-related lockdown was associated with decrease in DD in teens with T1DM, particularly in girls, while no significant change in DD was observed in children or parents. DD decrease in teens during the pandemic should attract attention to the potential “rebound” of DD related to return to regular on-site school routine.

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination ( R ² ), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R ² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. −0.152%, P < 0.0001], and overall 95% CI = −0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R ² = 0.83, bias = 0.026%, 95% CI = −0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients ( R ² = 0.76, bias = −0.069%, 95% CI = −0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Background. As diabetes affects multiple spheres of life the aim of this study was to explore the pain points of diabetes management as perceived by persons with type 2 diabetes and to identify their expectations towards new technologies. Methods. Patients with type 2 diabetes treated with oral hypoglycemic agents and/or insulin were surveyed. Respondents were asked to rate (i) the impact of diabetes on their daily life and (ii) their needs for improvements in different aspects of diabetes management on a five level Likert-type scale. Results. One hundred and fifty-four persons with type 2 diabetes were included. Most frequently reported challenges were: fear of diabetes complications development or progression (98.7% of patients), presence of diabetes complications (65.6%), frequent hyperglycemia (53.2%), and diabetes limiting one’s daily activities (50%). Most frequently expressed needs were: to evaluate glucose concentrations without finger pricking (98.1%), contact with a physician using mobile solutions and/or telemedicine (98.1%), and automation of insulin dosing (91.6%) and of calories/carbohydrates’ evaluation in meals (84.4%). Needs for telemedicine development, automation of insulin dosing and that the others help patients with diabetes management were more frequently reported by persons with: higher HbA1c, positive severe hypoglycemia history, concomitant chronic complications or diseases, and by those who were on insulin therapy. Conclusions. Although many diabetes technologies which meet the needs of patients with type 2 diabetes are already available, the study uncovers a high requirement for integrating them into disease management. The challenge pertains to implementation of the right technological solutions fulfilling needs of particular groups of patients and to helping them to embrace novelties into their daily lives. Background. As diabetes affects multiple spheres of life the aim of this study was to explore the pain points of diabetes management as perceived by persons with type 2 diabetes and to identify their expectations towards new technologies. Methods. Patients with type 2 diabetes treated with oral hypoglycemic agents and/or insulin were surveyed. Respondents were asked to rate (i) the impact of diabetes on their daily life and (ii) their needs for improvements in different aspects of diabetes management on a five level Likert-type scale. Results. One hundred and fifty-four persons with type 2 diabetes were included. Most frequently reported challenges were: fear of diabetes complications development or progression (98.7% of patients), presence of diabetes complications (65.6%), frequent hyperglycemia (53.2%), and diabetes limiting one’s daily activities (50%). Most frequently expressed needs were: to evaluate glucose concentrations without finger pricking (98.1%), contact with a physician using mobile solutions and/or telemedicine (98.1%), and automation of insulin dosing (91.6%) and of calories/carbohydrates’ evaluation in meals (84.4%). Needs for telemedicine development, automation of insulin dosing and that the others help patients with diabetes management were more frequently reported by persons with: higher HbA1c, positive severe hypoglycemia history, concomitant chronic complications or diseases, and by those who were on insulin therapy. Conclusions. Although many diabetes technologies which meet the needs of patients with type 2 diabetes are already available, the study uncovers a high requirement for integrating them into disease management. The challenge pertains to implementation of the right technological solutions fulfilling needs of particular groups of patients and to helping them to embrace novelties into their daily lives.