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The study aimed to estimate the prevalence of skin problems in children and adolescents with type 1 diabetes (T1D) using insulin pumps (IPs) and/or continuous glucose monitoring (CGM) in our center and analyze their association with various factors. As part of the international ISPAD JENIOUS-initiated SKIN-PEDIC project, we interviewed and examined patients who visited the regional pediatric diabetes center in Opole (Poland) for four weeks regarding the use of IP and/or CGM and the presence of skin problems. Body mass index (BMI) and glycemic parameters were obtained retrospectively from medical records. Among 115 individuals (45.2% girls, 83.5% IP users, 96.5% CGM users), old scars were the most common skin problem (IP users 53.1%; CGM users 66.4%), while ≥2 types of skin problems co-occurred (IP users 40.6%; CGM users 27.3%). Longer IP use was associated with a higher prevalence of skin problems (50% for IP < 1 year, 98.1%-IP 1-3 years, 100% for IP > 3 years; p < 0.001), pointing out extra attention with IP use > 1 year. No significant associations were found between skin problems and gender, age, BMI centile and glycemic parameters. Dermatological complications were common among children using IP and CGM in our center, highlighting the need for vigilant monitoring and early intervention to manage these skin-related issues effectively.

Objectives: To determine the impact of the COVID-19 pandemic on the incidence rates of infection and islet autoimmunity in children at risk for type 1 diabetes., Methods: 1050 children aged 4 to 7 months with an elevated genetic risk for type 1 diabetes were recruited from Germany, Poland, Sweden, Belgium and the UK. Reported infection episodes and islet autoantibody development were monitored until age 40 months from February 2018 to February 2023., Results: The overall infection rate was 311 (95% Confidence Interval [CI], 304-318) per 100 person years. Infection rates differed by age, country, family history of type 1 diabetes, and period relative to the pandemic. Total infection rates were 321 per 100 person-years (95% CI 304-338) in the pre-pandemic period (until February 2020), 160 (95% CI 148-173) per 100 person-years in the first pandemic year (March 2020-February 2021; P < 0.001) and 337 (95% CI 315-363) per 100 person-years in subsequent years. Similar trends were observed for respiratory and gastrointestinal infections. Islet autoantibody incidence rates were 1.6 (95% CI 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 (95% CI 0.8-1.9) per 100 person-years in the first pandemic year (P = 0.46), and 3.4 (95% CI 2.3-4.8) per 100 person-years in subsequent years (P = 0.005 vs. pre-pandemic year; P < 0.001 vs. first pandemic year)., Conclusions: The COVID-19 pandemic was associated with significantly altered infection patterns. Islet autoantibody incidence rates increased two-fold when infection rates returned to pre-pandemic levels., (© 2024. The Author(s).)

Purpose: To assess left atrial (LA) function in individuals with known paroxysmal atrial fibrillation (AF) compared with healthy and nonhealthy individuals without atrial fibrillation., Methods: The Akershus Cardiac Examination 1950 Study included 3,706 individuals all born in 1950. LA strain assessment of reservoir (LASr), conduit (LAScd) and contractile (LASct) functions were performed in all participants by investigators blinded to clinical data. Participants with cardiovascular disease, obesity, diabetes, pulmonary or renal disease were defined as nonhealthy, and those without as healthy. Patients with paroxysmal AF were identified through medical history and ECG documentation., Results: LA strain assessment was feasible in 3,229 (87%) of the participants (50% women). The healthy group (n = 758) had significantly higher LASr and LAScd than the nonhealthy (n = 2,376), but LASct was similar between the groups. Participants with paroxysmal AF had significantly lower values of all strain parameters than the other groups. Multivariable logistic regression showed a significantly reduced probability of having AF per standard deviation increase in LASr and LASct. A nonlinear restricted cubic spline model fitted better with the association of LASr with paroxysmal AF than the linear model, and LA strain values below the population mean associated with an increased probability of having AF, but for values above the population mean no such association was present., Conclusion: Compared to participants without AF, those with known paroxysmal AF had significantly lower values of all LA strain parameters during sinus rhythm. Lower values of LA strain were associated with a significantly increased probability of having AF., (© 2024 The Author(s). Echocardiography published by Wiley Periodicals LLC.)

Competing Interests: A.G.Z. ist Mitglied im Data Monitoring Committee (DMC) der Protect-Studie und der Petite-Studie (Teplizumab). O.K. ist National-Principle Investigator für Deutschland in der PROTECT-Studie (Teplizumab). R.B. ist Principle Investigator in der Protect-Studie (Teplizumab). Die weiteren Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.

Aims: Atrial fibrillation (AF) is prevalent, undiagnosed in approximately one-third of cases, and is associated with severe complications. Guidelines recommend screening individuals at increased risk of stroke. This report evaluated the digital recruitment procedure and compliance with the follow-up recommendations in participants with screen-detected AF in the Norwegian Atrial Fibrillation self-screening pilot study., Methods and Results: Norwegians ≥65 years were invited through Facebooks posts, web pages, and newspapers to participate in the study. Targeted Facebook posts promoted over 11 days reached 84 208 users and 10 582 visitors to the study homepage. This accounted for 51% of the total homepage visitors ( n = 20 704). A total of 2118 (10%) of the homepage visitors provided digital consent to participate after they met the inclusion criteria. The mean (standard deviation) age of the participants was 70 (4) years, and the majority [ n = 1569 (74%)] were women. A total of 1849 (87%) participants completed the electrocardiogram self-screening test, identifying AF in 41 (2.2%) individuals. Of these, 39 (95%) participants consulted a general practitioner, and 34 (83%) participants initiated anticoagulation therapy., Conclusion: Digital recruitment and inclusion in digital AF screening with a high rate of initiation of anticoagulation therapy in AF positive screening cases are feasible. However, digital recruitment and inclusion may introduce selection bias with regard to age and gender. Larger studies are needed to determine the efficacy and cost-effectiveness of a fully digital AF screening., Trial Registration: Clinical trials: NCT04700865., Competing Interests: Conflict of interest: E.L.S. has received speaking fees from Pfizer and AstraZeneca. S.H. has received speaking fees from Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, and Sanofi unrelated to this work. T.B. has received speaking fees from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, and Pfizer. J.G. has received speaking fees from Bayer, Boehringer-Ingelheim, Novartis and Sanofi, and fees for participating in an expert panel from Sanofi, unrelated to this work. D.A. has received speaker fees from Amarin, Amgen, AstraZeneca, Bayer, Bristol Myers Squibb, Boehringer-Ingelheim, Merck & Co., Novartis, Pfizer, Pharmacosmos, Philips, Sanofi and Vifor, as well as research funding to his institution from Bristol Myers Squibb, Pfizer, Medtronic, and Roche Diagnostics. He acts as scientific advisor to Appsens AS. B.L.G. has received speaking fees from Bristol Myers Squibb, Pfizer, Novartis, Astra Zeneca, Boehringer Ingelheim, and Bayer. J.J. has received speaking fees from Amarin, Amgen, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Novartis, Pfizer, and Sanofi. He is shareholder in Appsens AS and is employed in the company., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims/hypothesis: The aim of this study was to determine whether BMI in early childhood was affected by the COVID-19 pandemic and containment measures, and whether it was associated with the risk for islet autoimmunity., Methods: Between February 2018 and May 2023, data on BMI and islet autoimmunity were collected from 1050 children enrolled in the Primary Oral Insulin Trial, aged from 4.0 months to 5.5 years of age. The start of the COVID-19 pandemic was defined as 18 March 2020, and a stringency index was used to assess the stringency of containment measures. Islet autoimmunity was defined as either the development of persistent confirmed multiple islet autoantibodies, or the development of one or more islet autoantibodies and type 1 diabetes. Multivariate linear mixed-effect, linear and logistic regression methods were applied to assess the effect of the COVID-19 pandemic and the stringency index on early-childhood BMI measurements (BMI as a time-varying variable, BMI at 9 months of age and overweight risk at 9 months of age), and Cox proportional hazard models were used to assess the effect of BMI measurements on islet autoimmunity risk., Results: The COVID-19 pandemic was associated with increased time-varying BMI (β = 0.39; 95% CI 0.30, 0.47) and overweight risk at 9 months (β = 0.44; 95% CI 0.03, 0.84). During the COVID-19 pandemic, a higher stringency index was positively associated with time-varying BMI (β = 0.02; 95% CI 0.00, 0.04 per 10 units increase), BMI at 9 months (β = 0.13; 95% CI 0.01, 0.25) and overweight risk at 9 months (β = 0.23; 95% CI 0.03, 0.43). A higher age-corrected BMI and overweight risk at 9 months were associated with increased risk for developing islet autoimmunity up to 5.5 years of age (HR 1.16; 95% CI 1.01, 1.32 and HR 1.68, 95% CI 1.00, 2.82, respectively)., Conclusions/interpretation: Early-childhood BMI increased during the COVID-19 pandemic, and was influenced by the level of restrictions during the pandemic. Controlling for the COVID-19 pandemic, elevated BMI during early childhood was associated with increased risk for childhood islet autoimmunity in children with genetic susceptibility to type 1 diabetes., (© 2024. The Author(s).)

Aims: Users of homecare services are often excluded from clinical trials due to advanced age, multimorbidity, and frailty. Atrial fibrillation (AF) is a common and frequently undiagnosed arrhythmia in the elderly and is associated with severe mortality, morbidity, and healthcare costs. Timely identification prevents associated complications through evidence-based treatment. This study is aimed at assessing the feasibility of AF screening using new digital health technology in older people in a homecare setting., Methods: Users of homecare services ≥ 65 years old with at least one additional risk factor for stroke in two Norwegian municipalities were assessed for study participation by nurses. Participants performed a continuous prolonged ECG recording using a patch ECG device (ECG247 Smart Heart Sensor)., Results: A total of 144 individuals were assessed for study participation, but only 18 (13%) were included. The main reasons for noninclusion were known AF and/or anticoagulation therapy (25%), severe cognitive impairment (26%), and lack of willingness to participate (36%). The mean age of participants performing the ECG test was 81 (SD ± 7) years, and 9 (50%) were women. All ECG tests were interpretable; the mean ECG monitoring time was 104 hours (IQR 34-338 hours). AF was detected in one individual (6%)., Conclusion: This feasibility study highlights the challenges of enrolling older people receiving homecare services in clinical trials. However, all included participants performed an interpretable and prolonged continuous ECG recording with a digital ECG patch device. This trial is registered with NCT04700865., Competing Interests: Edvard Liljedahl Sandberg has received speaking fees from Pfizer and AstraZeneca. Sigrun Halvorsen has received speaking fees from Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, and Sanofi unrelated to this work. Trygve Berge has received speaking fees from Bayer, Boehringer Ingelheim and Bristol Myers Squibb, and Pfizer. Jostein Grimsmo has received speaking fees from Bayer, Boehringer-Ingelheim, Novartis and Sanofi, and fees for participating in an expert panel from Sanofi, unrelated to this work. Dan Atar has received speaker fees from Amgen, AstraZeneca, Bayer, Bristol Myers Squibb, Boehringer-Ingelheim, Merck & Co, Novartis, Pfizer, Pharmacosmos, Philips, Sanofi and Vifor, as well as research funding to his institution from Bristol Myers Squibb, Pfizer, Medtronic, and Roche Diagnostics. He acts as a scientific advisor to Appsens AS. Bjørnar L. Grenne has received speaking fees from Bristol Myers Squibb, Pfizer, Novartis, and Bayer. Jarle Jortveit has received speaking fees from Amgen, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Novartis, Pfizer, and Sanofi. He is a shareholder in Appsens AS and is employed in the company., (Copyright © 2024 Edvard Liljedahl Sandberg et al.)

Competing Interests: Conflict of interest: None declared.

Background: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity., Methods: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre., Results: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts., Conclusion: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening., (© 2023. The Author(s).)

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype., Competing Interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: KK reports no disclosures. NKdR reports no disclosures. AU received grants and contracts from FISM, Novartis, Biogen, Merck, Fondazione Cariplo, Italian Ministry of Health, received honoraria, or consultation fees from Biogen, Roche, Teva, Merck, Genzyme, Novartis. FI reports no disclosures. MC reports no disclosures. HFH has received honoraria for lecturing or advice from Biogen, Merck, Roche, Novartis and Sanofi. TB has received unrestricted research grants from Biogen and Sanofi-Genzyme. SDB reports no disclosures. EH received honoraria for lecturing and advisory board activity from Biogen, Merck and Sanofi-Genzyme and unrestricted research grant from Merck. SBI reports no disclosures. SAdRB reports no disclosures. FP received honoraria and research support from Alexion, Bayer, Biogen, Chugai, Merck Serono, Novartis, Genzyme, MedImmune, Shire, Teva, and serves on scientific advisory boards for Alexion, MedImmune, and Novartis. He has received funding from Deutsche Forschungsgemeinschaft (DFG Exc 257), Bundesministerium fu?r Bildung und Forschung (Competence Network Multiple Sclerosis), Guthy Jackson Charitable Foundation, EU Framework Program 7, National Multiple Sclerosis Society of the USA. AUB is named as inventor on multiple patents and patents pending owned by Charité - Universitätsmedizin Berlin and/or University of California Irvine for visual computing-based motor function analysis, multiple sclerosis serum biomarkers, and retinal image analysis. He is cofounder and holds shares of Motognosis GmbH and Nocturne GmbH. He serves on the executive board and is Treasurer/Secretary of IMSVISUAL. He received research support from BMWi, BMBF, NIH ICTS, the Kathleen C. Moore Foundation and the Guthy- Jackson Charitable Foundation. Priscilla Ba?cker-Koduah is funded by the DFG Excellence grant to FP (DFG exc 257) and is a Junior scholar of the Einstein Foundation. CC received honoraria for speaking from Bayer and research funding from Novartis, unrelated to this study. SA received a conference grant from Celgene and honoraria for speaking from Alexion, Bayer and Roche. JB reports no disclosures. JSR declares funding from GSK & Sanofi and fees from Travere Therapeutics & Singularity Bio. MR reports no disclosures. LGA is founder and hold stocks at ProtATonce. MA is an employee of Hoffman-La Roche AG, yet this article is related to his activity at the Hospital Clinic of Barcelona. EHML is an employee of the European Medicines Agency (Human Medicines) since 16 April 2019, yet this article is related to her activity at the Hospital Clinic of Barcelona and consequently, it does not in any way represent the views of the Agency or its Committees. SL received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, TEVA, Genzyme, Sanofi and Merck. AS received compensation for consulting services and speaker honoraria from Bayer-Schering, Merck- Serono, Biogen-Idec, Sanofi-Aventis, TEVA, Novartis and Roche. EMH reports no disclosures. Elisabeth Solana received travel reimbursement from Sanofi and ECTRIMS and reports personal fees from Roche Spain. IPV is currently an employee of UCB pharma, yet this article is related to her activity at the Hospital Clinic of Barcelona. She has received travel reimbursement from Roche Spain and Genzyme-Sanofi, European Academy of Neurology, and European Committee for Treatment and Research in Multiple Sclerosis for international and national meetings over the last 3 years; she holds a patent for an affordable eye-tracking system to measure eye movement in neurologic diseases, and she holds stock in Aura Innovative Robotics. JGO reports no disclosures. PV has received consultancy fees and held stocks from Accure Therapeutics SL, Attune Neurosciences Inc, Spiral Therapeutics Inc, QMenta Inc, CLight Inc, NeuroPrex Inc, StimuSIL and Adhera Health Inc, (Copyright: © 2024 Kennedy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Vitamin D insufficiency (VDI) may be a factor in the development of type 1 diabetes (T1D). The aim of this study is to investigate the presence and persistence of VDI in a large cohort of infants with increased risk of developing T1D, in light of the differences in local supplementation guidelines., Methods: In the POInT Study, a multicentre primary prevention study between February 2018 and March 2021 in Germany, Poland, Belgium, England and Sweden, including infants aged 4-7 months at high genetic risk of developing β-cell autoantibodies, vitamin D levels were analysed at each study visit from inclusion (4-7 months) until 3 years, with an interval of 2 months (first three visits) or 4-6 months (visits 4-8). The protocol actively promotes vitamin D sufficiency to optimise immune tolerance. VDI was defined as a concentration below 30 ng/mL and was treated according to local guidelines of participating centres. Recovery from VDI was defined as a concentration above or equal to 30 ng/mL on the subsequent visit after VDI., Results: 1050 infants were included, of which 5937 vitamin D levels were available for analyses. VDI was observed in 1464 (24.7%) visits and 507 (46.1%) of these were not resolved at the next visit. The risk of having VDI was independently associated with season (higher in winter), weight (higher with increased weight), age (higher with increased age) and country (higher in England). The risk of not recovering from VDI was independently associated with the season of the previously determined VDI, which was higher if VDI was identified in winter., Conclusions: VDI is frequent in infants with increased risk of developing T1D. Treatment guidelines for VDI do not seem effective. Increasing supplementation dosages in this patient population seems warranted, especially during winter, and increasing dosages more aggressively after VDI should be considered., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Aims: The benefit of oral anticoagulant (OAC) therapy in atrial fibrillation (AF) and intermediate stroke risk is debated. In a nationwide Norwegian cohort with a non-sex CHA2DS2-VASc risk score of one, this study aimed to investigate (i) stroke and bleeding risk in AF patients with and without OAC treatment, and (ii) the risk of stroke in non-anticoagulated individuals with and without AF., Methods: A total of 1 118 762 individuals including 34 460 AF patients were followed during 2011-18 until ischaemic stroke, intracranial haemorrhage, increased CHA2DS2-VASc score, or study end. One-year incidence rates (IRs) were calculated as events per 100 person-years (%/py). Cox regression models provided adjusted hazard ratios (aHRs [95% confidence intervals])., Results: Among AF patients, the ischaemic stroke IR was 0.51%/py in OAC users and 1.05%/py in non-users (aHR 0.47 [0.37-0.59]). Intracranial haemorrhage IR was 0.28%/py in OAC users and 0.19%/py in non-users (aHR 1.23 [0.88-1.72]). Oral anticoagulant use was associated with an increased risk of major bleeding (aHR 1.37 [1.16-1.63]) but lower risk of the combined outcome of ischaemic stroke, major bleeding, and mortality (aHR 0.57 [0.51-0.63]). Non-anticoagulated individuals with AF had higher risk of ischaemic stroke compared to non-AF individuals with the same risk profile (aHR 2.47 [2.17-2.81])., Conclusions: In AF patients at intermediate risk of stroke, OAC use was associated with overall favourable clinical outcomes. Non-anticoagulated AF patients had higher risk of ischaemic stroke compared to the general population without AF with the same risk profile., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)