Diabetes tipo 1 en pediatría durante la pandemia por COVID-19: tiempo de evolución de síntomas y formas de presentación en un centro de referencia / Type 1 diabetes in pediatrics during the COVID-19 pandemic: Time from symptom onset and forms of presentation at a referral hospital

Introducción. La pandemia por COVID-19 afectó la atención de pacientes con diabetes mellitus tipo 1 (DM1). Además, se reportó un aumento de cetoacidosis diabética (CAD) como forma de diagnóstico. Objetivos. Evaluar si durante la pandemia por COVID-19 se modificaron el tiempo de evolución de síntomas, las causas de hospitalización por DM1 y la proporción de formas graves, y describir la infección por SARS-CoV-2 en estos pacientes. Población y métodos. Estudio transversal que incluyó pacientes menores de 19 años hospitalizados por DM1 en un centro pediátrico de referencia de marzo de 2018 a agosto de 2019 (prepandemia) y de marzo de 2020 a agosto de 2021 (pandemia). Resultados. Se analizaron 231 internaciones, 135 prepandemia y 96 en pandemia. Los pacientes con debut diabético presentaron menor tiempo de evolución de síntomas en pandemia que en prepandemia (18,8 ± 10,2 vs. 52,1 ±12,1 días, respectivamente; p <0,001). Las hospitalizaciones por todas las formas de debut diabético y el debut con CAD fueron más frecuentes en pandemia que en prepandemia (59,4 % vs. 39,3 %; OR 2,3; IC95% 1,3-3,8; p = 0,003); y (40,6 % vs. 20,7 %; OR 2,6; IC95% 1,4-5,2; p = 0,006) respectivamente. La proporción de formas graves de CAD no se modificó entre ambos períodos (48,1 % vs. 59,9 %; p = 0,3). Solo 6 pacientes presentaron infección por SARS-CoV-2; 3 fueron formas graves. Conclusión. Durante la pandemia, disminuyó el tiempo de evolución de síntomas y aumentó la frecuencia de hospitalizaciones por debut de DM1, con mayor proporción de CAD. No se modificó la proporción de formas graves de CAD

Introduction. The COVID-19 pandemic impacted on the health care of patients with type 1 diabetes mellitus (DM1). An increase in diabetic ketoacidosis (DKA) as a form of diagnosis was reported. Objectives. To assess whether there were changes in the time from symptom onset, the causes of hospitalization due to DM1, and the proportion of severe forms, and to describe SARS-CoV-2 infection in these patients. Population and methods. Cross-sectional study in patients younger than 19 years hospitalized due to DM1 from March 2018 to August 2019 (pre-pandemic) and from March 2020 to August 2021 (pandemic). Results. The assessment included 135 hospitalizations in the pre-pandemic period and 96 during the pandemic. The time from symptom onset during the pandemic in those with debutof diabetes was shorter than in the pre-pandemic period (18.8 ± 10.2 versus 52.1 ± 12.1 days, respectively; p < 0.001). Hospitalizations due to all forms of diabetes debut and debut with DKA were more common during the pandemic than before it (59.4% versus 39.3%; odds ratio [OR]: 2.3; 95% confidence interval [CI]: 1.3­3.8; p = 0.003 and 40.6% versus 20.7%; OR: 2.6; 95% CI: 1.4­5.2; p = 0.006, respectively). Severe forms of DKA did not change between both periods (48.1% versus 59.9%; p = 0.3). Only 6 patients developed SARS-CoV-2 infection; 3 were severe. Conclusion. During the pandemic, the time from symptom onset decreased and the frequency of hospitalizations due to debut of DM1 increased. The proportion of severe forms of DKA did not change.

Macrolipasemia variant of macroenzymes: An endocrine laboma.

Macroenzymes, formed by polymerization of physiological enzymes with immunoglobulins, have slower renal clearance rates due to their higher molecular mass. They are usually incidentally detected, have no pathophysiological importance, and can potentially lead to over-treatment and iatrogenic morbidity. We present, possibly for the first time, a macro-lipasemia variant of macroenzyme, detected in a 14-year-old girl with type-1 diabetes admitted with severe hyperglycaemia and pain abdomen. Raised lipase levels (414 U/L), initially raised the suspicion of underlying pancreatitis, which was ruled out by the clinical symptoms and normal ultrasound and CT imaging of the pancreas. Upper gastrointestinal endoscopy revealed pangastritis, which could explain the mild upper abdominal pain in the child. She improved with proton pump inhibitor therapy and was discharged after 5 days of hospital admission after good glycaemic control using multiple subcutaneous injections of insulin. Post-polyethylene glycol (PEG) precipitation, the recovery of lipase activity in PEG treated serum sample was 30.6% (127 U/L), which confirmed the presence of macrolipase. An increased clinical suspicion and performing a cheap reliable test (PEG precipitation), whenever there is clinical biochemical discordance can help us in diagnosing more patients with macroenzymes and macrolipasemia.

Clinical features, biomarkers and diabetic ketoacidosis at diagnosis of type 1 diabetes among children and adolescents in Sana'a, Yemen.

INTRODUCTION: There is little published information on type 1 diabetes (T1D) in children in Yemen. We aimed to identify the clinical characteristics, biomarkers and diabetic ketoacidosis (DKA) at diagnosis of T1D among children and adolescents in a diabetes centre in Sana'a, Yemen. METHODS: A total of 485 children and adolescents aged ≤18 years diagnosed with T1D during the period 2010-2020 were included in the study. The variables investigated were demographic and clinical characteristics, biomarkers, subtypes of T1D, and the risk factors for severe DKA at diagnosis. RESULTS: At diagnosis, children aged <10 years compared with those aged ≥10 years had higher mean plasma glucose (p<0.001) and mean HbA1c (p=0.026), and lower mean C-peptide (pmol/L) (p=0.019), and a higher frequency of DKA at diagnosis than older children (p<0.001). A majority of the study population (383, 79%) presented in DKA . Children aged <10 years presenting with DKA had significantly longer median appraisal interval (p=0.009) and median total diagnosis interval (p=0.025), and significantly lower mean C-peptide (p=0.001) as compared with their peers without DKA. The prevalence of autoantibody-negative 'idiopathic' T1D was 36 (32%) of the total number tested for autoantibody and familial T1D 61 (12.6%) of all the study population. CONCLUSION: In Yemen children aged <10 years with new-onset T1D frequently faced the challenge of a delay in diagnosis and treatment initiation, with severe hyperglycaemia and a higher risk of DKA at diagnosis.

Determinants of vascular impairment in type 1 diabetes-impact of sex and connexin 37 gene polymorphism: A cross-sectional study.

BACKGROUND: The associations of risk factors with vascular impairment in type 1 diabetes patients seem more complex than that in type 2 diabetes patients. Therefore, we analyzed the associations between traditional and novel cardiovascular risk factors and vascular parameters in individuals with T1D and modifications of these associations according to sex and genetic factors. METHODS: In a cross-sectional study, we analyzed the association of risk factors in T1D individuals younger than 65 years using vascular parameters, such as ankle brachial index (ABI) and toe brachial index (TBI), duplex ultrasound, measuring the presence of plaques in carotid and femoral arteries (Belcaro score) and intima media thickness of carotid arteries (CIMT). We also used photoplethysmography, which measured the interbranch index expressed as the Oliva-Roztocil index (ORI), and analyzed renal parameters, such as urine albumin/creatinine ratio (uACR) and glomerular filtration rate (GFR). We evaluated these associations using multivariate regression analysis, including interactions with sex and the gene for connexin 37 (Cx37) polymorphism (rs1764391). RESULTS: In 235 men and 227 women (mean age 43.6 ± 13.6 years; mean duration of diabetes 22.1 ± 11.3 years), pulse pressure was strongly associated with unfavorable values of most of the vascular parameters under study (ABI, TBI, Belcaro scores, uACR and ORI), whereas plasma lipids, represented by remnant cholesterol (cholesterol - LDL-HDL cholesterol), the atherogenic index of plasma (log (triglycerides/HDL cholesterol) and Lp(a), were associated primarily with renal impairment (uACR, GFR and lipoprotein (a)). Plasma non-HDL cholesterol was not associated with any vascular parameter under study. In contrast to pulse pressure, the associations of lipid factors with kidney and vascular parameters were modified by sex and the Cx37 gene. CONCLUSION: In addition to known information, easily obtainable risk factor, such as pulse pressure, should be considered in individuals with T1D irrespective of sex and genetic background. The associations of plasma lipids with kidney function are complex and associated with sex and genetic factors. The decision of whether pulse pressure, remnant lipoproteins, Lp(a) and other determinants of vascular damage should become treatment targets in T1D should be based on the results of future clinical trials.

Association between HLA alleles and haplotypes with age at diagnosis of type 1 diabetes in an admixed Brazilian population: A nationwide study.

To investigate the potential relationship between HLA alleles and haplotypes and the age at diagnosis of type 1 diabetes (T1DAgeD) in an admixed Brazilian population. This nationwide study was conducted in public clinics across 12 Brazilian cities. We collected demographic and genetic data from 1,600 patients with T1D. DNA samples were utilised to determine genomic ancestry (GA) and perform HLA typings for DRB1, DQA1 and DQB1. We explored allele and haplotype frequencies and GA in patients grouped by T1DAgeD categories (<6 years, ≥6-<11 years, ≥11-<19 years and ≥19 years) through univariate and multivariate analyses and primary component analyses. Additionally, we considered self-reported colour-race and identified a familiar history of T1D in first-degree relatives. The homozygosity index for DRB1~DQA1~DQB1 haplotypes exhibited the highest variation among T1DAgeD groups, and the percentages of Sub-Saharan African and European ancestries showed opposite trends in principal component analysis (PCA) analyses. Regarding the association of alleles and haplotypes with T1DAgeD, risk alleles such as HLA-DQB1*03:02g, -DQA1*03:01g, -02:01g, DRB1*04:05g and -04:02g were more frequently observed in heterozygosity or homozygosity in T1D patients with an early disease onset. Conversely, alleles such as DRB1*07:01g, -13:03g, DQB1*06:02g and DQA1*02:01 were more prevalent in older T1D patients. The combination DR3/DR4.5 was significantly associated with early disease onset. However, gender, GA, familiar history of T1D and self-reported colour-race identity did not exhibit significant associations with the onset of T1D. It is worth noting that the very common risk haplotype DRB1*03:01g~DQA1*05:01g~DQB1*02:01g did not differentiate between T1DAgeD groups. In the admixed Brazilian population, the high-risk haplotype DRB1*04:05~DQA1*03:01~DQB1*03:02 was more prevalent in individuals diagnosed before 6 years of age. In contrast, the protective alleles DQA1*01:02g, DQB1*06:02g, DRB1*07:01g and DRB1*13:03g and haplotypes DRB1*13:03g~DQA1*05:01g~DQB1*03:01g and DRB1*16:02g~DQA1*01:02g~DQB1*05:02g were more frequently observed in patients diagnosed in adulthood. Notably, these associations were independent of factors such as sex, economic status, GA, familiar history of T1D and region of birth in Brazil. These alleles and haplotypes contribute to our understanding of the disease onset heterogeneity and may have implications for early interventions when detected in association with well-known genomic risk or protection factors for T1D.

Estimation of Individual Positive Anti-Islet Autoantibodies from 3 Screen ICA Titer.

The 3 Screen ICA ELISA is a novel assay capable of simultaneously measuring autoantibodies to glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), making it a valuable tool for screening type 1 diabetes. Despite its advantages, it cannot specify which individual autoantibodies are positive or negative. This study aimed to estimate individual positive autoantibodies based on the 3 Screen ICA titer. Six hundred seventeen patients with type 1 diabetes, simultaneously measured for 3 Screen ICA and three individual autoantibodies, were divided into five groups based on their 3 Screen ICA titer. The sensitivities and contribution rates of the individual autoantibodies were then examined. The study had a cross-sectional design. Sixty-nine percent (424 of 617) of patients with type 1 diabetes had 3 Screen ICA titers exceeding the 99th percentile cut-off level (20 index). The prevalence of GADA ranged from 80% to 100% in patients with a 3 Screen ICA over 30 index and 97% of patients with a 3 Screen ICA ≥300 index. Furthermore, the prevalence of all individual autoantibodies being positive was 0% for ≤80 index and as high as 92% for ≥300 index. Significant associations were observed in specific titer groups: the 20-29.9 index group when all the individual autoantibodies were negative, the 30-79.9 index group when positive for GADA alone or IA-2A alone, the 30-299.9 index group when positive for ZnT8A alone, the 80-299.9 index group when positive for both IA-2A and ZnT8A, the 300-499.9 index group when positive for both GADA and ZnT8A, and the ≥300 index group when positive for all individual autoantibodies. These results suggest that the 3 Screen ICA titer may be helpful in estimating individual positive autoantibodies.

High-Density Lipoprotein Particle Concentration and Size Predict Incident Coronary Artery Disease Events in a Cohort With Type 1 Diabetes.

BACKGROUND: The cholesterol efflux capacity of high density lipoprotein (HDL) is negatively associated with cardiovascular risk. Small HDL particles account almost quantitatively for cholesterol efflux capacity, perhaps mediated through efflux of cholesterol and outer leaflet plasma membrane phospholipids by ABCA1 (ATP binding cassette subfamily A member 1). People with type 1 diabetes are at increased coronary artery disease (CAD) risk despite normal HDL-cholesterol concentrations. We therefore tested the hypothesis that small HDL particles (HDL-P)-rather than HDL-cholesterol-predict incident CAD in type 1 diabetes. METHODS AND RESULTS: Incident CAD (CAD death, myocardial infarction, or coronary revascularization) was determined in 550 individuals with childhood-onset type 1 diabetes. HDL-P was quantified by calibrated ion mobility analysis and cholesterol efflux capacity was quantified with validated assays. During a median follow-up of 26 years, 36.5% of the participants developed incident CAD, for an incidence density of 181.3 per 10 000 person-years. In multivariable Cox models, neither HDL-cholesterol nor apolipoprotein A1 concentration was significantly associated with CAD risk. In contrast, higher extra-small HDL-P concentrations were significantly associated with decreased CAD risk (hazard ratio [HR], 0.26 [95% CI, 0.14-0.50]). Weaker associations were observed for total HDL-P (HR, 0.88 [95% CI, 0.83-0.93]), small HDL (HR, 0.83 [95% CI, 0.68-1.02]), medium HDL (HR, 0.79 [95% CI, 0.71-0.89]), and large HDL (HR, 0.72 [95% CI, 0.59-0.89]). Although cholesterol efflux capacity was negatively associated with incident CAD, this association was no longer significant after adjustment for total HDL-P. CONCLUSIONS: Lower concentrations of total HDL-P and HDL subpopulations were positively associated with incident CAD independently of HDL-cholesterol, apolipoprotein A1, and other common CVD risk factors. Extra-small HDL was a much stronger predictor of risk than the other HDLs. Our data are consistent with the proposal that extra-small HDL plays a critical role in cardioprotection in type 1 diabetes, mediated by macrophage cholesterol efflux by the ABCA1 pathway.

Protein glycation products associate with progression of kidney disease and incident cardiovascular events in individuals with type 1 diabetes.

BACKGROUND: Despite improved glycemic treatment, the impact of glycation on pathological consequences may persist and contribute to adverse clinical outcomes in diabetes. In the present study we investigated the association between serum protein glycation products and progression of kidney disease as well as incident major adverse cardiovascular events (MACE) in type 1 diabetes. METHODS: Fructosamine, advanced glycation end products (AGEs), and methylglyoxal-modified hydro-imidazolone (MG-H1) were measured from baseline serum samples in the FinnDiane study (n = 575). Kidney disease progression was defined as steep eGFR decline (> 3 mL/min/1.73 m2/year) or progression of albuminuria (from lower to higher stage of albuminuria). MACE was defined as acute myocardial infarction, coronary revascularization, cerebrovascular event (stroke), and cardiovascular death. RESULTS: Fructosamine was independently associated with steep eGFR decline (OR 2.15 [95% CI 1.16-4.01], p = 0.016) in the fully adjusted model (age, sex, baseline eGFR). AGEs were associated with steep eGFR decline (OR 1.58 per 1 unit of SD [95% CI 1.07-2.32], p = 0.02), progression to end-stage kidney disease (ESKD) (HR 2.09 per 1 unit of SD [95% CI 1.43-3.05], p < 0.001), and pooled progression (to any stage of albuminuria) (HR 2.72 per 1 unit of SD [95% CI 2.04-3.62], p < 0.001). AGEs (HR 1.57 per 1 unit of SD [95% CI 1.23-2.00], p < 0.001) and MG-H1 (HR 4.99 [95% CI 0.98-25.55], p = 0.054) were associated with incident MACE. MG-H1 was also associated with pooled progression (HR 4.19 [95% CI 1.11-15.89], p = 0.035). Most AGEs and MG-H1 associations were no more significant after adjusting for baseline eGFR. CONCLUSIONS: Overall, these findings suggest that protein glycation products are an important risk factor for target organ damage in type 1 diabetes. The data provide further support to investigate a potential causal role of serum protein glycation in the progression of diabetes complications.

Nonpharmacological interventions on glycated haemoglobin in youth with type 1 diabetes: a Bayesian network meta-analysis.

The available evidence on the impact of specific non-pharmacological interventions on glycaemic control is currently limited. Consequently, there is a need to determine which interventions could provide the most significant benefits for the metabolic health of young individuals with type 1 diabetes mellitus. The aim of this study was to identify optimal nonpharmacological interventions on glycaemic control, measured by glycated haemoglobin (HbA1c), in children and adolescents with type 1 diabetes. Systematic searches were conducted in PubMed, Web of Science, Scopus, and SPORTDiscus from inception to July 1, 2023. Randomised clinical trials (RCT) investigating nonpharmacological interventions (e.g., physical activity, nutrition, and behavioural therapies) were included. Primary outcome was change in HbA1c levels. Secondary outcome was change in daily insulin dose requirement. Seventy-four RCT with 6,815 participants (49.43% girls) involving 20 interventions were analysed using a network meta-analysis. Most interventions showed greater efficacy than standard care. However, multicomponent exercise, which includes aerobic and strength training (n = 214, standardised mean difference [SMD] =- 0.63, 95% credible interval [95% CrI] - 1.09 to - 0.16) and nutritional supplements (n = 146, SMD =- 0.49, - 0 .92 to - 0.07) demonstrated the greatest HbA1c reductions. These interventions also led to the larger decreases in daily insulin needs (n = 119, SMD =- 0.79, 95% CrI -  1.19 to - 0.34) and (n = 57, SMD =- 0.62, 95% CrI -  1.18 to - 0.12, respectively). The current study underscores non-pharmacological options such as multicomponent exercise and nutritional supplements, showcasing their potential to significantly improve HbA1c in youth with type 1 diabetes. Although additional research to confirm their efficacy is required, these approaches could be considered as potential adjuvant therapeutic options in the management of type 1 diabetes among children and adolescents.

An online tool using clinical factors to estimate the probability of partial clinical remission of adult-onset Type 1 diabetes.

A type 1 diabetes (T1D) diagnosis is often followed by a period of reduced exogenous insulin requirement, with acceptable glucose control, called partial clinical remission (pCR). Various criteria exist to define pCR, which is associated with better clinical outcomes. We aimed to develop formulae and a related online calculator to predict the probability of pCR at 3- and 12-months post-T1D diagnosis. We analysed data from 133 adults at their T1D diagnosis (mean ± SD age: 27 ± 6 yrs., HbA1c 11.1 ± 2.0 %, 98 ± 22 mmol/mol), 3- and 12-months later. All patients were enrolled in the prospective observational InLipoDiab1 study (NCT02306005). We compared four definitions of pCR: 1) stimulated C-peptide >300 pmol/l; 2) insulin dose-adjusted HbA1c ≤9 %; 3) insulin dose <0.3 IU/kg/24 h; and HbA1c ≤6.4 % (46 mmol/mol); and 4) insulin dose <0.5 IU/kg/24 h and HbA1c <7 % (53 mmol/mol). Using readily available demographics and clinical chemistry data exhaustive search methodology was used to model pCR probability. There was low concordance between pCR definitions (kappa 0.10). The combination of age, HbA1c, diastolic blood pressure, triglycerides and smoking at T1D onset predicted pCR at 12-months with an area under the curve (AUC) = 0.87. HbA1c, triglycerides and insulin dose 3-mths post-diagnosis had an AUC = 0.89. A related calculator for pCR in adult-onset T1D is available at http://www.bit.ly/T1D-partial-remission.

A high-volume study on the impact of diabetes mellitus on clinical outcomes after surgical and percutaneous cardiac interventions.

BACKGROUND: Type I and type II diabetes mellitus (DM) patients have a higher prevalence of cardiovascular diseases, as well as a higher mortality risk of cardiovascular diseases and interventions. This study provides an update on the impact of DM on clinical outcomes, including mortality, complications and reinterventions, using data on percutaneous and surgical cardiac interventions in the Netherlands. METHODS: This is a retrospective, nearby nationwide study using real-world observational data registered by the Netherlands Heart Registration (NHR) between 2015 and 2020. Patients treated for combined or isolated coronary artery disease (CAD) and aortic valve disease (AVD) were studied. Bivariate analyses and multivariate logistic regression models were used to evaluate the association between DM and clinical outcomes both unadjusted and adjusted for baseline characteristics. RESULTS: 241,360 patients underwent the following interventions; percutaneous coronary intervention(N = 177,556), coronary artery bypass grafting(N = 39,069), transcatheter aortic valve implantation(N = 11,819), aortic valve replacement(N = 8,028) and combined CABG and AVR(N = 4,888). The incidence of DM type I and II was 21.1%, 26.7%, 17.8%, 27.6% and 27% respectively. For all procedures, there are statistically significant differences between patients living with and without diabetes, adjusted for baseline characteristics, at the expense of patients with diabetes for 30-days mortality after PCI (OR = 1.68; p <.001); 120-days mortality after CABG (OR = 1.35; p <.001), AVR (OR = 1.5; p <.03) and CABG + AVR (OR = 1.42; p =.02); and 1-year mortality after CABG (OR = 1.43; p <.001), TAVI (OR = 1.21; p =.01) and PCI (OR = 1.68; p <.001). CONCLUSION: Patients with DM remain to have unfavourable outcomes compared to nondiabetic patients which calls for a critical reappraisal of existing care pathways aimed at diabetic patients within the cardiovascular field.

Glycemic Control Assessed by Intermittently Scanned Glucose Monitoring in Type 1 Diabetes during the COVID-19 Pandemic in Austria.

OBJECTIVE: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. METHODS: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. RESULTS: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR[70-180mg/dL]) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR[<70mg/dL]) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70-180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[<70mg/dL] and the TAR[>180mg/dL] underscored the data in the main analysis. CONCLUSION: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.

Association between perinatal and obstetric factors and early age at diagnosis of type 1 diabetes mellitus: a cohort study.

OBJECTIVES: To evaluate the association between perinatal and obstetric factors as potential triggers for the early onset of T1DM. METHODS: This was a retrospective cohort study enrolling 409 patients diagnosed with T1DM, in Bauru, São Paulo, Brazil, from 1981 to 2023. Data were retrieved from medical records, regarding sociodemographic parameters as age, sex, ethnicity, and socioeconomic status. Perinatal and obstetric factors as delivery type, gestational age, filiation order, length of exclusive breastfeeding, maternal age, maternal and fetal blood types, and occurrence of maternal gestational diabetes were also analyzed. An adapted survival analysis was employed to gauge the impact of each assessed variable at the age of T1DM diagnosis. RESULTS: The median age of T1DM diagnosis was 10.3 years with an interquartile range between 6.4 and 15.5 years. Delivery type and filiation order were the only factors statistically significantly associated with an early age at T1DM diagnosis. Patients who were born through cesarean section and who were firstborns showed a 28.6 and 18.0 % lower age at T1DM diagnosis, respectively, compared to those born through vaginal delivery and those that were nonfirstborns. CONCLUSIONS: Being born by cesarean section and being firstborn showed to be statistically significant factors to determine an early T1DM diagnosis.

New Data on GLP-1s, Diabetic Retinopathy, and CGMs-Highlights From the ADA's Scientific Sessions.

This Medical News article is an interview with Robert Gabbay, MD, PhD, the American Diabetes Association's chief scientific and medical officer.

Progression From Presymptomatic to Clinical Type 1 Diabetes After COVID-19 Infection.

This study examines whether an association exists between COVID-19 infection and progression to clinical diabetes among youth with presymptomatic type 1 diabetes.

Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes.

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

Association between low-carbohydrate-diet score, glycemia and cardiovascular risk factors in adults with type 1 diabetes.

BACKGROUND AND AIMS: Low-carbohydrate-diets (LCDs) are gaining popularity in individuals with type 1 diabetes (T1D). However, the impact of such diets on glycemia and cardiovascular risk factors is debated. This study aims to evaluate associations between low-carbohydrate intakes using LCD score with glycemia and cardiovascular risk factors (lipid profile) in adults with T1D or LADA in Québec, Canada. METHODS AND RESULTS: This is a cross-sectional study using data collected in the BETTER registry (02/2019 and 04/2021) including self-reported 24-h dietary recalls to calculate LCD scores, waist circumference, level-2 and level-3 hypoglycemic episodes and measured biochemical data (HbA1c, LDL-cholesterol and non-HDL-cholesterol). Participants were divided into quartiles (Q) based on LCD scores. Two hundred eighty-five adults (aged 48.2 ± 15.0 years; T1D duration 25.9 ± 16.2 years) were included. Categorical variables underwent Chi-squared/Fisher's Exact tests, while continuous variables underwent ANOVA tests. Mean carbohydrate intake ranged from 31.2 ± 6.9% (Q1) to 56.5 ± 6.8% (Q4) of total daily energy. Compared to Q4, more people in Q1 reported HbA1c ≤ 7% [≤53.0 mmol/mol] (Q1: 53.4% vs. Q4: 29.4%; P = 0.011). The same results were found in the models adjusted for age, sex and T1D duration. A greater proportion of participants in Q1 never experienced level-3 hypoglycemia compared to Q3 (Q1: 60.0% vs. Q3: 31.0%; P = 0.004). There were no differences across quartiles for frequency of level-2 hypoglycemia events and lipid profile (LDL-cholesterol and non-HDL-cholesterol). CONCLUSIONS: Low-carbohydrate intakes are associated with higher probabilities of reaching HbA1c target and of never having experienced level-3 hypoglycemia. No associations with level-2 hypoglycemia frequency, nor cardiovascular risk factors were observed.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young.

BACKGROUND: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. METHODS: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. RESULTS: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. CONCLUSION: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.