Presencia de autoanticuerpos anti islotes pancreáticos en diabetes tipo 1 durante la edad pediátrica / Presence of anti-pancreatic islet autoantibodies in type 1 diabetes in children

En la Diabetes tipo 1 (DM1) la pérdida de células ß pancreáticas es consecuencia de un proceso de autoinmunidad que cursa con la presencia de autoanticuerpos anti-islotes pancreáticos (AAPs). Estos AAPs son marcadores útiles para la clasificación de la enfermedad. En un centro pediátrico de tercer nivel se analizó la frecuencia de presentación de GADA, IA-2A, ZnT8A e IAA en un grupo con reciente debut entre enero 2018 y agosto 2021 (n= 90). Además, se investigó la frecuencia de presentación y relación de los AAPs con la edad, sexo y tiempo de evolución en pacientes en seguimiento (n= 240). En el grupo de debut se obtuvo positividad de GADA, IA-2A, ZnT8A y IAA en 77,8; 60; 62 y 47,8% de los pacientes respectivamente, un 4% no presentó AAPs. El 95,6% de los pacientes presentaron al menos un AAPs positivo. La frecuencia de IAA en el grupo en debut fue mayor en menores de 5 años. En el grupo en seguimiento el 75,2% resultaron GADA positivo (85,7% en mujeres y 62,8% en varones) p<0,05. IA-2A y ZnT8A fueron positivos en 45 y 51.7% respectivamente. El 91% presentaron al menos un AAP positivo. En este grupo se evidenció una menor positividad en función del tiempo de evolución. Se pudo determinar la frecuencia de presentación de los AAPs en un grupo en debut y la relación con la edad, sexo y tiempo de evolución en pacientes en seguimiento. La determinación de APPs facilita la correcta clasificación y elección de la terapia adecuada (AU)

In type 1 diabetes (DM1) the loss of pancreatic ß-cells is a consequence of an autoimmune process that results in the presence of pancreatic anti-islet autoantibodies (PAAs). PAAs are useful markers for the classification of the disease. The frequency of presentation of GADA, IA-2A, ZnT8A, and IAA in a group with recent debut seen between January 2018 and August 2021 (n= 90) was analyzed in a tertiary pediatric center. In addition, we investigated the frequency of presentation and association of PAAs with age, sex, and time of evolution in patients in follow-up (n= 240). In the debut group, GADA, IA2A, ZnT8A, and IAA positivity was found in 77.8, 60, 62, and 47.8% of patients, respectively; no PAAs were observed in 4% of the patients. Overall, 95.6% presented at least one positive PAA. The frequency of IAA in the debut group was higher in children younger than 5 years. In the follow-up group, 75.2% were GADA positive (85.7% of females and 62.8% of males) p<0.05. IA-2A and ZnT8A were positive in 45 and 51.7% respectively. Ninety-one percent presented with at least one positive PAA. In this group, a lower positivity was evidenced as a function of the time of evolution. The frequency of presentation of PAAs in a debut group and the relationship with age, sex, and time of evolution in patients in follow-up was demonstrated. The assessment of PAAs facilitates the correct classification and choice of adequate therapy (AU)

Planned vaginal and planned cesarean delivery outcomes in pregnancies complicated with pregestational type 1 diabetes - A three-year academic tertiary hospital cohort study.

BACKGROUND: Finland has the world's highest incidence of 62.5/100000 of diabetes mellitus type 1 (DM1) with approximately 400 (1%) DM1 pregnancies annually. Pregnancies complicated by DM1 are accompanied with increased risk for perinatal morbidity and mortality. Timing and mode of delivery are based on the risk of complications, yet the data on labor induction is limited. The aim of this study was to compare delivery outcomes in planned vaginal (VD) and planned cesarean deliveries (CD) in late preterm and term DM1 pregnancies, and to evaluate the feasibility of labor induction. MATERIALS AND METHODS: Pregnant women with DM1, live singleton fetus in cephalic presentation ≥34 gestational weeks delivering in Helsinki University Hospital between January 1st 2017 and December 31st 2019 were included. The primary outcome were the rates of adverse maternal and perinatal outcome. The study population was classified according to the 1980-revised White's classification. Statistical analyses were performed by IBM SPSS Statistics for Windows. RESULTS: Two hundred four women were included, 59.8% (n = 122) had planned VD. The rate of adverse maternal outcome was 27.5% (n = 56), similar between the planned modes of delivery and White classes. The rate of perinatal adverse outcome was 38.7% (n = 79), higher in planned CD (52.4% vs. 29.5%;p = 0.001). The most common adverse perinatal event was respiratory distress (48.8% vs. 23.0%;p <  0.001). The rate of adverse perinatal outcome was higher in White class D + Vascular compared to B + C (45.0% vs. 25.0%, OR after adjustment by gestational age 2.34 [95% CI 1.20-4.50];p = 0.01). The total rate of CD was 63.7% (n = 130), and 39.3% (n = 48) in planned VD. Women with White class D + Vascular more often had emergency CD compared to White Class B + C (48.6% vs. 25.0%;p = 0.009). The rate of labor induction was 51%, being 85.2% in planned VD. The rate of VD in induced labor was 58.7% (n = 61) and the rate of failed induction was 14.1% (n = 15). CONCLUSION: Planned VD was associated with lower rate of adverse perinatal outcome compared to planned CS, with no difference in the rates of adverse maternal outcome. Induction of labor may be feasible option but should be carefully considered in this high-risk population.

The psychological profile of youth and young adults with type 1 diabetes in New Zealand.

BACKGROUND: The psychological status of New Zealanders living with type 1 diabetes (T1D) is unknown. This study's purpose is to determine the prevalence of general wellbeing, diabetes-specific distress, and disordered eating, and explore their relationships with glycemic control. METHODS: Participants were patients aged 15-24 years with T1D (N = 200) who attended their routine multidisciplinary clinic at the Waikato Regional Diabetes Service. They completed questionnaires including the World Health Organization Well-Being Index, the Problem Areas in Diabetes scales, and the Diabetes Eating Problem Survey-Revised. Clinical and demographic information were also collected. RESULTS: Median age of participants was 19.3 years and 14% identified as Maori (indigenous people of Aotearoa New Zealand). Median HbA1c was 73 mmol/mol. One fifth of participants experienced low emotional wellbeing, including 7.5% who experienced likely depression. Diabetes distress was found in 24.1%, and 30.7% experienced disordered eating behaviors. Differences were identified between Maori and non-Maori in measures of diabetes distress and disordered eating, with Maori more likely to score in clinically significant ranges (50% vs. 19.9%; 53.6% vs. 26.7%, p < 0.05). Disordered eating was correlated with HbA1c , body mass index, and social deprivation; diabetes distress was associated with HbA1c and inversely with age (all p < 0.05). CONCLUSIONS: This study is the first of its kind to determine that New Zealanders living with T1D experience significant psychological distress. Research with larger Maori representation is needed to more closely review identified inequities. Replication in other local clinics will help contribute to the ongoing development of normative data for Aotearoa New Zealand.

Prevalence and epidemiological profile of celiac disease in children with type 1 diabetes mellitus: Experience from a tertiary care center of India.

BACKGROUND: The HLA associations of celiac disease (CD) in north Indians differ from that in Europeans. Our dietary gluten is among the highest in the world. Data on CD in people with diabetes (PWD) in north India is scant. OBJECTIVE: To estimate the prevalence and clinical profile of CD in children with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Retrospective review of case records of PWD with onset ≤18 years of age, registered between 2009 and 2020, having at least one anti tissue-transglutaminase (anti-tTG) serology report. RESULTS: Of 583 registered PWD, 398 (68.2%) had celiac serology screening. A positive report was obtained in 66 (16.6%). Of 51 biopsied people, 22 (5.5%) were diagnosed to have CD, 12 in the first 2 years of diabetes onset. Symptomatic CD at diagnosis was seen in 63% (14/22). Age at diabetes onset (median [IQR] age 5.5 years, [2-12]) was lower in PWD and CD compared to PWD alone (10 years, [7-14], p < 0.016). Of 36 biopsied children with anti-tTG >100 au/ml, 20 (55.5%) had CD, while 2 out of 15 (13.3%) of those with lower anti-tTG titer had histopathology suggestive of CD. Of 23 seropositive children not diagnosed with CD, 5 of 8 with anti tTG >100 au/ml, and all 15 with lower anti-tTG, had normalization of titers over the 24 (10-41) months. CONCLUSIONS: Our prevalence of CD is comparable to international data. Celiac disease was common with younger age at onset of T1D and higher titer of celiac serology. A high proportion was symptomatic of CD at diagnosis.

Clinical heterogeneity among pediatric patients with autoimmune type 1 diabetes stratified by immunoglobulin deficiency.

BACKGROUND: Type 1 diabetes (T1D) may coexist with primary immunodeficiencies, indicating a shared genetic background. OBJECTIVE: To evaluate the prevalence and clinical characteristics of immunoglobulin deficiency (IgD) among children with T1D. METHODS: Serum samples and medical history questionnaires were obtained during routine visits from T1D patients aged 4-18 years. IgG, IgA, IgM, and IgE were measured by nephelometry and enzyme-linked immunosorbent assay (ELISA). IgG and IgM deficiency (IgGD, IgMD) were defined as IgG/IgM >2 standard deviations (SD) below age-adjusted mean. IgE deficiency was defined as IgE <2 kIU/L. IgA deficiency (IgAD) was defined as IgA >2 SD below age-adjusted mean irrespective of other immunoglobulin classes (absolute if <0.07 g/L, partial otherwise) and as selective IgAD when IgA >2 SD below age-adjusted mean with normal IgG and IgM (absolute if <0.07 g/L, partial otherwise). RESULTS: Among 395 patients (53.4% boys) with the median age of 11.2 (8.4-13.7) and diabetes duration 3.6 (1.1-6.0) years, 90 (22.8%) were found to have hypogammaglobulinemia. The IgGD and IgAD were the most common each in 40/395 (10.1%). Complex IgD was found in seven patients. Increased odds of infection-related hospitalization (compared to children without any IgD) was related to having any kind of IgD and IgAD; OR (95%CI) = 2.1 (1.2-3.7) and 3.7 (1.8-7.5), respectively. Furthermore, IgAD was associated with having a first-degree relative with T1D OR (95%CI) = 3.3 (1.4-7.6) and suffering from non-autoimmune comorbidities 3.3 (1.4-7.6), especially neurological disorders 3.5 (1.2-10.5). CONCLUSIONS: IgDs frequently coexist with T1D and may be associated with several autoimmune and nonimmune related disorders suggesting their common genetic background.

The accuracy of provider diagnosed diabetes type in youth compared to an etiologic criteria in the SEARCH for Diabetes in Youth Study.

BACKGROUND: Although surveillance for diabetes in youth relies on provider-assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown. METHODS: Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider-assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002-2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance. RESULTS: Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively. CONCLUSION: Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification.

Challenges in the diagnosis of diabetes type in pediatrics.

The incidence of diabetes, both type 1 and type 2, is increasing. Health outcomes in pediatric diabetes are currently poor, with trends indicating that they are worsening. Minority racial/ethnic groups are disproportionately affected by suboptimal glucose control and have a higher risk of acute and chronic complications of diabetes. Correct clinical management starts with timely and accurate classification of diabetes, but in children this is becoming increasingly challenging due to high prevalence of obesity and shifting demographic composition. The growing obesity epidemic complicates classification by obesity's effects on diabetes. Since the prevalence and clinical characteristics of diabetes vary among racial/ethnic groups, migration between countries leads to changes in the distribution of diabetes types in a certain geographical area, challenging the clinician's ability to classify diabetes. These challenges must be addressed to correctly classify diabetes and establish an appropriate treatment strategy early in the course of disease for all. This may be the first step in improving diabetes outcomes across racial/ethnic groups. This review will discuss the pitfalls in the current diabetes classification scheme that is leading to increasing overlap between diabetes types and heterogeneity within each type. It will also present proposed alternative classification schemes and approaches to understanding diabetes type that may improve the timely and accurate classification of pediatric diabetes type.

Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records.

BACKGROUND: Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis. METHODS: We included all people with diabetes (age at diagnosis 1.5-100 years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort. RESULTS: There were 10,196 (T1D n = 60, T2D n = 10,136) and 5101 (T1D n = 43, T2D n = 5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9 years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, or at least 1 insulin prescription within 90 days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ≥ 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ≥ 4, and at least 1 insulin prescription within 90 days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms. CONCLUSIONS: Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.

Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

Genetic Discrimination Between LADA and Childhood-Onset Type 1 Diabetes Within the MHC.

OBJECTIVE: The MHC region harbors the strongest loci for latent autoimmune diabetes in adults (LADA); however, the strength of association is likely attenuated compared with that for childhood-onset type 1 diabetes. In this study, we recapitulate independent effects in the MHC class I region in a population with type 1 diabetes and then determine whether such conditioning in LADA yields potential genetic discriminators between the two subtypes within this region. RESEARCH DESIGN AND METHODS: Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects). RESULTS: The strongest associations in the MHC class II region (rs3957146, ß [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (ß [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, ß [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA. CONCLUSIONS: In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

Clinical Implications of Urinary C-Peptide Creatinine Ratio in Patients with Different Types of Diabetes.

INTRODUCTION: Urinary C-peptide creatinine ratio (UCPCR) is used as a marker of endogenous insulin secretion. This study aims to assess the effectiveness of UCPCR for distinguishing between type 1 diabetes (T1DM) and non-T1DM (monogenic diabetes and T2DM) and predicting therapeutic choices in type 2 diabetes (T2DM) patients. METHODS: Twenty-three patients with genetically confirmed monogenic diabetes (median age 35.0 years (interquartile range 30.0-47.0), 13 (56.5%) men), 56 patients with T1DM (median age 46.0 years (interquartile range 26.5-59.5), 28 (50.0%) men), 136 patients with T2DM (median age 53.0 years (interquartile range 42.0-60.0), 87 (64.0%) men), and 59 healthy subjects (median age 36.0 years (30.0-42.0), 26 (44.1%) men) were included. UCPCR was collected in the morning. Receiver operating characteristic (ROC) curves were used to identify optimal UCPCR cut-off values to differentiate T1DM from non-T1DM. This UCPCR cut-off was used to divide T2DM patients into two groups, and the two groups were compared. RESULTS: The UCPCR was lower in patients with T1DM compared with T2DM, monogenic diabetes, and healthy subjects, while the UCPCR was similar in T2DM and monogenic diabetes. A UCPCR cut-off of ≥0.21 nmol/mmol distinguished between monogenic diabetes and T1DM (area under the curve [AUC], 0.949) with 87% sensitivity and 93% specificity. UCPCR ≥ 0.20 nmol/mmol had 82% sensitivity and 93% specificity for distinguishing between T2DM and T1DM, with an AUC of 0.932. UCPCR was not reliable for distinguishing between monogenic diabetes and T2DM (AUC, 0.605). Twenty-five of 136 (18.4%) T2DM patients had UCPCR ≤ 0.20 nmol/mmol. Compared with T2DM patients with a UCPCR > 0.20 nmol/mmol, T2DM patients with UCPCR ≤ 0.20 nmol/mmol had a lower serum C-peptide (fasting C-peptide, 0.39 nmol/L vs. 0.66 nmol/L, P < 0.001; postprandial C-peptide, 0.93 nmol/L vs. 1.55 nmol/L, P < 0.001), lower BMI (22.8 kg/m2 vs. 25.2 kg/m2, P = 0.006), and higher percentage of insulin or secretagogue therapy (92.0% vs. 59.5%, P = 0.002). CONCLUSIONS: UCPCR is a practical and noninvasive marker that can distinguish between TIDM and T2DM or monogenic diabetes. UCPCR ≤ 0.20 nmol/mmol reflects severe impaired beta cell function and the need for insulin or secretagogue therapy in T2DM patients.

Type 1 diabetes genetic risk score discriminates between monogenic and Type 1 diabetes in children diagnosed at the age of <5 years in the Iranian population.

AIM: To examine the extent to which discriminatory testing using antibodies and Type 1 diabetes genetic risk score, validated in European populations, is applicable in a non-European population. METHODS: We recruited 127 unrelated children with diabetes diagnosed between 9 months and 5 years from two centres in Iran. All children underwent targeted next-generation sequencing of 35 monogenic diabetes genes. We measured three islet autoantibodies (islet antigen 2, glutamic acid decarboxylase and zinc transporter 8) and generated a Type 1 diabetes genetic risk score in all children. RESULTS: We identified six children with monogenic diabetes, including four novel mutations: homozygous mutations in WFS1 (n=3), SLC19A2 and SLC29A3, and a heterozygous mutation in GCK. All clinical features were similar in children with monogenic diabetes (n=6) and in the rest of the cohort (n=121). The Type 1 diabetes genetic risk score discriminated children with monogenic from Type 1 diabetes [area under the receiver-operating characteristic curve 0.90 (95% CI 0.83-0.97)]. All children with monogenic diabetes were autoantibody-negative. In children with no mutation, 59 were positive to glutamic acid decarboxylase, 39 to islet antigen 2 and 31 to zinc transporter 8. Measuring zinc transporter 8 increased the number of autoantibody-positive individuals by eight. CONCLUSIONS: The present study provides the first evidence that Type 1 diabetes genetic risk score can be used to distinguish monogenic from Type 1 diabetes in an Iranian population with a large number of consanguineous unions. This test can be used to identify children with a higher probability of having monogenic diabetes who could then undergo genetic testing. Identification of these individuals would reduce the cost of treatment and improve the management of their clinical course.

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.

BACKGROUND: Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE: This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS: A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS: The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS: Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual's GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

BACKGROUND/OBJECTIVE: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes." METHODS: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). RESULTS: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05). CONCLUSIONS: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.

Diabetes tipo MODY-3, no todo debut es diabetes tipo 1 / MODY 3 diabetes, not every early onset diabetes is type 1 diabetes

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Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes.

Objective: Diagnosis codes might be used for diabetes surveillance if they accurately distinguish diabetes type. We assessed the validity of International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) among health plan members with youth-onset (diagnosis age <20 years) diabetes. Research design and methods: Diabetes case identification and abstraction of diabetes type was done as part of the SEARCH for Diabetes in Youth Study. The gold standard for diabetes type is the physician-assigned diabetes type documented in patients' medical records. Using all healthcare encounters with ICD-10-CM codes for diabetes, we summarized codes within each encounter and determined diabetes type using percent of encounters classified as T2DM. We chose 50% as the threshold from a receiver operating characteristic curve because this threshold yielded the largest Youden's index. Persons with ≥50% T2DM-coded encounters were classified as having T2DM. Otherwise, persons were classified as having T1DM. We calculated sensitivity, specificity, positive and negative predictive values, and accuracy overall and by demographic characteristics. Results: According to the gold standard, 1911 persons had T1DM and 652 persons had T2DM (mean age (SD): 19.1 (6.5) years). We obtained 90.6% (95% CI 88.4% to 92.9%) sensitivity, 96.3% (95% CI 95.4% to 97.1%) specificity, 89.3% (95% CI 86.9% to 91.6%) positive predictive value, 96.8% (95% CI 96.0% to 97.6%) negative predictive value, and 94.8% (95% CI 94.0% to 95.7%) accuracy for discriminating T2DM from T1DM. Conclusions: ICD-10-CM codes can accurately classify diabetes type for persons with youth-onset diabetes, showing promise for rapid, cost-efficient diabetes surveillance.

Disease-Modifying Therapies in Type 1 Diabetes: A Look into the Future of Diabetes Practice.

The novel understanding that the presence of multiple islet autoantibodies, indicating islet autoimmunity, inevitably leads to type 1 diabetes mellitus (T1DM) has necessitated the development of a new staging classification system for the condition. Coupled with an improved understanding of the disease course, the realization that T1DM appears to be more heterogeneous than previously thought has led to unique opportunities to develop more targeted therapies that may be applied even before the onset of dysglycemia or symptoms. To date, several therapies have been trialed to delay or halt disease progression in both presymptomatic and clinical T1DM, each demonstrating varying degrees of effectiveness, toxicity, and utility. Key research supports the eventual implementation of immunotherapy in autoimmune diabetes, potentially calling for a paradigm shift among care providers. It will likely be necessary to develop new approaches to trial design and to address potential barriers to progress before an effective treatment for the disease may be achieved.