[Towards a new way to classify diabetes mellitus]. / Vers une nouvelle classification des diabètes.

Precision medicine makes it possible to classify patients into groups on the basis of molecular and genetic biomarkers, as well as clinical characteristics, in order to optimize therapeutic response. For example, several types of type 2 diabetes seem to coexist with classic insulin-dependent, autoimmune type 1 diabetes : diabetes with insulinopenia (generally severe), diabetes linked to aging or obesity (less severe), and diabetes with insulin resistance, whose patients will be those with the most numerous complications, notably macrovascular. In this article, we examine the possibilities offered by this new classification of diabetes with a view to personalized medicine.

La médecine de précision permet de classer les patients en groupes sur la base de biomarqueurs moléculaires et génétiques ainsi que de caractéristiques cliniques afin d'optimiser la réponse thérapeutique. Ainsi, plusieurs types de diabètes de type 2 semblent coexister à côté du classique diabète de type 1, insulinoprive et avec auto-immunité : des diabètes avec insulinopénie (généralement sévères), des diabètes liés au vieillissement ou à l'obésité (moins sévères), et des diabètes avec insulinorésistance dont les patients porteurs seront ceux qui auront le plus de complications, en particulier macrovasculaires. Dans cet article, nous abordons les possibilités offertes par cette nouvelle classification du diabète vers la perspective d'une médecine personnalisée.

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.

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.

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.

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.

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.

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.

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.

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.

Glycemic control indicator levels at diagnosis of neonatal diabetes mellitus: Comparison with other types of insulin-dependent diabetes mellitus.

BACKGROUND: Neonatal diabetes mellitus (NDM) is a monogenic insulin-dependent diabetes that develops within 6 months of age. The progression of hyperglycemia until diagnosis is unknown. Glycemic control indicators at diagnosis are useful to estimate the extent and duration of hyperglycemia. We recently established that age-adjusted glycated albumin (GA) is a useful indicator of glycemic control, regardless of age. OBJECTIVE: To compare the levels of various glycemic control indicators at diagnosis between NDM and other types of insulin-dependent diabetes mellitus. PATIENTS AND METHODS: We included 8 patients with NDM, 8 with fulminant type 1 diabetes (FT1D), and 24 with acute-onset autoimmune type 1 diabetes (T1AD). Plasma glucose, glycated hemoglobin (HbA1c), GA, and age-adjusted GA (calculated as previously reported) were measured and compared. RESULTS: There were no significant differences in the plasma glucose levels of the group of patients with NDM and those with FT1D or T1AD. HbA1c and GA levels in the NDM group were not significantly different from those in the FT1D group, and both indicators were lower than those in the T1AD group. Age-adjusted GA levels in the NDM group did not differ significantly from those in the T1AD group, but were higher than those in the FT1D group. CONCLUSIONS: These findings suggest that the time-course of plasma glucose elevation in NDM until diagnosis is similar to that in T1AD. In addition, the high age-adjusted GA value at diagnosis of NDM indicates that this test is useful for assessing chronic hyperglycemia in NDM.

The many faces of diabetes: a disease with increasing heterogeneity.

Diabetes is a much more heterogeneous disease than the present subdivision into types 1 and 2 assumes; type 1 and type 2 diabetes probably represent extremes on a range of diabetic disorders. Both type 1 and type 2 diabetes seem to result from a collision between genes and environment. Although genetic predisposition establishes susceptibility, rapid changes in the environment (ie, lifestyle factors) are the most probable explanation for the increase in incidence of both forms of diabetes. Many patients have genetic predispositions to both forms of diabetes, resulting in hybrid forms of diabetes (eg, latent autoimmune diabetes in adults). Obesity is a strong modifier of diabetes risk, and can account for not only a large proportion of the epidemic of type 2 diabetes in Asia but also the ever-increasing number of adolescents with type 2 diabetes. With improved characterisation of patients with diabetes, the range of diabetic subgroups will become even more diverse in the future.

[Classification of diabetes: an increasing heterogeneity]. / Classification du diabète: vers une hétérogénéité croissante.

Diabetes mellitus is usually subdivided into type 1 and type 2. Despite precise criteria, distinction between these two types of diabetes can be difficult because of cases with superposition of the two classes. Adults aged 20 to 40 are particularly at risk of presenting an intermediary type of diabetes and thus are subject to misclassification. The distinction between these subtypes is relevant because of the therapeutic decision and the outcome which relies on insulin supply and therefore the evolution to insulin dependence. Thus, it seems important to review a new and more accurate classification of diabetes to offer a more appropriated care to patients.

RD Lawrence lecture 2013. Stratified approaches to the management of diabetes.

A stratified approach to medicine aims to identify subgroups of patients who should be managed differently from others. Diabetes is a condition that offers considerable potential for stratification, in areas of drug response, complication risk and rate of progression amongst others. Approaches to stratification can be simple, using clinical phenotyping, or more complex involving genomic and other '-omic' technologies. In this review, I will highlight the utility of measuring endogenous insulin production to aid in diagnosis and appropriate treatment; outline key advances in monogenic diabetes where determining genetic aetiology can result in dramatic changes in treatment, and describe the developments in the field of pharmacogenetics in Type 2 diabetes.

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study.

BACKGROUND: The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics. OBJECTIVE: This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age, and race/ethnicity. SUBJECTS: Of 57 767 children aged <20 yr as of 31 December 2011 seen at University of North Carolina Health Care System in 2011 were included. METHODS: Using an initial algorithm including billing data, patient problem lists, laboratory test results, and diabetes related medications between 1 July 2008 and 31 December 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 vs. type 2), age (<10 vs. ≥10 yr) and race/ethnicity (non-Hispanic White vs. 'other'). Sensitivity, specificity, and positive predictive value were calculated and compared. RESULTS: The best algorithm for ascertainment of overall diabetes cases was billing data. The best type 1 algorithm was the ratio of the number of type 1 billing codes to the sum of type 1 and type 2 billing codes ≥0.5. A useful algorithm to ascertain youth with type 2 diabetes with 'other' race/ethnicity was identified. Considerable age and racial/ethnic differences were present in type-non-specific and type 2 algorithms. CONCLUSIONS: Administrative and EHR data may be used to identify cases of childhood diabetes (any type), and to identify type 1 cases. The performance of type 2 case ascertainment algorithms differed substantially by race/ethnicity.

Is it time to challenge the established theories surrounding type 1 diabetes?

UNLABELLED: Type one diabetes (T1D) seems a well-defined disease, but its classification may be difficult. Evidence is weak that an autoimmune process with insulitis causes loss of the beta cells in all patients. Some scientists propose that it may be caused by a virus, increased hygiene or the early introduction of cow's milk or gluten, while views about the nerve supply, vascular function and the beta cells own role tend to be disregarded. Immune interventions have had limited success. There are differences, but also similarities, between T1D and type 2 diabetes (T2D). CONCLUSION: Several views on T1D have become so widely accepted that they may actually hamper progress into the true cause of this disease. Research on T1D needs to be carried out with an open mind, and clinicians might be wise to recommend a lifestyle that aims to decrease both the risk of T1D and T2D.

Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care.

INTRODUCTION: Approximately 366 million people worldwide live with diabetes and this figure is expected to rise. Among the correct diagnosis, there will be errors in the diagnosis, classification and coding, resulting in adverse health and financial implications. AIM: To determine the prevalence and characteristics of diagnostic errors in people with diabetes managed in primary care settings. METHODS: We conducted a cross-sectional study in nine general practices in Leicester, UK, from May to August 2011, using a validated electronic toolkit. Searches identified cases with potential errors which were manually checked for accuracy. RESULTS: There were 54 088 patients and 2434 (4.5%) diagnosed with diabetes. Out of 316 people identified with potential errors with the toolkit, 180 (57%) had confirmed errors after manually reviewing the records, resulting in an error prevalence of 7.4%. Correctly coded people on registers had significantly greater glycated haemoglobin (HbA1c) reductions. There were no significant differences between patients with and without errors in their HbA1C, body mass index, age and size of practice. There was also no significant association of the errors with pay-for-performance initiatives; however, those patients not on disease register had worse glycaemic control. CONCLUSIONS: A high prevalence of diabetic diagnostic errors was confirmed using medication, biochemical and demographic data. Larger studies are needed to more accurately assess the scale of this problem. Automation of these processes might be possible, which would allow searches to be even more user friendly.

Classification of type 1 diabetes: A pathogenic and treatment-based classification.

AIM: To improve the diagnosis and classification of patients who fail to satisfy current type 1 diabetes diagnostic criteria. METHODS: Review of the literature and current diagnostic guidelines. DISCUSSION: We propose a novel, clinically useful classification based on islet autoantibody status and non-fasting C-peptide levels. Notably, we discuss the subgroup of latent autoimmune diabetes in the young and propose a new subgroup classification of autoantibody negative type 1 diabetes in remission. CONCLUSION: A novel classification system is proposed. Further work is needed to accurately diagnose and manage minority type 1 diabetes subgroups.

Improving peptide relative quantification in MALDI-TOF MS for biomarker assessment.

Proteomic profiling by MALDI-TOF MS presents various advantages (speed of analysis, ease of use, relatively low cost, sensitivity, tolerance against detergents and contaminants, and possibility of automation) and is being currently used in many applications (e.g. peptide/protein identification and quantification, biomarker discovery, and imaging MS). Earlier studies by many groups indicated that moderate reproducibility in relative peptide quantification is a major limitation of MALDI-TOF MS. In the present work, we examined and demonstrate a clear effect, in cases apparently random, of sample dilution in complex samples (urine) on the relative quantification of peptides by MALDI-TOF MS. Results indicate that in urine relative abundance of peptides cannot be assessed with confidence based on a single MALDI-TOF MS spectrum. To account for this issue, we developed and propose a novel method of determining the relative abundance of peptides, taking into account that peptides have individual linear quantification ranges in relation to sample dilution. We developed an algorithm that calculates the range of dilutions at which each peptide responds in a linear manner and normalizes the received peptide intensity values accordingly. This concept was successfully applied to a set of urine samples from patients diagnosed with diabetes presenting normoalbuminuria (controls) and macroalbuminuria (cases).