Screening for type 1 diabetes: are we nearly there yet?

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm

BACKGROUND

Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threatening diabetic ketoacidosis. However, early recognition of children with type 1 diabetes is challenging. Children might not present with classic symptoms, or symptoms might be attributed to more common conditions. A quarter of children present with diabetic ketoacidosis, a proportion unchanged over 25 years. Our aim was to investigate whether a machine-learning algorithm could lead to earlier detection of type 1 diabetes in primary care.

METHODS

We developed the predictive algorithm using Welsh primary care electronic health records (EHRs) linked to the Brecon Dataset, a register of children newly diagnosed with type 1 diabetes. Children were included from their first primary care record within the study period of Jan 1, 2000, to Dec 31, 2016, until either type 1 diabetes diagnosis, they turned 15 years of age, or study end. We developed an ensemble learner (SuperLearner) using 26 potential predictors. Validation of the algorithm was done in English EHRs from the Clinical Practice Research Datalink (primary care) and Hospital Episode Statistics, focusing on the ability of the algorithm to identify children who went on to develop type 1 diabetes and the time by which diagnosis could be anticipated.

FINDINGS

The development dataset comprised 34 754 400 primary care contacts, relating to 952 402 children, and the validation dataset comprised 43 089 103 primary care contacts, relating to 1 493 328 children. Of these, 1829 (0·19%) children younger than 15 years in the development dataset, and 1516 (0·10%) in the validation dataset had a reliable date of type 1 diabetes diagnosis. If set to give an alert in 10% of contacts, an estimated 71·6% (95% CI 68·8-74·4) of the children with type 1 diabetes would receive an alert by the algorithm in the 90 days before diagnosis, with diagnosis anticipated, on average, by an estimated 9·34 days (95% CI 7·77-10·9).

INTERPRETATION

If implemented into primary care settings, this predictive algorithm could substantially reduce the proportion of patients with new-onset type 1 diabetes presenting in diabetic ketoacidosis. Acceptability of alert thresholds should be explored in primary care.

FUNDING

Diabetes UK.

Feasibility and Validity of In-Home Self-Collected Capillary Blood Spot Screening for Type 1 Diabetes Risk

Aims: Self-collection of a blood sample for autoantibody testing has potential to facilitate screening for type 1 diabetes risk. We sought to determine the feasibility and acceptability of this approach and the performance of downstream antibody assays. Methods: People living with type 1 diabetes and their family members (N = 97) provided paired capillary blood spot and serum samples collected, respectively, by themselves and a health worker. They provided feedback on the ease, convenience, and painfulness of blood spot collection. Islet antibodies were measured in blood spots by antibody detection by agglutination PCR (ADAP) or multiplex enzyme-linked immunoassay (ELISA), and in serum by radioimmunoassay (RIA) or ELISA. Results: Using serum RIA and ELISA to define antibody status, 50 antibody-negative (Abneg) and 47 antibody-positive (Abpos) participants enrolled, of whom 43 and 47, respectively, returned testable blood spot samples. The majority indicated that self-collection was easier, more convenient, and less painful than formal venesection. The sensitivity and specificity for detection of Abpos by blood spot were, respectively, 85% and 98% for ADAP and 87% and 100% for multiplex ELISA. The specificities by ADAP for each of the four antigen specificities ranged from 98% to 100% and areas under the receiver operator curve from 0.841 to 0.986. Conclusions: Self-collected blood spot sampling is preferred over venesection by research participants. ADAP and multiplex ELISA are highly specific assays for islet antibodies in blood spots with acceptable performance for use alone or in combination to facilitate screening for type 1 diabetes risk. Clinical Trial Registration number: ACTRN12620000510943.

Understanding Providers' Readiness and Attitudes Toward Autoantibody Screening: A Mixed-Methods Study

Screening for autoantibodies associated with type 1 diabetes can identify people most at risk for progressing to clinical type 1 diabetes and provide an opportunity for early intervention. Drawbacks and barriers to screening exist, and concerns arise, as methods for disease prevention are limited and no cure exists today. The availability of novel treatment options such as teplizumab to delay progression to clinical type 1 diabetes in high-risk individuals has led to the reassessment of screening programs. This study explored awareness, readiness, and attitudes of endocrinology providers toward type 1 diabetes autoantibody screening.

Simulating Screening for Risk of Childhood Diabetes: The Collaborative Open Outcomes tooL (COOL)

The Collaborative Open Outcomes tooL (COOL) is a novel, highly configurable application to simulate, evaluate and compare potential population-level screening schedules. Its first application is type 1 diabetes (T1D) screening, where known biomarkers for risk exist but clinical application lags behind. COOL was developed with the T1DI Study Group, in order to assess screening schedules for islet autoimmunity development based on existing datasets. This work shows clinical research utility, but the tool can be applied in other contexts. COOL helps the user define and evaluate a domain knowledge-driven screening schedule, which can be further refined with data-driven insights. COOL can also compare performance of alternative schedules using adjusted sensitivity, specificity, PPV and NPV metrics. Insights from COOL may support a variety of needs in disease screening and surveillance.

HOMA2-B enhances assessment of type 1 diabetes risk among TrialNet Pathway to Prevention participants

AIMS/HYPOTHESIS

Methods to identify individuals at highest risk for type 1 diabetes are essential for the successful implementation of disease-modifying interventions. Simple metabolic measures are needed to help stratify autoantibody-positive (Aab+) individuals who are at risk of developing type 1 diabetes. HOMA2-B is a validated mathematical tool commonly used to estimate beta cell function in type 2 diabetes using fasting glucose and insulin. The utility of HOMA2-B in association with type 1 diabetes progression has not been tested.

METHODS

Baseline HOMA2-B values from single-Aab+ (n = 2652; mean age, 21.1 ± 14.0 years) and multiple-Aab+ (n = 3794; mean age, 14.5 ± 11.2 years) individuals enrolled in the TrialNet Pathway to Prevention study were compared. Cox proportional hazard models were used to determine associations between HOMA2-B tertiles and time to progression to type 1 diabetes, with adjustments for age, sex, HLA status and BMI z score. Receiver operating characteristic (ROC) analysis was used to test the association of HOMA2-B with type 1 diabetes development in 1, 2, 5 and 10 years.

RESULTS

At study entry, HOMA2-B values were higher in single- compared with multiple-Aab+ Pathway to Prevention participants (91.1 ± 44.5 vs 83.9 ± 38.9; p < 0.001). Single- and multiple-Aab+ individuals in the lowest HOMA2-B tertile had a higher risk and faster rate of progression to type 1 diabetes. For progression to type 1 diabetes within 1 year, area under the ROC curve (AUC-ROC) was 0.685, 0.666 and 0.680 for all Aab+, single-Aab+ and multiple-Aab+ individuals, respectively. When correlation between HOMA2-B and type 1 diabetes risk was assessed in combination with additional factors known to influence type 1 diabetes progression (insulin sensitivity, age and HLA status), AUC-ROC was highest for the single-Aab+ group's risk of progression at 2 years (AUC-ROC 0.723 [95% CI 0.652, 0.794]).

CONCLUSIONS/INTERPRETATION

These data suggest that HOMA2-B may have utility as a single-time-point measurement to stratify risk of type 1 diabetes development in Aab+ individuals.

Masqueraders: how to identify atypical diabetes in primary care

Diabetes mellitus is a complex set of conditions that impacts 34 million Americans. While type 1 diabetes, type 2 diabetes, and gestational diabetes are most frequently encountered, there are many other types of diabetes with which healthcare providers are less familiar. These atypical forms of diabetes make up nearly 10% of diabetes cases and can masquerade as type 1 or 2 diabetes mellitus (T1DM or T2DM), and the treatment may not be optimized if the diagnosis is not accurate. Atypical forms include monogenic diabetes (formally known as maturity-onset diabetes of the young [MODY]), latent autoimmune diabetes of the adult (LADA), ketosis-prone diabetes, and secondary diabetes. This paper will detail the defining characteristics of each atypical form and demonstrate how they can masquerade as type 1 or 2 diabetes mellitus. Gestational diabetes mellitus will not be discussed in this article.

Typ-1-Diabetes: Früherkennung und Ansätze zur Prävention

Die Inzidenz des Typ-1-Diabetes nimmt zu, besonders bei Kleinkindern. Die Erkrankung kann effektiv bereits im asymptomatischen Frühstadium der Inselautoimmunität erkannt werden. Ein Screening ist nicht nur für Risikofamilien, sondern auch in bevölkerungsweiten Studien wie Fr1da^plus in Bayern möglich und sinnvoll. Komplikationen bei der Manifestation kann durch eine frühe Diagnosestellung vorgebeugt werden. Die Teilnahme an experimentellen Interventionen zur Verzögerung der Stadienprogression ist möglich. Unterschiedliche Ansätze zur sekundären Prävention werden verfolgt. Mit dem monoklonalen Antikörper Teplizumab gelang es erstmals, bei Patienten in Stadium 2 den Zeitpunkt der Manifestation hinauszuzögern. Säuglinge mit einem hohen Risiko für die Entwicklung eines Typ-1-Diabetes können durch genetisches Screening identifiziert werden. Bei der Primärprävention wird u. a. das Ziel verfolgt, das Entstehen der Autoimmunreaktion zu verhindern. In der POInT-Studie sollen bei Risikokindern durch frühe orale Exposition zu Insulin die Immuntoleranz verbessert und das Auftreten eines Frühstadiums verzögert oder verhindert werden.

Anknüpfend an das Leitthemenheft Früherkennung und präventive Behandlung des Typ-1-Diabetes dieser Zeitschrift von 2018 werden in diesem Beitrag ausgewählte Entwicklungen als Update der letzten 2 Jahre vorgestellt.

Multicentre analysis of hyperglycaemic hyperosmolar state and diabetic ketoacidosis in type 1 and type 2 diabetes

AIMS

To compare diabetes patients with hyperglycaemic hyperosmolar state (HHS), diabetic ketoacidosis (DKA), and patients without decompensation (ND).

METHODS

In total, 500,973 patients with type 1 or type 2 diabetes of all ages registered in the diabetes patient follow-up (DPV) were included. Analysis was stratified by age (≤ / > 20 years) and by manifestation/follow-up. Patients were categorized into three groups: HHS or DKA-during follow-up according to the most recent episode-or ND.

RESULTS

At onset of diabetes, HHS criteria were met by 345 (68.4% T1D) and DKA by 9824 (97.6% T1D) patients. DKA patients had a lower BMI(-SDS) in both diabetes types compared to ND. HbA1c was higher in HHS/DKA. During follow-up, HHS occurred in 1451 (42.2% T1D) and DKA in 8389 patients (76.7% T1D). In paediatric T1D, HHS/DKA was associated with younger age, depression, and dyslipidemia. Pump usage was less frequent in DKA patients. In adult T1D/T2D subjects, metabolic control was worse in patients with HHS/DKA. HHS and DKA were also associated with excessive alcohol intake, dementia, stroke, chronic kidney disease, and depression.

CONCLUSIONS

HHS/DKA occurred mostly in T1D and younger patients. However, both also occurred in T2D, which is of great importance in the treatment of diabetes. Better education programmes are necessary to prevent decompensation and comorbidities.

Age of onset of diabetes and all-cause mortality

Diabetes mellitus continues to present a large social, financial and health system burden across the world. The relationship between age of onset of the different types of diabetes and all-cause mortality is uncertain. In this review paper, the relationship between age of onset of the different types of diabetes and all-cause mortality will be reviewed and an update of the current evidence will be presented. There is strong evidence of the relationship between age of onset of type 2 diabetes mellitus (T2DM) and all-cause mortality, good evidence of the relationship between age of onset of T1DM and all-cause mortality and no evidence of the relationship between age of onset of gestational diabetes or prediabetes and all-cause mortality. Further research is needed to look at whether aggressive management of earlier onset of T2DM can help to reduce premature mortality.

CTLA4-Ig (abatacept): a promising investigational drug for use in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is an autoimmune disease that results from the destruction of insulin-producing beta cells in the pancreas; it leads to the under or nonproduction of insulin. T1D is associated with numerous life-threatening micro- and macro-vascular complications and early deaths, hence the development of preventative strategies is a priority for research.Areas covered: The authors outline the drawbacks of available treatments for T1D and assess the three key strategies for prevention, including immunomodulatory therapies which hold the most potential. This article examines CTLA4-Ig and its efficacy and safety profiles. Finally, the pharmacokinetic parameters and pharmacodynamic markers of abatacept are shown in vivo and in clinical trials, guiding dosage regimen recommendations for future investigational studies.Expert opinion: Immunomodulation is one of the promising strategies for decelerating the progression of beta-cell destruction after the onset of T1D. It holds the advantage of specific immune modulation without systemic general immunosuppression. Preclinical and clinical studies have yielded promising data on the use of CTLA4-Ig in T1D. Variations in response to CTLA4-Ig might be partially explained by the existence of multiple T1D subtypes with varying baseline innate inflammatory/regulatory bias and the rate of C-peptide decline.