Transdermal Blood Sampling for C-Peptide Is a Minimally Invasive, Reliable Alternative to Venous Sampling in Children and Adults With Type 1 Diabetes.

OBJECTIVE: C-peptide and islet autoantibodies are key type 1 diabetes biomarkers, typically requiring venous sampling, which limits their utility. We assessed transdermal capillary blood (TCB) collection as a practical alternative. RESEARCH DESIGN AND METHODS: Ninety-one individuals (71 with type 1 diabetes, 20 control; individuals with type 1 diabetes: aged median 14.8 years [interquartile range (IQR) 9.1-17.1], diabetes duration 4.0 years [1.5-7.7]; control individuals: 42.2 years [38.0-52.1]) underwent contemporaneous venous and TCB sampling for measurement of plasma C-peptide. Participants with type 1 diabetes also provided venous serum and plasma, and TCB plasma for measurement of autoantibodies to glutamate decarboxylase, islet antigen-2, and zinc transporter 8. The ability of TCB plasma to detect significant endogenous insulin secretion (venous C-peptide ≥200 pmol/L) was compared along with agreement in levels, using Bland-Altman. Venous serum was compared with venous and TCB plasma for detection of autoantibodies, using established thresholds. Acceptability was assessed by age-appropriate questionnaire. RESULTS: Transdermal sampling took a mean of 2.35 min (SD 1.49). Median sample volume was 50 µL (IQR 40-50) with 3 of 91 (3.3%) failures, and 13 of 88 (14.7%) <35 µL. TCB C-peptide showed good agreement with venous plasma (mean venous ln[C-peptide] - TCB ln[C-peptide] = 0.008, 95% CI [-0.23, 0.29], with 100% [36 of 36] sensitivity/100% [50 of 50] specificity to detect venous C-peptide ≥200 pmol/L). Where venous serum in multiple autoantibody positive TCB plasma agreed in 22 of 32 (sensitivity 69%), comparative specificity was 35 of 36 (97%). TCB was preferred to venous sampling (type 1 diabetes: 63% vs. 7%; 30% undecided). CONCLUSIONS: Transdermal capillary testing for C-peptide is a sensitive, specific, and acceptable alternative to venous sampling; TCB sampling for islet autoantibodies needs further assessment.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion.

BACKGROUND: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Some diabetes types, called monogenic diabetes, are caused by changes in a single gene. It is important to know who has this kind of diabetes because treatment can differ from that of other types of diabetes. Some treatments also work better than others for specific types, and some people can for example change from insulin injections to tablets. In addition, relatives can be offered a test to see if they are at risk. Genetic testing is needed to diagnose monogenic diabetes but is expensive, so it's not possible to test every person with diabetes for it. We evaluated published research on who should be tested and what test to use. Based on this, we provide recommendations for doctors and health care providers on how to implement genetic testing for monogenic diabetes.

First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes.

BACKGROUND: Type 1 diabetes (T1D) is a CD4+ T cell-driven autoimmune disease characterized by the destruction of insulin-producing pancreatic ß-cells by CD8+ T cells. Achieving glycemic targets in T1D remains challenging in clinical practice; new treatments aim to halt autoimmunity and prolong ß-cell survival. IMCY-0098 is a peptide derived from human proinsulin that contains a thiol-disulfide oxidoreductase motif at the N-terminus and was developed to halt disease progression by promoting the specific elimination of pathogenic T cells. METHODS: This first-in-human, 24-week, double-blind phase 1b study evaluated the safety of three dosages of IMCY-0098 in adults diagnosed with T1D < 6 months before study start. Forty-one participants were randomized to receive four bi-weekly injections of placebo or increasing doses of IMCY-0098 (dose groups A/B/C received 50/150/450 µg for priming followed by three further administrations of 25/75/225 µg, respectively). Multiple T1D-related clinical parameters were also assessed to monitor disease progression and inform future development. Long-term follow-up to 48 weeks was also conducted in a subset of patients. RESULTS: Treatment with IMCY-0098 was well tolerated with no systemic reactions; a total of 315 adverse events (AEs) were reported in 40 patients (97.6%) and were related to study treatment in 29 patients (68.3%). AEs were generally mild; no AE led to discontinuation of the study or death. No significant decline in C-peptide was noted from baseline to Week 24 for dose A, B, C, or placebo (mean change - 0.108, - 0.041, - 0.040, and - 0.012, respectively), suggesting no disease progression. CONCLUSIONS: Promising safety profile and preliminary clinical response data support the design of a phase 2 study of IMCY-0098 in patients with recent-onset T1D. TRIAL REGISTRATION: IMCY-T1D-001: ClinicalTrials.gov NCT03272269; EudraCT: 2016-003514-27; and IMCY-T1D-002: ClinicalTrials.gov NCT04190693; EudraCT: 2018-003728-35.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes.

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Fucosylated AGP glycopeptides as biomarkers of HNF1A-Maturity onset diabetes of the young.

AIMS: We previously demonstrated that antennary fucosylated N-glycans on plasma proteins are regulated by HNF1A and can identify cases of Maturity-Onset Diabetes of the Young caused by HNF1A variants (HNF1A-MODY). Based on literature data, we further postulated that N-glycans with best diagnostic value mostly originate from alpha-1-acid glycoprotein (AGP). In this study we analyzed fucosylation of AGP in subjects with HNF1A-MODY and other types of diabetes aiming to evaluate its diagnostic potential. METHODS: A recently developed LC-MS method for AGP N-glycopeptide analysis was utilized in two independent cohorts: a) 466 subjects with different diabetes subtypes to test the fucosylation differences, b) 98 selected individuals to test the discriminative potential for pathogenic HNF1A variants. RESULTS: Our results showed significant reduction in AGP fucosylation associated to HNF1A-MODY when compared to other diabetes subtypes. Additionally, ROC curve analysis confirmed significant discriminatory potential of individual fucosylated AGP glycopeptides, where the best performing glycopeptide had an AUC of 0.94 (95% CI 0.90-0.99). CONCLUSIONS: A glycopeptide based diagnostic tool would be beneficial for patient stratification by providing information about the functionality of HNF1A. It could assist the interpretation of DNA sequencing results and be a useful addition to the differential diagnostic process.

Development of an exoglycosidase plate-based assay for detecting α1-3,4 fucosylation biomarker in individuals with HNF1A-MODY.

Maturity-onset diabetes of the young due to hepatocyte nuclear factor-1 alpha variants (HNF1A-MODY) causes monogenic diabetes. Individuals carrying damaging variants in HNF1A show decreased levels of α1-3,4 fucosylation, as demonstrated on antennary fucosylation of blood plasma N-glycans. The excellent diagnostic performance of this glycan biomarker in blood plasma N-glycans of individuals with HNF1A-MODY has been demonstrated using liquid chromatography methods. Here, we have developed a high-throughput exoglycosidase plate-based assay to measure α1-3,4 fucosylation levels in blood plasma samples. The assay has been optimized and its validity tested using 1000 clinical samples from a cohort of individuals with young-adult onset diabetes including cases with HNF1A-MODY. The α1-3,4 fucosylation levels in blood plasma showed a good differentiating power in identifying cases with damaging HNF1A variants, as demonstrated by receiver operating characteristic curve analysis with the AUC values of 0.87 and 0.95. This study supports future development of a simple diagnostic test to measure this glycan biomarker for application in a clinical setting.

Interlaboratory evaluation of plasma N-glycan antennary fucosylation as a clinical biomarker for HNF1A-MODY using liquid chromatography methods.

Antennary fucosylation alterations in plasma glycoproteins have been previously proposed and tested as a biomarker for differentiation of maturity onset diabetes of the young (MODY) patients carrying a functional mutation in the HNF1A gene. Here, we developed a novel LC-based workflow to analyze blood plasma N-glycan fucosylation in 320 diabetes cases with clinical features matching those at risk of HNF1A-MODY. Fucosylation levels measured in two independent research centers by using similar LC-based methods were correlated to evaluate the interlaboratory performance of the biomarker. The interlaboratory study showed good correlation between fucosylation levels measured for the 320 cases in the two centers with the correlation coefficient (r) of up to 0.88 for a single trait A3FG3S2. The improved chromatographic separation allowed the identification of six single glycan traits and a derived antennary fucosylation trait that were able to differentiate individuals carrying pathogenic mutations from benign or no HNF1A mutation cases, as determined by the area under the curve (AUC) of up to 0.94. The excellent (r = 0.88) interlaboratory performance of the glycan biomarker for HNF1A-MODY further supports the development of a clinically relevant diagnostic test measuring antennary fucosylation levels.

Integrated digital system for community engagement and community-based surveillance during the 2014-2016 Ebola outbreak in Sierra Leone: lessons for future health emergencies.

Community engagement and community-based surveillance are essential components of responding to infectious disease outbreaks, but real-time data reporting remains a challenge. In the 2014-2016 Ebola outbreak in Sierra Leone, the Social Mobilisation Action Consortium was formed to scale-up structured, data-driven community engagement. The consortium became operational across all 14 districts and supported an expansive network of 2500 community mobilisers, 6000 faith leaders and 42 partner radio stations. The benefit of a more agile digital reporting system became apparent within few months of implementing paper-based reporting given the need to rapidly use the data to inform the fast-evolving epidemic. In this paper, we aim to document the design, deployment and implementation of a digital reporting system used in six high transmission districts. We highlight lessons learnt from our experience in scaling up the digital reporting system during an unprecedented public health crisis. The lessons learnt from our experience in Sierra Leone have important implications for designing and implementing similar digital reporting systems for community engagement and community-based surveillance during public health emergencies.

The correlation between breath acetone and blood betahydroxybutyrate in individuals with type 1 diabetes.

Ketone testing is an important element of the self-management of illness in type 1 diabetes. The aim of the present study was to see if a breath test for acetone could be used to predict quantitatively the levels of the ketone betahydroxybutyrate in the blood of those with type 1 diabetes, and thus be used as an alternative to capillary testing for ketones. Simultaneous capillary ketones and breath acetone were measured in 72 individuals with type 1 diabetes attending a diabetes clinic and on 9 individuals admitted to hospital with diabetic ketoacidosis. Capillary blood measurements ranged from 0.1 mmol l-1 (the lower limit of the ketone monitor) to over 7 mmol l-1, with breath acetone varying between 0.25 and 474 parts per million by volume. The two variables were found to be correlated and allowed modelling to be carried out which separated breath acetone levels into three categories corresponding to normal, elevated and 'at risk' levels of blood ketones. The results on this limited set of participants suggest that a breath acetone test could be a simple, non-invasive substitute for capillary ketone measurement in type 1 diabetes.

Community engagement in outbreak response: lessons from the 2014-2016 Ebola outbreak in Sierra Leone.

Documentation of structured community engagement initiatives and real-time monitoring of community engagement activities during large-scale epidemics is limited. To inform such initiatives, this paper analyses the Community Led Ebola Action (CLEA) approach implemented through the Social Mobilization Action Consortium (SMAC) during the 2014-2016 Ebola epidemic in Sierra Leone. The SMAC initiative consisted of a network of 2466 community mobilisers, >6000 religious leaders and 42 local radio stations across all 14 districts of Sierra Leone. Community mobilisers were active in nearly 70% of all communities across the country using the CLEA approach to facilitate community analysis, trigger collective action planning and maintain community action plans over time. CLEA was complemented by interactive radio programming and intensified religious leader engagement.Community mobilisers trained in the CLEA approach used participatory methods, comprised of an initial community 'triggering' event, action plan development and weekly follow-ups to monitor progress on identified action items. Mobilisers collected operational and behavioural data on a weekly basis as part of CLEA. We conducted a retrospective analysis of >50 000 weekly reports from approximately 12 000 communities from December 2014 to September 2015. The data showed that 100% of the communities that were engaged had one or more action plans in place. Out of the 63 110 cumulative action points monitored by community mobilisers, 92% were marked as 'in-progress' (85%) or 'achieved' (7%) within 9 months. A qualitative examination of action points revealed that the in-progress status was indicative of the long-term sustainability of most action points (eg, continuous monitoring of visitors into the community) versus one-off action items that were marked as achieved (eg, initial installation of handwashing station). Analysis of behavioural outcomes of the intervention indicate an increase over time in the fraction of reported safe burials and fraction of reported cases referred for medical care within 24 hours of symptom onset in the communities that were engaged.Through CLEA, we have demonstrated how large-scale, coordinated community engagement interventions can be achieved and monitored in real-time during future Ebola epidemics and other similar epidemics. The SMAC initiative provides a practical model for the design, implementation and monitoring of community engagement, integration and coordination of community engagement interventions with other health emergency response pillars, and adaptive strategies for large-scale community-based operational data collection.

Lixisenatide in type 1 diabetes: A randomised control trial of the effect of lixisenatide on post-meal glucose excursions and glucagon in type 1 diabetes patients.

AIMS: The GLP1 agonist lixisenatide is glucagonostatic and reduces post-prandial blood glucose (PPBG) in type 2 diabetes. This study investigates its impact in type 1 diabetes (T1D). METHODS: In a blinded, crossover trial, 25 patients with T1D were randomised to 4 weeks adjunctive treatment with lixisenatide (L) or placebo (P), with a 4-week washout period. The primary outcome was percentage of 3 hours PPBG in target (4-10 mmol/L) assessed by CGM before and after treatment. Participants also underwent post-treatment standardised mixed meal test (MMT, n = 25) and hyperinsulinaemic hypoglycaemic clamp (n = 15). RESULTS: PPBG CGM readings in target were similar between L vs P (Mean % ± SE, breakfast 45.4 ± 6.0 vs 44.3 ± 6.0, P = .48, lunch 45.5 ± 5.8 vs 50.6 ± 5.3, P = .27 and dinner 43.0 ± 6.7 vs 47.7 ± 5.6, P = .30). HbA1C was similar between L vs P (64.7 ± 1.6 vs 64.1 ± 1.6 mmol/mol, P = .30). Prandial insulin fell after lixisenatide (dose change -0.7 ± 0.6 vs +2.4 ± 0.7 units/d, P = .004), but basal insulin dose was similar between groups. The post-MMT glucose area under the curve (AUC) was lower with L than P (392.0 ± 167.7 vs 628.1 ± 132.5 mmol/L × min, P < .001), as was the corresponding glucagon AUC (140.0 ± 110.0 vs 304.2 ± 148.2 nmol/L × min, P < .001). Glucagon and counter-regulatory hormone values at a blood glucose of 2.4 mmol/L during the hypoglycaemic clamp were similar between L and P. CONCLUSION: In T1D, PPBG values were not altered by adjunctive lixisenatide although prandial insulin dose fell. Glucose and glucagon level during an MMT were significantly lower after lixisenatide, without affecting counter-regulatory response during hypoglycaemia.

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults.

BACKGROUND: There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models. METHODS: We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes). RESULTS: Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities. CONCLUSION: Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.

Altered cortisol metabolism in individuals with HNF1A-MODY.

OBJECTIVE AND CONTEXT: Maturity onset diabetes of the young due to variants in HNF1A (HNF1A-MODY) is the most common form of monogenic diabetes. Individuals with HNF1A-MODY usually have a lean phenotype which contrasts with type 2 diabetes (T2DM). Data from hepatocytes derived from Hnf1a knock-out mice demonstrated dysregulation of 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1), which regulates glucocorticoid availability and action in target tissues, together with 11ß-HSD2 and steroid A-ring reductases, 5α- and 5ß-reductase. We proposed that altered glucocorticoid metabolism might underpin some of the phenotypic differences between patients with HNF1A-MODY and those with T2DM. DESIGN: A retrospective matched cohort study. PATIENTS AND MEASUREMENTS: 24-hours urine steroid metabolome profiling was carried out by gas chromatography-mass spectrometry in 35 subjects with HNF1A-MODY, 35 individuals with T2DM and 35 healthy controls matched for age, sex and BMI. The steroid metabolites were expressed as µg/L in all groups and measured in mid-morning urine in diabetic subjects and 24-hour urine collection in healthy controls. Hence, only ratios were compared not the individual steroids. Established ratios of glucocorticoid metabolites were used to estimate 11ß-HSD1/2 and 5α- and 5ß-reductase activities. RESULTS: While 11ß-HSD1 activity was similar in all groups, 11ß-HSD2 activity was significantly lower in subjects with HNF1A-MODY and T2DM than in healthy controls. The ratio of 5ß- to 5α-metabolites of cortisol was higher in subjects with HNF1A-MODY than in T2DM and healthy controls, probably due to increased activity of the 5ß-reductase (AKR1D1) in HNF1A-MODY. CONCLUSIONS: This is the first report of steroid metabolites in HNF1A-MODY. We have identified distinct differences in steroid metabolism pathways in subjects with HNF1A-MODY that have the potential to alter steroid hormone availability. Further studies are required to explore whether these changes link to phenotype.

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years.

OBJECTIVE: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18-50. DESIGN: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, body mass index) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. SETTING: UK cohorts recruited from primary and secondary care. PARTICIPANTS: 1352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. MAIN OUTCOME MEASURES: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200 pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600 pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. RESULTS: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 (95% CI 0.88 to 0.93) (clinical features only) to 0.97 (95% CI 0.96 to 0.98) (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 (0.90 to 0.96)). CONCLUSIONS: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.

Plasma Fucosylated Glycans and C-Reactive Protein as Biomarkers of HNF1A-MODY in Young Adult-Onset Nonautoimmune Diabetes.

OBJECTIVE: Maturity-onset diabetes of the young (MODY) due to variants in HNF1A is the most common type of monogenic diabetes. Frequent misdiagnosis results in missed opportunity to use sulfonylureas as first-line treatment. A nongenetic biomarker could improve selection of subjects for genetic testing and increase diagnosis rates. We previously reported that plasma levels of antennary fucosylated N-glycans and high-sensitivity C-reactive protein (hs-CRP) are reduced in individuals with HNF1A-MODY. In this study, we examined the potential use of N-glycans and hs-CRP in discriminating individuals with damaging HNF1A alleles from those without HNF1A variants in an unselected population of young adults with nonautoimmune diabetes. RESEARCH DESIGN AND METHODS: We analyzed the plasma N-glycan profile, measured hs-CRP, and sequenced HNF1A in 989 individuals with diabetes diagnosed when younger than age 45, persistent endogenous insulin production, and absence of pancreatic autoimmunity. Systematic assessment of rare HNF1A variants was performed. RESULTS: We identified 29 individuals harboring 25 rare HNF1A alleles, of which 3 were novel, and 12 (in 16 probands) were considered pathogenic. Antennary fucosylated N-glycans and hs-CRP were able to differentiate subjects with damaging HNF1A alleles from those without rare HNF1A alleles. Glycan GP30 had a receiver operating characteristic curve area under the curve (AUC) of 0.90 (88% sensitivity, 80% specificity, cutoff 0.70%), whereas hs-CRP had an AUC of 0.83 (88% sensitivity, 69% specificity, cutoff 0.81 mg/L). CONCLUSIONS: Half of rare HNF1A sequence variants do not cause MODY. N-glycan profile and hs-CRP could both be used as tools, alone or as adjuncts to existing pathways, for identifying individuals at high risk of carrying a damaging HNF1A allele.

Genetics of Monogenic Diabetes: Present Clinical Challenges.

PURPOSE OF REVIEW: Monogenic forms of diabetes have specific treatments that differ from the standard care provided for type 1 and type 2 diabetes, making the appropriate diagnosis essential. In this review, we discuss current clinical challenges that remain, including improving case-finding strategies, particularly those that have transethnic applicability, and understanding the interpretation of genetic variants as pathogenic, with clinically meaningful impacts. RECENT FINDINGS: Biomarker approaches to the stratification for genetic testing now appear to be most effective in identifying cases of monogenic diabetes, and use of genetic risk scores may also prove useful. However, applicability in all ethnic groups is lacking. Challenges remain in the classification of genes as diabetes-causing and the interpretation of genetic variants at the clinical interface. Since the discovery that genetic defects can cause neonatal or young-onset diabetes, multiple causal genes have been identified and there have been many advances in strategies to detect genetic forms of diabetes and their treatments. Approaches learnt from monogenic diabetes are now being translated to polygenic diabetes.

Traditional clinical criteria outperform high-sensitivity C-reactive protein for the screening of hepatic nuclear factor 1 alpha maturity-onset diabetes of the young among young Asians with diabetes.

BACKGROUND: Young adults with diabetes in Asia represent a heterogeneous group. Using traditional clinical criteria to preselect individuals for testing for maturity-onset diabetes of the young (MODY) may exclude a large proportion from testing. High-sensitivity C-reactive protein (hs-CRP) has shown promise as a biomarker to differentiate hepatic nuclear factor 1 alpha (HNF1A)-MODY from type 2 diabetes. We aimed to compare the use of hs-CRP as a biomarker versus traditional criteria, to guide testing for HNF1A-MODY among a cohort of young adults with diabetes in Singapore. METHODS: A total of 252 adults (age of onset ⩽45 years) and 20 children with diabetes were recruited. Using traditional criteria (family history of diabetes and onset of diabetes ⩽25 years) and an hs-CRP cut off of ⩽0.5 mg/l, 125 and 37 adults, respectively, were identified for HNF1A gene testing. All children underwent HNF1A gene testing. RESULTS: Five adults (5/143, 3.5%) with HNF1A-MODY were identified. There were no HNF1A gene mutations among the children. Traditional criteria correctly identified all five HNF1A-MODY individuals (5/125, 4%), while applying an hs-CRP level of ⩽0.5 mg/l selected just 1 of these 5 for HNF1A gene testing (1/37, 2.7%). None of those with a positive GAD antibody or undetectable C-peptide level had HNF1A-MODY. CONCLUSION: The use of hs-CRP to guide screening for HNF1A-MODY among Asian young adults with diabetes did not improve the diagnostic yield. Applying a combination of age of onset of diabetes under 25 years and a family history of diabetes alone could guide targeted HNF1A-MODY screening in Asians, with an expected yield of 4% diagnosed with HNF1A-MODY among those screened.

Monogenic diabetes in adults: what are the new developments?

Maturity-onset diabetes of the young (MODY) is a heterogeneous group of monogenic causes of beta-cell dysfunction and diabetes arising in children and young adults. Making an accurate diagnosis of MODY is important for establishing the correct management. Recent advances in our understanding of human sequence variation, through data collated in resources such as the Exome Aggregation Consortium have refined guidelines for assessment of rare genetic variants. This will allow a more precise aetiological diagnosis in childhood and young adult diabetes. No major new monogenic causes of diabetes outside the neonatal period have been identified in recent years, but the allelic spectrum of disease phenotype associated with known genes continues to expand. Improving uptake of genetic testing by defining who should be tested is an area of active research. A population based study found that 6.5% of children who have negative beta-cell antibodies at diagnosis have rare functional variants in MODY genes. Defining the high risk groups in adults with diabetes is more difficult, but online decision aids will assist clinicians in selecting who to refer for testing.

Maturity onset diabetes of the young due to HNF1A variants in Croatia.

INTRODUCTION: Maturity onset diabetes of the young due to HNF1A mutations (HNF1A-MODY) is the most frequent form of monogenic diabetes in adults. It is often misdiagnosed as type 1 or type 2 diabetes, but establishing genetic diagnosis is important, as treatment differs from the common types of diabetes. HNF1A-MODY has not been investigated in Croatia before due to limited access to genetic testing. In this study we aimed to describe the characteristics of young adults diagnosed with diabetes before the age of 45 years, who have rare HNF1A allele variants, and estimate the prevalence of HNF1A-MODY in Croatia. MATERIALS AND METHODS: We recruited 477 C-peptide positive and beta cell antibody negative subjects through the Croatian Diabetes Registry. HNF1A was sequenced for all participants and systematic assessment of the variants found was performed. The prevalence of HNF1A-MODY was calculated in the study group and results extrapolated to estimate the proportion of diabetic individuals with HNF1A-MODY in Croatia and the population prevalence. RESULTS: Our study identified 13 individuals harbouring rare HNF1A allelic variants. After systematic assessment, 8 were assigned a diagnosis of HNF1A-MODY. Two individuals were able to discontinue insulin treatment following the diagnosis. We estimated that HNF1A-MODY in Croatia has a prevalence of 66 (95% CI 61 - 72) cases per million. CONCLUSIONS: The estimated prevalence of HNF1A-MODY in Croatia is similar to that reported in other European countries. Finding cases lead to important treatment changes for patients. This strongly supports the introduction of diagnostic genetic testing for monogenic diabetes in Croatia.