Treatment switch from multiple daily insulin injections to sulphonylureas in an African young adult diagnosed with HNF1A MODY: a case report.

BACKGROUND: Maturity onset diabetes of the young is one of the commonest causes of monogenic diabetes and can easily be mistaken for type 1 diabetes. A diagnosis of maturity onset diabetes of the young can have direct implications for genetic counseling, family screening, and precision diabetes treatment. However, the cost of genetic testing and identifying individuals to test are the main challenges for diagnosis and management in sub-Saharan Africa. We report the very first documented case of HNF1A maturity onset diabetes of the young in the sub-Saharan African region. CASE PRESENTATION: A 20-year-old female Black African young adult diagnosed with type 1 diabetes aged 14 presented for routine out-patient diabetes consultation. She was on multiple daily insulin injections; total combined dose 0.79 IU/kg/day with an HbA1c of 7.7%. The rest of her laboratory examinations were normal. On extended laboratory analysis, she had good residual insulin secretion with post-meal plasma C-peptide levels at 1150 pmol/L. She tested negative for glutamic acid decarboxylase (GAD65), islet antigen-2 (IA-2), and zinc transporter 8 (ZnT8) islet autoantibodies. Targeted next-generation sequencing (t-NGS) for monogenic diabetes was performed using DNA extracted from a buccal sample. She was diagnosed with HNF1A maturity onset diabetes of the young, with the c.607C > T; p.(Arg203Cys) pathogenic variant, which has never been reported in sub-Saharan Africa. Her clinical practitioners provided genetic and therapeutic counseling. Within 10 months following the diagnosis of maturity onset diabetes of the young, she was successfully switched from multiple daily insulin injections to oral antidiabetic tablets (sulphonylurea) while maintaining stable glycemic control (HBA1c of 7.0%) and reducing hypoglycemia. She expressed a huge relief from the daily finger pricks for blood glucose monitoring. CONCLUSION: This case reveals that HNF1A maturity onset diabetes of the young (and probably other causes of monogenic diabetes) can present in sub-Saharan Africa. A diagnosis of maturity onset diabetes of the young can have significant life-changing therapeutic implications.

Molecular mechanism of HNF-1A-mediated HNF4A gene regulation and promoter-driven HNF4A-MODY diabetes.

Monogenic diabetes is a gateway to precision medicine through molecular mechanistic insight. Hepatocyte nuclear factor 1A (HNF-1A) and HNF-4A are transcription factors that engage in crossregulatory gene transcription networks to maintain glucose-stimulated insulin secretion in pancreatic ß cells. Variants in the HNF1A and HNF4A genes are associated with maturity-onset diabetes of the young (MODY). Here, we explored 4 variants in the P2-HNF4A promoter region: 3 in the HNF-1A binding site and 1 close to the site, which were identified in 63 individuals from 21 families of different MODY disease registries across Europe. Our goal was to study the disease causality for these variants and to investigate diabetes mechanisms on the molecular level. We solved a crystal structure of HNF-1A bound to the P2-HNF4A promoter and established a set of techniques to probe HNF-1A binding and transcriptional activity toward different promoter variants. We used isothermal titration calorimetry, biolayer interferometry, x-ray crystallography, and transactivation assays, which revealed changes in HNF-1A binding or transcriptional activities for all 4 P2-HNF4A variants. Our results suggest distinct disease mechanisms of the promoter variants, which can be correlated with clinical phenotype, such as age of diagnosis of diabetes, and be important tools for clinical utility in precision medicine.

Development of a clinical calculator to aid the identification of MODY in pediatric patients at the time of diabetes diagnosis.

Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when the existing "MODY calculator" cannot be used. Firth logistic regression models were developed on data from 3541 paediatric patients from the Swedish 'Better Diabetes Diagnosis' (BDD) population study (n = 46 (1.3%) MODY (HNF1A, HNF4A, GCK)). Model performance was compared to using islet autoantibody testing. HbA1c, parent with diabetes, and absence of polyuria were significant independent predictors of MODY. The model showed excellent discrimination (c-statistic = 0.963) and calibrated well (Brier score = 0.01). MODY probability > 1.3% (ie. above background prevalence) had similar performance to being negative for all 3 antibodies (positive predictive value (PPV) = 10% v 11% respectively i.e. ~ 1 in 10 positive test rate). Probability > 1.3% and negative for 3 islet autoantibodies narrowed down to 4% of the cohort, and detected 96% of MODY cases (PPV = 31%). This MODY calculator for paediatric patients at time of diabetes diagnosis will help target genetic testing to those most likely to benefit, to get the right diagnosis.

Identification of monogenic diabetes in an Australian cohort using the Exeter maturity-onset diabetes of the young (MODY) probability calculator and next-generation sequencing gene panel testing.

AIMS: This study aims to describe the prevalence of monogenic diabetes in an Australian referral cohort, in relation to Exeter maturity-onset diabetes of the young (MODY) probability calculator (EMPC) scores and next-generation sequencing with updated testing where relevant. METHODS: State-wide 5-year retrospective cohort study of individuals referred for monogenic diabetes genetic testing. RESULTS: After excluding individuals who had cascade testing for a familial variant (21) or declined research involvement (1), the final cohort comprised 40 probands. Incorporating updated testing, the final genetic result was positive (likely pathogenic/pathogenic variant) in 11/40 (27.5%), uncertain (variant of uncertain significance) in 8/40 (20%) and negative in 21/40 (52.5%) participants. Causative variants were found in GCK, HNF1A, MT-TL1 and HNF4A. Variants of uncertain significance included a novel multi-exonic GCK duplication. Amongst participants with EMPC scores ≥ 25%, a causative variant was identified in 37%. Cascade testing was positive in 9/10 tested relatives with diabetes and 0/6 tested relatives with no history of diabetes. CONCLUSIONS: Contemporary genetic testing produces a high yield of positive results in individuals with clinically suspected monogenic diabetes and their relatives with diabetes, highlighting the value of genetic testing for this condition. An EMPC score cutoff of ≥ 25% correctly yielded a positive predictive value of ≥ 25% in this multiethnic demographic. This is the first Australian study to describe EMPC scores in the Australian clinic setting, albeit a biased referral cohort. Larger studies may help characterise EMPC performance between ethnic subsets, noting differences in the expected probability of monogenic diabetes relative to type 2 diabetes.

Penetrance and expressivity of mitochondrial variants in a large clinically unselected population.

Whole genome sequencing (WGS) from large clinically unselected cohorts provides a unique opportunity to assess the penetrance and expressivity of rare and/or known pathogenic mitochondrial variants in population. Using WGS from 179 862 clinically unselected individuals from the UK Biobank, we performed extensive single and rare variant aggregation association analyses of 15 881 mtDNA variants and 73 known pathogenic variants with 15 mitochondrial disease-relevant phenotypes. We identified 12 homoplasmic and one heteroplasmic variant (m.3243A>G) with genome-wide significant associations in our clinically unselected cohort. Heteroplasmic m.3243A>G (MAF = 0.0002, a known pathogenic variant) was associated with diabetes, deafness and heart failure and 12 homoplasmic variants increased aspartate aminotransferase levels including three low-frequency variants (MAF ~0.002 and beta~0.3 SD). Most pathogenic mitochondrial disease variants (n = 66/74) were rare in the population (<1:9000). Aggregated or single variant analysis of pathogenic variants showed low penetrance in unselected settings for the relevant phenotypes, except m.3243A>G. Multi-system disease risk and penetrance of diabetes, deafness and heart failure greatly increased with m.3243A>G level ≥ 10%. The odds ratio of these traits increased from 5.61, 12.3 and 10.1 to 25.1, 55.0 and 39.5, respectively. Diabetes risk with m.3243A>G was further influenced by type 2 diabetes genetic risk. Our study of mitochondrial variation in a large-unselected population identified novel associations and demonstrated that pathogenic mitochondrial variants have lower penetrance in clinically unselected settings. m.3243A>G was an exception at higher heteroplasmy showing a significant impact on health making it a good candidate for incidental reporting.

Phenotypic effect of GBA1 variants in individuals with and without Parkinson's disease: The RAPSODI study.

BACKGROUND: Variants in the GBA1 gene cause the lysosomal storage disorder Gaucher disease (GD). They are also risk factors for Parkinson's disease (PD), and modify the expression of the PD phenotype. The penetrance of GBA1 variants in PD is incomplete, and the ability to determine who among GBA1 variant carriers are at higher risk of developing PD, would represent an advantage for prognostic and trial design purposes. OBJECTIVES: To compare the motor and non-motor phenotype of GBA1 carriers and non-carriers. METHODS: We present the cross-sectional results of the baseline assessment from the RAPSODI study, an online assessment tool for PD patients and GBA1 variant carriers. The assessment includes clinically validated questionnaires, a tap-test, the University of Pennsyllvania Smell Identification Test and cognitive tests. Additional, homogeneous data from the PREDICT-PD cohort were included. RESULTS: A total of 379 participants completed all parts of the RAPSODI assessment (89 GBA1-negative controls, 169 GBA1-negative PD, 47 GBA1-positive PD, 47 non-affected GBA1 carriers, 27 GD). Eighty-six participants were recruited through PREDICT-PD (43 non-affected GBA1 carriers and 43 GBA1-negative controls). GBA1-positive PD patients showed worse performance in visual cognitive tasks and olfaction compared to GBA1-negative PD patients. No differences were detected between non-affected GBA1 carriers carriers and GBA1-negative controls. No phenotypic differences were observed between any of the non-PD groups. CONCLUSIONS: Our results support previous evidence that GBA1-positive PD has a specific phenotype with more severe non-motor symptoms. However, we did not reproduce previous findings of more frequent prodromal PD signs in non-affected GBA1 carriers.

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.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

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.

Monogenic diabetes in New Zealand - An audit based revision of the monogenic diabetes genetic testing pathway in New Zealand.

Aims: To evaluate (a) the diagnostic yield of genetic testing for monogenic diabetes when using single gene and gene panel-based testing approaches in the New Zealand (NZ) population, (b) whether the MODY (Maturity Onset Diabetes of the Young) pre-test probability calculator can be used to guide referrals for testing in NZ, (c) the number of referrals for testing for Maori/Pacific ethnicities compared to NZ European, and (d) the volume of proband vs cascade tests being requested. Methods: A retrospective audit of 495 referrals, from NZ, for testing of monogenic diabetes genes was performed. Referrals sent to LabPlus (Auckland) laboratory for single gene testing or small multi-gene panel testing, or to the Exeter Genomics Laboratory, UK, for a large gene panel, received from January 2014 - December 2021 were included. Detection rates of single gene, small multi-gene and large gene panels (neonatal and non-neonatal), and cascade testing were analysed. Pre-test probability was calculated using the Exeter MODY probability calculator and ethnicity data was also collected. Results: The diagnostic detection rate varied across genes, from 32% in GCK, to 2% in HNF4A, with single gene or small gene panel testing averaging a 12% detection rate. Detection rate by type of panel was 9% for small gene panel, 23% for non-neonatal monogenic diabetes large gene panel and 40% for neonatal monogenic diabetes large gene panel. 45% (67/147) of patients aged 1-35 years at diabetes diagnosis scored <20% on MODY pre-test probability, of whom 3 had class 4/5 variants in HNF1A, HNF4A or HNF1B. Ethnicity data of those selected for genetic testing correlated with population diabetes prevalence for Maori (15% vs 16%), but Pacific People appeared under-represented (8% vs 14%). Only 1 in 6 probands generated a cascade test. Conclusions: A new monogenic diabetes testing algorithm for NZ is proposed, which directs clinicians to choose a large gene panel in patients without syndromic features who score a pre-test MODY probability of above 20%.

Expanding the Phenotype of TRMT10A Mutations: Case Report and a Review of the Existing Cases

The tRNA methyltransferase 10 homologue A (TRMT10A) gene encodes tRNA methyl transferase, and biallelic loss of function mutations cause a recessive syndrome of intellectual disability, microcephaly, short stature and diabetes. A case with intellectual disability and distinctive features including microcephaly was admitted. She was diagnosed with epilepsy at 2.5 years old. At 3.6 years of age, severe short stature related to growth hormone (GH) deficiency was detected. She had an incidental diagnosis of diabetes at age 11.4 years which was negative for diabetes antibodies with persistent C-peptide level and she was treated with metformin. Spontaneous puberty did not begin until 15.7 years of age and she was found to have primary ovarian failure. A homozygous p.Arg127* mutation in TRMT10A was detected. In addition to the typical clinical features which characterize TRMT10A syndrome, we observed an unusual form of impaired glucose metabolism which presented in early childhood with hypoglycemia followed by diabetes in late childhood. GH deficiency and primary ovarian failure may also be additional findings of this syndrome. Patients with slow onset diabetes who are negative for auto-antibodies and have extra-pancreatic features should be tested for all known subtypes of monogenic diabetes.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes.

AIMS/HYPOTHESIS: The reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults. METHODS: We analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants. RESULTS: The T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4). CONCLUSIONS/INTERPRETATION: The inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.

Reduced penetrance of MODY-associated HNF1A/HNF4A variants but not GCK variants in clinically unselected cohorts.

The true prevalence and penetrance of monogenic disease variants are often not known because of clinical-referral ascertainment bias. We comprehensively assess the penetrance and prevalence of pathogenic variants in HNF1A, HNF4A, and GCK that account for >80% of monogenic diabetes. We analyzed clinical and genetic data from 1,742 clinically referred probands, 2,194 family members, clinically unselected individuals from a US health system-based cohort (n = 132,194), and a UK population-based cohort (n = 198,748). We show that one in 1,500 individuals harbor a pathogenic variant in one of these genes. The penetrance of diabetes for HNF1A and HNF4A pathogenic variants was substantially lower in the clinically unselected individuals compared to clinically referred probands and was dependent on the setting (32% in the population, 49% in the health system cohort, 86% in a family member, and 98% in probands for HNF1A). The relative risk of diabetes was similar across the clinically unselected cohorts highlighting the role of environment/other genetic factors. Surprisingly, the penetrance of pathogenic GCK variants was similar across all cohorts (89%-97%). We highlight that pathogenic variants in HNF1A, HNF4A, and GCK are not ultra-rare in the population. For HNF1A and HNF4A, we need to tailor genetic interpretation and counseling based on the setting in which a pathogenic monogenic variant was identified. GCK is an exception with near-complete penetrance in all settings. This along with the clinical implication of diagnosis makes it an excellent candidate for the American College of Medical Genetics secondary gene list.

How do I diagnose Maturity Onset Diabetes of the Young in my patients?

Maturity Onset Diabetes of the Young (MODY) is a monogenic form of diabetes diagnosed in young individuals that lack the typical features of type 1 and type 2 diabetes. The genetic subtype of MODY determines the most effective treatment and this is the driver for MODY genetic testing in diabetes populations. Despite the obvious clinical and health economic benefits, MODY is significantly underdiagnosed with the majority of patients being inappropriately managed as having type 1 or type 2 diabetes. Low detection rates result from the difficulty in identifying patients with a likely diagnosis of MODY from the high background population of young onset type 1 and type 2 diabetes, compounded by the lack of MODY awareness and education in diabetes care physicians. MODY diagnosis can be improved through (1) access to education and training, (2) the use of sensitive and specific selection criteria based on accurate prediction models and biomarkers to identify patients for testing, (3) the development and mainstream implementation of simple criteria-based selection pathways applicable across a range of healthcare settings and ethnicities to select the most appropriate patients for genetic testing and (4) the correct use of next generation sequencing technology to provide accurate and comprehensive testing of all known MODY and monogenic diabetes genes. The creation and public sharing of educational materials, clinical and scientific best practice guidelines and genetic variants will help identify the missing patients so they can benefit from the more effective clinical care that a genetic diagnosis brings.

Evaluation of Evidence for Pathogenicity Demonstrates That BLK, KLF11, and PAX4 Should Not Be Included in Diagnostic Testing for MODY.

Maturity-onset diabetes of the young (MODY) is an autosomal dominant form of monogenic diabetes, reported to be caused by variants in 16 genes. Concern has been raised about whether variants in BLK (MODY11), KLF11 (MODY7), and PAX4 (MODY9) cause MODY. We examined variant-level genetic evidence (cosegregation with diabetes and frequency in population) for published putative pathogenic variants in these genes and used burden testing to test gene-level evidence in a MODY cohort (n = 1,227) compared with a control population (UK Biobank [n = 185,898]). For comparison we analyzed well-established causes of MODY, HNF1A, and HNF4A. The published variants in BLK, KLF11, and PAX4 showed poor cosegregation with diabetes (combined logarithm of the odds [LOD] scores ≤1.2), compared with HNF1A and HNF4A (LOD scores >9), and are all too common to cause MODY (minor allele frequency >4.95 × 10-5). Ultra-rare missense and protein-truncating variants (PTV) were not enriched in a MODY cohort compared with the UK Biobank population (PTV P > 0.05, missense P > 0.1 for all three genes) while HNF1A and HNF4A were enriched (P < 10-6). Findings of sensitivity analyses with different population cohorts supported our results. Variant and gene-level genetic evidence does not support BLK, KLF11, or PAX4 as a cause of MODY. They should not be included in MODY diagnostic genetic testing.

Improvements in Awareness and Testing Have Led to a Threefold Increase Over 10 Years in the Identification of Monogenic Diabetes in the U.K.

OBJECTIVE: Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes. In 2009, >80% of U.K. cases were estimated to be misdiagnosed. Since then, there have been a number of initiatives to improve the awareness and detection of MODY, including education initiatives (Genetic Diabetes Nurse [GDN] project), the MODY probability calculator, and targeted next-generation sequencing (tNGS). We examined how the estimated prevalence of MODY and other forms of monogenic diabetes diagnosed outside the neonatal period has changed over time and how the initiatives have impacted case finding. RESEARCH DESIGN AND METHODS: U.K. referrals for genetic testing for monogenic diabetes diagnosed >1 year of age from 1 January 1996 to 31 December 2019 were examined. Positive test rates were compared for referrals reporting GDN involvement/MODY calculator use with those that did not. RESULTS: A diagnosis of monogenic diabetes was confirmed in 3,860 individuals, more than threefold higher than 2009 (1 January 1996 to 28 February 2009, n = 1,177). Median age at diagnosis in probands was 21 years. GDN involvement was reported in 21% of referrals; these referrals had a higher positive test rate than those without GDN involvement (32% vs. 23%, P < 0.001). MODY calculator usage was indicated in 74% of eligible referrals since 2014; these referrals had a higher positive test rate than those not using the calculator (33% vs. 25%, P = 0.001). Four hundred ten (10.6%) cases were identified through tNGS. Monogenic diabetes prevalence was estimated to be 248 cases/million (double that estimated in 2009 because of increased case finding). CONCLUSIONS: Since 2009, referral rates and case diagnosis have increased threefold. This is likely to be the consequence of tNGS, GDN education, and use of the MODY calculator.

Syndromic Monogenic Diabetes Genes Should Be Tested in Patients With a Clinical Suspicion of Maturity-Onset Diabetes of the Young.

At present, outside of infancy, genetic testing for monogenic diabetes is typically for mutations in maturity-onset diabetes of the young (MODY) genes that predominantly result in isolated diabetes. Monogenic diabetes syndromes are usually only tested for when supported by specific syndromic clinical features. How frequently patients with suspected MODY have a mutation in a monogenic syndromic diabetes gene is unknown and thus missed by present testing regimes. We performed genetic testing of 27 monogenic diabetes genes (including 18 associated with syndromic diabetes) for 1,280 patients with a clinical suspicion of MODY who were not suspected of having monogenic syndromic diabetes. We confirmed monogenic diabetes in 297 (23%) patients. Mutations in seven different syndromic diabetes genes accounted for 19% (95% CI 15-24%) of all monogenic diabetes. The mitochondrial m.3243A>G and mutations in HNF1B were responsible for the majority of mutations in syndromic diabetes genes. They were also the 4th and 5th most common causes of monogenic diabetes overall. These patients lacked typical features, and their diabetes phenotypes overlapped with patients with nonsyndromic monogenic diabetes. Syndromic monogenic diabetes genes (particularly m.3243A>G and HNF1B) should be routinely tested in patients with suspected MODY who do not have typical features of a genetic syndrome.

Systematic genetic testing for recessively inherited monogenic diabetes: a cross-sectional study in paediatric diabetes clinics.

AIMS/HYPOTHESIS: Current clinical guidelines for childhood-onset monogenic diabetes outside infancy are mainly focused on identifying and testing for dominantly inherited, predominantly MODY genes. There are no systematic studies of the recessively inherited causes of monogenic diabetes that are likely to be more common in populations with high rates of consanguinity. We aimed to determine the contribution of recessive causes of monogenic diabetes in paediatric diabetes clinics and to identify clinical criteria by which to select individuals for recessive monogenic diabetes testing. METHODS: We conducted a cross-sectional study of 1093 children from seven paediatric diabetes clinics across Turkey (a population with high rates of consanguinity). We undertook genetic testing of 50 known dominant and recessive causes of monogenic diabetes for 236 children at low risk of type 1 diabetes. As a comparison, we used monogenic diabetes cases from UK paediatric diabetes clinics (a population with low rates of consanguinity). RESULTS: Thirty-four children in the Turkish cohort had monogenic diabetes, equating to a minimal prevalence of 3.1%, similar to that in the UK cohort (p = 0.40). Forty-one per cent (14/34) had autosomal recessive causes in contrast to 1.6% (2/122) in the UK monogenic diabetes cohort (p < 0.0001). All conventional criteria for identifying monogenic diabetes (parental diabetes, not requiring insulin treatment, HbA1c ≤ 58 mmol/mol [≤7.5%] and a composite clinical probability of MODY >10%) assisted the identification of the dominant (all p ≤ 0.0003) but not recessive cases (all p ≥ 0.2) in Turkey. The presence of certain non-autoimmune extra-pancreatic features greatly assisted the identification of recessive (p < 0.0001, OR 66.9) but not dominant cases. CONCLUSIONS/INTERPRETATION: Recessively inherited mutations are a common cause of monogenic diabetes in populations with high rates of consanguinity. Present MODY-focused genetic testing strategies do not identify affected individuals. To detect all cases of monogenic paediatric diabetes, it is crucial that recessive genes are included in genetic panels and that children are selected for testing if they have certain non-autoimmune extra-pancreatic features in addition to current criteria.