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.

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.

Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years: an instrumental variable approach.

AIMS/HYPOTHESIS: Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. METHODS: Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. RESULTS: Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3]). CONCLUSIONS/INTERPRETATION: Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.

Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes.

AIMS/HYPOTHESIS: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

Developmentally dynamic changes in DNA methylation in the human pancreas.

Development of the human pancreas requires the precise temporal control of gene expression via epigenetic mechanisms and the binding of key transcription factors. We quantified genome-wide patterns of DNA methylation in human fetal pancreatic samples from donors aged 6 to 21 post-conception weeks. We found dramatic changes in DNA methylation across pancreas development, with > 21% of sites characterized as developmental differentially methylated positions (dDMPs) including many annotated to genes associated with monogenic diabetes. An analysis of DNA methylation in postnatal pancreas tissue showed that the dramatic temporal changes in DNA methylation occurring in the developing pancreas are largely limited to the prenatal period. Significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small proportion of sites showing sex-specific DNA methylation trajectories across pancreas development. Pancreas dDMPs were not distributed equally across the genome and were depleted in regulatory domains characterized by open chromatin and the binding of known pancreatic development transcription factors. Finally, we compared our pancreas dDMPs to previous findings from the human brain, identifying evidence for tissue-specific developmental changes in DNA methylation. This study represents the first systematic exploration of DNA methylation patterns during human fetal pancreas development and confirms the prenatal period as a time of major epigenomic plasticity.

Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight.

BACKGROUND: Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. AIM: We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. METHODS: We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. RESULTS: Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602). CONCLUSIONS: Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes.

BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young.

BACKGROUND: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. METHODS: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. RESULTS: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. CONCLUSION: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

Recurrent 17q12 microduplications contribute to renal disease but not diabetes.

BACKGROUND: 17q12 microdeletion and microduplication syndromes present as overlapping, multisystem disorders. We assessed the disease phenotypes of individuals with 17q12 CNV in a population-based cohort. METHODS: We investigated 17q12 CNV using microarray data from 450 993 individuals in the UK Biobank and calculated disease status associations for diabetes, liver and renal function, neurological and psychiatric traits. RESULTS: We identified 11 17q12 microdeletions and 106 microduplications. Microdeletions were strongly associated with diabetes (p=2×10-7) but microduplications were not. Estimated glomerular filtration rate (eGFR mL/min/1.73 m2) was consistently lower in individuals with microdeletions (p=3×10-12) and microduplications (p=6×10-25). Similarly, eGFR <60, including end-stage renal disease, was associated with microdeletions (p=2×10-9, p<0.003) and microduplications (p=1×10-9, p=0.009), respectively, highlighting sometimes substantially reduced renal function in each. Microduplications were associated with decreased fluid intelligence (p=3×10-4). SNP association analysis in the 17q12 region implicated changes to HNF1B as causing decreased eGFR (NC_000017.11:g.37741642T>G, rs12601991, p=4×10-21) and diabetes (NC_000017.11:g.37741165C>T, rs7501939, p=6×10-17). A second locus within the region was also associated with fluid intelligence (NC_000017.11:g.36593168T>C, rs1005552, p=6×10-9) and decreased eGFR (NC_000017.11:g.36558947T>C, rs12150665, p=4×10-15). CONCLUSION: We demonstrate 17q12 microdeletions but not microduplications are associated with diabetes in a population-based cohort, likely caused by HNF1B haploinsufficiency. We show that both 17q12 microdeletions and microduplications are associated with renal disease, and multiple genes within the region likely contribute to renal and neurocognitive phenotypes.

The impact of population-level HbA1c screening on reducing diabetes diagnostic delay in middle-aged adults: a UK Biobank analysis.

AIMS/HYPOTHESIS: Screening programmes can detect cases of undiagnosed diabetes earlier than symptomatic or incidental diagnosis. However, the improvement in time to diagnosis achieved by screening programmes compared with routine clinical care is unclear. We aimed to use the UK Biobank population-based study to provide the first population-based estimate of the reduction in time to diabetes diagnosis that could be achieved by HbA1c-based screening in middle-aged adults. METHODS: We studied UK Biobank participants aged 40-70 years with HbA1c measured at enrolment (but not fed back to participants/clinicians) and linked primary and secondary healthcare data (n=179,923) and identified those with a pre-existing diabetes diagnosis (n=13,077, 7.3%). Among the remaining participants (n=166,846) without a diabetes diagnosis, we used an elevated enrolment HbA1c level (≥48 mmol/mol [≥6.5%]) to identify those with undiagnosed diabetes. For this group, we used Kaplan-Meier analysis to assess the time between enrolment HbA1c measurement and subsequent clinical diabetes diagnosis up to 10 years, and Cox regression to identify clinical factors associated with delayed diabetes diagnosis. RESULTS: In total, 1.0% (1703/166,846) of participants without a diabetes diagnosis had undiagnosed diabetes based on calibrated HbA1c levels at UK Biobank enrolment, with a median HbA1c level of 51.3 mmol/mol (IQR 49.1-57.2) (6.8% [6.6-7.4]). These participants represented an additional 13.0% of diabetes cases in the study population relative to the 13,077 participants with a diabetes diagnosis. The median time to clinical diagnosis for those with undiagnosed diabetes was 2.2 years, with a median HbA1c at clinical diagnosis of 58.2 mmol/mol (IQR 51.0-80.0) (7.5% [6.8-9.5]). Female participants with lower HbA1c and BMI measurements at enrolment experienced the longest delay to clinical diagnosis. CONCLUSIONS/INTERPRETATION: Our population-based study shows that HbA1c screening in adults aged 40-70 years can reduce the time to diabetes diagnosis by a median of 2.2 years compared with routine clinical care. The findings support the use of HbA1c screening to reduce the time for which individuals are living with undiagnosed diabetes.

Bringing precision medicine to the management of pregnancy in women with glucokinase-MODY: a study of diagnostic accuracy and feasibility of non-invasive prenatal testing.

AIMS/HYPOTHESIS: In pregnancies where the mother has glucokinase-MODY (GCK-MODY), fetal growth is determined by fetal genotype. When the fetus inherits a maternal pathogenic GCK variant, normal fetal growth is anticipated, and insulin treatment of maternal hyperglycaemia is not recommended. At present, fetal genotype is estimated from measurement of fetal abdominal circumference on ultrasound. Non-invasive prenatal testing of fetal GCK genotype (NIPT-GCK) using cell-free DNA in maternal blood has recently been developed. We aimed to compare the diagnostic accuracy of NIPT-GCK with that of ultrasound, and determine the feasibility of using NIPT-GCK to guide pregnancy management. METHODS: We studied an international cohort of pregnant women with hyperglycaemia due to GCK-MODY. We compared the diagnostic accuracy of NIPT-GCK with that of measurement of fetal abdominal circumference at 28 weeks' gestation (n=38) using a directly genotyped offspring sample as the reference standard. In a feasibility study, we assessed the time to result given to clinicians in 43 consecutive pregnancies affected by GCK-MODY between July 2019 and September 2021. RESULTS: In terms of diagnostic accuracy, NIPT-GCK was more sensitive and specific than ultrasound in predicting fetal genotype (sensitivity 100% and specificity 96% for NIPT-GCK vs sensitivity 53% and specificity 61% for fetal abdominal circumference 75th percentile). In terms of feasibility, a valid NIPT-GCK fetal genotype (≥95% probability) was reported in all 38 pregnancies with an amenable variant and repeated samples when needed. The median time to report was 5 weeks (IQR 3-8 weeks). For the 25 samples received before 20 weeks' gestation, results were reported at a median gestational age of 20 weeks (IQR 18-24), with 23/25 (92%) reported before 28 weeks. CONCLUSIONS/INTERPRETATION: Non-invasive prenatal testing of fetal genotype in GCK-MODY pregnancies is highly accurate and is capable of providing a result before the last trimester for most patients. This means that non-invasive prenatal testing of fetal genotype is the optimal approach to management of GCK-MODY pregnancies.

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.

FOXP3 TSDR Measurement Could Assist Variant Classification and Diagnosis of IPEX Syndrome.

Pathogenic FOXP3 variants cause immune dysregulation polyendocrinopathy enteropathy X-linked (IPEX) syndrome, a progressive autoimmune disease resulting from disruption of the regulatory T cell (Treg) compartment. Assigning pathogenicity to novel variants in FOXP3 is challenging due to the heterogeneous phenotype and variable immunological abnormalities. The number of cells with demethylation at the Treg cell-specific demethylated region (TSDR) is an independent biomarker of IPEX. We aimed to investigate if diagnosing IPEX at presentation with isolated diabetes could allow for effective monitoring of disease progression and assess whether TSDR analysis can aid FOXP3 variant classification and predict disease course. We describe a large genetically diagnosed IPEX cohort (n = 65) and 13 individuals with other monogenic autoimmunity subtypes in whom we quantified the proportion of cells with FOXP3 TSDR demethylation, normalized to the number with CD4 demethylation (%TSDR/CD4) and compare them to 29 unaffected controls. IPEX patients presenting with isolated diabetes (50/65, 77%) often later developed enteropathy (20/50, 40%) with a median interval of 23.5 weeks. %TSDR/CD4 was a good discriminator of IPEX vs. unaffected controls (ROC-AUC 0.81, median 13.6% vs. 8.5%, p < 0.0001) with higher levels of demethylation associated with more severe disease. Patients with other monogenic autoimmunity had a similar %TSDR/CD4 to controls (median 8.7%, p = 1.0). Identifying increased %TSDR/CD4 in patients with novel FOXP3 mutations presenting with isolated diabetes facilitates diagnosis and could offer an opportunity to monitor patients and begin immune modulatory treatment before onset of severe enteropathy.

Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies.

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced environment-related growth-restriction or overgrowth, respectively, and babies who are heritably small or large. However, the relative proportions within each group are unclear. We assessed the extent to which common genetic variants underlying variation in birth weight influence the probability of being SGA or LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 11,951 babies and 5,182 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal = 0.75 (0.71,0.80) and 1.32 (1.26,1.39); ORmaternal = 0.81 (0.75,0.88) and 1.17 (1.09,1.25), respectively per 1 decile higher GS). We found evidence that the smallest 3% of babies had a higher BW GS, on average, than expected from their observed birth weight (assuming an additive polygenic model: Pfetal = 0.014, Pmaternal = 0.062). Higher maternal SBP GS was associated with higher odds of SGA P = 0.005. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

OBJECTIVE: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

Understanding the pathogenesis of lean non-autoimmune diabetes in an African population with newly diagnosed diabetes.

AIMS/HYPOTHESIS: Apparent type 2 diabetes is increasingly reported in lean adult individuals in sub-Saharan Africa. However, studies undertaking robust clinical and metabolic characterisation of lean individuals with new-onset type 2 diabetes are limited in this population. This cross-sectional study aimed to perform a detailed clinical and metabolic characterisation of newly diagnosed adult patients with diabetes in Uganda, in order to compare features between lean and non-lean individuals. METHODS: Socio-demographic, clinical, biophysical and metabolic (including oral glucose tolerance test) data were collected on 568 adult patients with newly diagnosed diabetes. Participants were screened for islet autoantibodies to exclude those with autoimmune diabetes. The remaining participants (with type 2 diabetes) were then classified as lean (BMI <25 kg/m2) or non-lean (BMI ≥25 kg/m2), and their socio-demographic, clinical, biophysical and metabolic characteristics were compared. RESULTS: Thirty-four participants (6.4%) were excluded from analyses because they were positive for pancreatic autoantibodies, and a further 34 participants because they had incomplete data. For the remaining 500 participants, the median (IQR) age, BMI and HbA1c were 48 years (39-58), 27.5 kg/m2 (23.6-31.4) and 90 mmol/mol (61-113) (10.3% [7.7-12.5]), respectively, with a female predominance (approximately 57%). Of the 500 participants, 160 (32%) and 340 (68%) were lean and non-lean, respectively. Compared with non-lean participants, lean participants were mainly male (60.6% vs 35.3%, p<0.001) and had lower visceral adiposity level (5 [4-7] vs 11 [9-13], p<0.001) and features of the metabolic syndrome (uric acid, 246.5 [205.0-290.6] vs 289 [234-347] µmol/l, p<0.001; leptin, 660.9 [174.5-1993.1] vs 3988.0 [1336.0-6595.0] pg/ml, p<0.001). In addition, they displayed markedly reduced markers of beta cell function (oral insulinogenic index 0.8 [0.3-2.5] vs 1.6 [0.6-4.6] pmol/mmol; 120 min serum C-peptide 0.70 [0.33-1.36] vs 1.02 [0.60-1.66] nmol/l, p<0.001). CONCLUSIONS/INTERPRETATION: Approximately one-third of participants with incident adult-onset non-autoimmune diabetes had BMI <25 kg/m2. Diabetes in these lean individuals was more common in men, and predominantly associated with reduced pancreatic secretory function rather than insulin resistance. The underlying pathological mechanisms are unclear, but this is likely to have important management implications.

Congenital beta cell defects are not associated with markers of islet autoimmunity, even in the context of high genetic risk for type 1 diabetes.

AIMS/HYPOTHESIS: A key unanswered question in type 1 diabetes is whether beta cells initiate their own destruction or are victims of an aberrant immune response (beta cell suicide or homicide?). To investigate this, we assessed islet autoantibodies in individuals with congenital beta cell defects causing neonatal diabetes mellitus (NDM). METHODS: We measured autoantibodies to GAD (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A) in 242 individuals with NDM (median age diagnosed 1.8 months [IQR 0.39-2.9 months]; median age collected 4.6 months [IQR 1.8-27.6 months]; median diabetes duration 2 months [IQR 0.6-23 months]), including 75 whose NDM resulted from severe beta cell endoplasmic reticulum (ER) stress. As a control cohort we also tested samples from 69 diabetes-free individuals (median age collected 9.9 months [IQR 9.0-48.6 months]) for autoantibodies. RESULTS: We found low prevalence of islet autoantibodies in individuals with monogenic NDM; 13/242 (5.4% [95% CI 2.9, 9.0%]) had detectable GADA, IA-2A and/or ZnT8A. This was similar to the proportion in the control participants who did not have diabetes (1/69 positive [1.4%, 95% CI 0.03, 7.8%], p=0.3). Importantly, monogenic individuals with beta cell ER stress had a similar rate of GADA/IA-2A/ZnT8A positivity to non-ER stress aetiologies (2.7% [95% CI 0.3, 9.3%] vs 6.6% [95% CI 3.3, 11.5%] p=0.4). We observed no association between islet autoimmunity and genetic risk, age at testing (including 30 individuals >10 years at testing) or diabetes duration (p>0.4 for all). CONCLUSIONS/INTERPRETATION: Our data support the hypothesis that beta cell stress/dysfunction alone does not lead to the production of islet autoantibodies, even in the context of high-risk HLA types. This suggests that additional factors are required to trigger an autoimmune response towards beta cells.

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.

Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting.

More than 100,000 genetic variants are classified as disease causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and might be over-estimated by clinical ascertainment. Here, we use data from 379,768 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants. Although rare variants are harder to genotype accurately than common variants, we were able to classify as high quality 1,244 of 4,585 (27%) putatively clinically relevant rare (MAF < 1%) variants genotyped on the UKB microarray. We defined as "clinically relevant" variants that were classified as either pathogenic or likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases: maturity-onset diabetes of the young (MODY) and severe developmental disorders (DDs). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically relevant traits. 27 of the variants were associated with a UKB trait, and we were able to refine the penetrance estimate for some of the variants. For example, the HNF4A c.340C>T (p.Arg114Trp) (GenBank: NM_175914.4) variant associated with diabetes is <10% penetrant by the time an individual is 40 years old. We also observed associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g., heterozygous carriers of the ERCC4 c.2395C>T (p.Arg799Trp) variant that causes Xeroderma pigmentosum were more susceptible to sunburn. Finally, we refute the previous disease association of RNF135 in developmental disorders. In conclusion, this study shows that very large population-based studies will help refine our understanding of the pathogenicity of rare genetic variants.

A Specific CNOT1 Mutation Results in a Novel Syndrome of Pancreatic Agenesis and Holoprosencephaly through Impaired Pancreatic and Neurological Development.

We report a recurrent CNOT1 de novo missense mutation, GenBank: NM_016284.4; c.1603C>T (p.Arg535Cys), resulting in a syndrome of pancreatic agenesis and abnormal forebrain development in three individuals and a similar phenotype in mice. CNOT1 is a transcriptional repressor that has been suggested as being critical for maintaining embryonic stem cells in a pluripotent state. These findings suggest that CNOT1 plays a critical role in pancreatic and neurological development and describe a novel genetic syndrome of pancreatic agenesis and holoprosencephaly.