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ONECUT1 (also known as HNF6) is a transcription factor involved in pancreatic development and β-cell function. Recently, biallelic variants in ONECUT1 were reported as a cause of neonatal diabetes mellitus (NDM) in two subjects, and missense monoallelic variants were associated with type 2 diabetes and possibly maturity-onset diabetes of the young (MODY). Here we examine the role of ONECUT1 variants in NDM, MODY, and type 2 diabetes in large international cohorts of subjects with monogenic diabetes and >400,000 subjects from UK Biobank. We identified a biallelic frameshift ONECUT1 variant as the cause of NDM in one individual. However, we found no enrichment of missense or null ONECUT1 variants among 484 individuals clinically suspected of MODY, in whom all known genes had been excluded. Finally, using a rare variant burden test in the UK Biobank European cohort, we identified a significant association between heterozygous ONECUT1 null variants and type 2 diabetes (P = 0.006) but did not find an association between missense variants and type 2 diabetes. Our results confirm biallelic ONECUT1 variants as a cause of NDM and highlight monoallelic null variants as a risk factor for type 2 diabetes. These findings confirm the critical role of ONECUT1 in human β-cell function. Article Highlights: We confirmed homozygous ONECUT1 variants as causative for neonatal diabetes with the identification of a third case. Rare heterozygous ONECUT1 variants were not enriched in a cohort of 484 individuals clinically suspected of having maturity-onset diabetes of the young. Heterozygous null ONECUT1 variants are significantly associated with type 2 diabetes in the UK Biobank European population. No association was observed between heterozygous ONECUT1 missense variants and type 2 diabetes in UK Biobank. [ABSTRACT FROM AUTHOR]

The article discusses a recent analysis of the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) randomized controlled trial. The study compares the glycemic outcomes of four commonly used drug classes for type 2 diabetes: sulfonylureas, dipeptidyl peptidase 4 (DPP4) inhibitors, glucagon-like peptide 1 receptor agonists (GLP-1RA), and insulin. The findings indicate that there is differential glycemic response among these drug classes, with sex and age being identified as important factors in treatment effectiveness. The article also addresses some points of clarification regarding the study's findings. Overall, the study contributes to the growing evidence of treatment effect heterogeneity in noninsulin type 2 diabetes therapies. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Background Systematic reviews of randomised controlled trials (RCTs) have suggested that maternal vitamin D (25[OH]D) and calcium supplementation increase birth weight. However, limitations of many trials were highlighted in the reviews. Our aim was to combine genetic and RCT data to estimate causal effects of these two maternal traits on offspring birth weight. Methods and findings We performed two-sample mendelian randomisation (MR) using genetic instrumental variables associated with 25(OH)D and calcium that had been identified in genome-wide association studies (GWAS; sample 1; N = 122,123 for 25[OH]D and N = 61,275 for calcium). Associations between these maternal genetic variants and offspring birth weight were calculated in the UK Biobank (UKB) (sample 2; N = 190,406). We used data on mother-child pairs from two United Kingdom birth cohorts (combined N = 5,223) in sensitivity analyses to check whether results were influenced by fetal genotype, which is correlated with the maternal genotype (r [almost equal to] 0.5). Further sensitivity analyses to test the reliability of the results included MR-Egger, weighted-median estimator, 'leave-one-out', and multivariable MR analyses. We triangulated MR results with those from RCTs, in which we used randomisation to supplementation with vitamin D (24 RCTs, combined N = 5,276) and calcium (6 RCTs, combined N = 543) as an instrumental variable to determine the effects of 25(OH)D and calcium on birth weight. In the main MR analysis, there was no strong evidence of an effect of maternal 25(OH)D on birth weight (difference in mean birth weight -0.03 g [95% CI -2.48 to 2.42 g, p = 0.981] per 10% higher maternal 25[OH]D). The effect estimate was consistent across our MR sensitivity analyses. Instrumental variable analyses applied to RCTs suggested a weak positive causal effect (5.94 g [95% CI 2.15-9.73, p = 0.002] per 10% higher maternal 25[OH]D), but this result may be exaggerated because of risk of bias in the included RCTs. The main MR analysis for maternal calcium also suggested no strong evidence of an effect on birth weight (-20 g [95% CI -44 to 5 g, p = 0.116] per 1 SD higher maternal calcium level). Some sensitivity analyses suggested that the genetic instrument for calcium was associated with birth weight via exposures that are independent of calcium levels (horizontal pleiotropy). Application of instrumental variable analyses to RCTs suggested that calcium has a substantial effect on birth weight (178 g [95% CI 121-236 g, p = 1.43 x 10.sup.-9 ] per 1 SD higher maternal calcium level) that was not consistent with any of the MR results. However, the RCT instrumental variable estimate may have been exaggerated because of risk of bias in the included RCTs. Other study limitations include the low response rate of UK Biobank, which may bias MR estimates, and the lack of suitable data to test whether the effects of genetic instruments on maternal calcium levels during pregnancy were the same as those outside of pregnancy. Conclusions Our results suggest that maternal circulating 25(OH)D does not influence birth weight in otherwise healthy newborns. However, the effect of maternal circulating calcium on birth weight is unclear and requires further exploration with more research including RCT and/or MR analyses with more valid instruments., Author(s): William D. Thompson 1,2, Jessica Tyrrell 1, Maria-Carolina Borges 2,3, Robin N. Beaumont 1, Bridget A. Knight 1, Andrew R. Wood 1, Susan M. Ring 2,3,4, Andrew T. Hattersley [...]

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. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes (T2D) is common and increasing in prevalence. It is possible to prevent or delay T2D using lifestyle intervention programmes. Entry to these programmes is usually determined by a measure of glycaemia in the 'intermediate' range. This paper investigated the relationship between HbA1c and future diabetes risk and determined the impact of varying thresholds to identify those at high risk of developing T2D.Methods: We studied 4227 participants without diabetes aged ≥ 40 years recruited to the Exeter 10,000 population cohort in South West England. HbA1c was measured at study recruitment with repeat HbA1c available as part of usual care. Absolute risk of developing diabetes within 5 years, defined by HbA1c ≥ 48 mmol/mol (6.5%), according to baseline HbA1c, was assessed by a flexible parametric survival model.Results: The overall absolute 5-year risk (95% CI) of developing T2D in the cohort was 4.2% (3.6, 4.8%). This rose to 7.1% (6.1, 8.2%) in the 56% (n = 2358/4224) of participants classified 'high-risk' with HbA1c ≥ 39 mmol/mol (5.7%; ADA criteria). Under IEC criteria, HbA1c ≥ 42 mmol/mol (6.0%), 22% (n = 929/4277) of the cohort was classified high-risk with 5-year risk 14.9% (12.6, 17.2%). Those with the highest HbA1c values (44-47 mmol/mol [6.2-6.4%]) had much higher 5-year risk, 26.4% (22.0, 30.5%) compared with 2.1% (1.5, 2.6%) for 39-41 mmol/mol (5.7-5.9%) and 7.0% (5.4, 8.6%) for 42-43 mmol/mol (6.0-6.1%). Changing the entry criterion to prevention programmes from 39 to 42 mmol/mol (5.7-6.0%) reduced the proportion classified high-risk by 61%, and increased the positive predictive value (PPV) from 5.8 to 12.4% with negligible impact on the negative predictive value (NPV), 99.6% to 99.1%. Increasing the threshold further, to 44 mmol/mol (6.2%), reduced those classified high-risk by 59%, and markedly increased the PPV from 12.4 to 23.2% and had little impact on the NPV (99.1% to 98.5%).Conclusions: A large proportion of people are identified as high-risk using current thresholds. Increasing the risk threshold markedly reduces the number of people that would be classified as high-risk and entered into prevention programmes, although this must be balanced against cases missed. Raising the entry threshold would allow limited intervention opportunities to be focused on those most likely to develop T2D. [ABSTRACT FROM AUTHOR]

Latent autoimmune diabetes of adults (LADA) is typically defined as a new diabetes diagnosis after 35 years of age, presenting with clinical features of type 2 diabetes, in whom a type 1 diabetes-associated islet autoantibody is detected. Identifying autoimmune diabetes is important since the prognosis and optimal therapy differ. However, the existing LADA definition identifies a group with clinical and genetic features intermediate between typical type 1 and type 2 diabetes. It is unclear whether this is due to 1) true autoimmune diabetes with a milder phenotype at older onset ages that initially appears similar to type 2 diabetes but later requires insulin, 2) a disease syndrome where the pathophysiologies of type 1 and type 2 diabetes are both present in each patient, or 3) a heterogeneous group resulting from difficulties in classification. Herein, we suggest that difficulties in classification are a major component resulting from defining LADA using a diagnostic test-islet autoantibody measurement-with imperfect specificity applied in low-prevalence populations. This yields a heterogeneous group of true positives (autoimmune type 1 diabetes) and false positives (nonautoimmune type 2 diabetes). For clinicians, this means that islet autoantibody testing should not be undertaken in patients who do not have clinical features suggestive of autoimmune diabetes: in an adult without clinical features of type 1 diabetes, it is likely that a single positive antibody will represent a false-positive result. This is in contrast to patients with features suggestive of type 1 diabetes, where false-positive results will be rare. For researchers, this means that current definitions of LADA are not appropriate for the study of autoimmune diabetes in later life. Approaches that increase test specificity, or prior likelihood of autoimmune diabetes, are needed to avoid inclusion of participants who have nonautoimmune (type 2) diabetes. Improved classification will allow improved assignment of prognosis and therapy as well as an improved cohort in which to analyze and better understand the detailed pathophysiological components acting at onset and during disease progression in late-onset autoimmune diabetes. [ABSTRACT FROM AUTHOR]

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).Research Design and Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.Results: Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression. [ABSTRACT FROM AUTHOR]

Objective: To describe the relationship between type 2 diabetes and all-cause mortality among adults with coronavirus disease 2019 (COVID-19) in the critical care setting.Research Design and Methods: This was a nationwide retrospective cohort study in people admitted to hospital in England with COVID-19 requiring admission to a high dependency unit (HDU) or intensive care unit (ICU) between 1 March 2020 and 27 July 2020. Cox proportional hazards models were used to estimate 30-day in-hospital all-cause mortality associated with type 2 diabetes, with adjustment for age, sex, ethnicity, obesity, and other major comorbidities (chronic respiratory disease, asthma, chronic heart disease, hypertension, immunosuppression, chronic neurological disease, chronic renal disease, and chronic liver disease).Results: A total of 19,256 COVID-19-related HDU and ICU admissions were included in the primary analysis, including 13,809 HDU (mean age 70 years) and 5,447 ICU (mean age 58 years) admissions. Of those admitted, 3,524 (18.3%) had type 2 diabetes and 5,077 (26.4%) died during the study period. Patients with type 2 diabetes were at increased risk of death (adjusted hazard ratio [aHR] 1.23 [95% CI 1.14, 1.32]), and this result was consistent in HDU and ICU subsets. The relative mortality risk associated with type 2 diabetes decreased with higher age (age 18-49 years aHR 1.50 [95% CI 1.05, 2.15], age 50-64 years 1.29 [1.10, 1.51], and age ≥65 years 1.18 [1.09, 1.29]; P value for age-type 2 diabetes interaction = 0.002).Conclusions: Type 2 diabetes may be an independent prognostic factor for survival in people with severe COVID-19 requiring critical care treatment, and in this setting the risk increase associated with type 2 diabetes is greatest in younger people. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the association between metformin use and anemia risk in type 2 diabetes, and the time-course for this, in a randomized controlled trial (RCT) and real-world population data.Research Design and Methods: Anemia was defined as a hemoglobin measure of <11 g/dL. In the RCTs A Diabetes Outcome Progression Trial (ADOPT; n = 3,967) and UK Prospective Diabetes Study (UKPDS; n = 1,473), logistic regression was used to model anemia risk and nonlinear mixed models for change in hematological parameters. In the observational Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) population (n = 3,485), discrete-time failure analysis was used to model the effect of cumulative metformin exposure on anemia risk.Results: In ADOPT, compared with sulfonylureas, the odds ratio (OR) (95% CI) for anemia was 1.93 (1.10, 3.38) for metformin and 4.18 (2.50, 7.00) for thiazolidinediones. In UKPDS, compared with diet, the OR (95% CI) was 3.40 (1.98, 5.83) for metformin, 0.96 (0.57, 1.62) for sulfonylureas, and 1.08 (0.62, 1.87) for insulin. In ADOPT, hemoglobin and hematocrit dropped after metformin initiation by 6 months, with no further decrease after 3 years. In UKPDS, hemoglobin fell by 3 years in the metformin group compared with other treatments. At years 6 and 9, hemoglobin was reduced in all treatment groups, with no greater difference seen in the metformin group. In GoDARTS, each 1 g/day of metformin use was associated with a 2% higher annual risk of anemia.Conclusions: Metformin use is associated with early risk of anemia in individuals with type 2 diabetes, a finding consistent across two RCTs and replicated in one real-world study. The mechanism for this early fall in hemoglobin is uncertain, but given the time course, is unlikely to be due to vitamin B12 deficiency alone. [ABSTRACT FROM AUTHOR]

Aims: To investigate whether combinations of routinely available clinical features can predict which patients are likely to be non‐adherent to diabetes medication. Materials and Methods: A total of 67 882 patients with prescription records for their first and second oral glucose‐lowering therapies were identified from electronic healthcare records (Clinical Practice Research Datalink). Non‐adherence was defined as a medical possession ratio (MPR) ≤80%. Potential predictors were examined, including age at diagnosis, sex, body mass index, duration of diabetes, glycated haemoglobin, Charlson index and other recent prescriptions. Results: Routine clinical features were poor at predicting non‐adherence to the first diabetes therapy (c‐statistic = 0.601 for all in combined model). Non‐adherence to the second drug was better predicted for all combined factors (c‐statistic =0.715) but this improvement was predominantly a result of including adherence to the first drug (c‐statistic =0.695 for this alone). Patients with an MPR ≤80% for their first drug were 3.6 times (95% confidence interval 3.3,3.8) more likely to be non‐adherent to their second drug (32% vs. 9%). Conclusions: Although certain clinical features were associated with poor adherence, their performance for predicting who is likely to be non‐adherent, even when combined, was weak. The strongest predictor of adherence to second‐line therapy was adherence to the first therapy. Examining previous prescription records could offer a practical way for clinicians to identify potentially non‐adherent patients and is an area warranting further research. [ABSTRACT FROM AUTHOR]

Aim: To describe population‐level time trends in prescribing patterns of type 2 diabetes therapy, and in short‐term clinical outcomes (glycated haemoglobin [HbA1c], weight, blood pressure, hypoglycaemia and treatment discontinuation) after initiating new therapy. Materials and methods: We studied 81 532 people with type 2 diabetes initiating a first‐ to fourth‐line drug in primary care between 2010 and 2017 inclusive in United Kingdom electronic health records (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent 6‐ and 12‐month adjusted changes in glycaemic response (reduction in HbA1c), weight, blood pressure and rates of hypoglycaemia and treatment discontinuation were examined. Results: Use of dipeptidyl peptidase‐4 inhibitors as second‐line therapy near doubled (41% of new prescriptions in 2017 vs. 22% in 2010), replacing sulphonylureas as the most common second‐line drug (29% in 2017 vs. 53% in 2010). Sodium‐glucose co‐transporter‐2 inhibitors, introduced in 2013, comprised 17% of new first‐ to fourth‐line prescriptions by 2017. First‐line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% in 2010). Over the study period there was little change in average glycaemic response and in the proportion of people discontinuing treatment. There was a modest reduction in weight after initiating second‐ and third‐line therapy (improvement in weight change 2017 vs. 2010 for second‐line therapy: −1.5 kg, 95% confidence interval [CI] −1.9, −1.1; P < 0.001), and a slight reduction in systolic blood pressure after initiating first‐, second‐ and third‐line therapy (improvement in systolic blood pressure change 2017 vs. 2010 range: −1.7 to −2.1 mmHg; all P < 0.001). Hypoglycaemia rates decreased over time with second‐line therapy (incidence rate ratio 0.94 per year, 95% CI 0.88, 1.00; P = 0.04), mirroring the decline in use of sulphonylureas. Conclusions: Recent changes in prescribing of therapy for people with type 2 diabetes have not led to a change in glycaemic response and have resulted in modest improvements in other population‐level short‐term clinical outcomes. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Late-onset type 1 diabetes can be difficult to identify. Measurement of endogenous insulin secretion using C-peptide provides a gold standard classification of diabetes type in longstanding diabetes that closely relates to treatment requirements. We aimed to determine the prevalence and characteristics of type 1 diabetes defined by severe endogenous insulin deficiency after age 30 and assess whether these individuals are identified and managed as having type 1 diabetes in clinical practice. Methods: We assessed the characteristics of type 1 diabetes defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (non-fasting C-peptide <200 pmol/l) in 583 participants with insulin-treated diabetes, diagnosed after age 30, from the Diabetes Alliance for Research in England (DARE) population cohort. We compared characteristics with participants with retained endogenous insulin secretion (>600 pmol/l) and 220 participants with severe insulin deficiency who were diagnosed under age 30. Results: Twenty-one per cent of participants with insulin-treated diabetes who were diagnosed after age 30 met the study criteria for type 1 diabetes. Of these participants, 38% did not receive insulin at diagnosis, of whom 47% self-reported type 2 diabetes. Rapid insulin requirement was highly predictive of severe endogenous insulin deficiency: 85% required insulin within 1 year of diagnosis, and 47% of all those initially treated without insulin who progressed to insulin treatment within 3 years of diagnosis had severe endogenous insulin deficiency. Participants with late-onset type 1 diabetes defined by development of severe insulin deficiency had similar clinical characteristics to those with young-onset type 1 diabetes. However, those with later onset type 1 diabetes had a modestly lower type 1 diabetes genetic risk score (0.268 vs 0.279; p < 0.001 [expected type 2 diabetes population median, 0.231]), a higher islet autoantibody prevalence (GAD-, islet antigen 2 [IA2]- or zinc transporter protein 8 [ZnT8]-positive) of 78% at 13 years vs 62% at 26 years of diabetes duration; (p = 0.02), and were less likely to identify as having type 1 diabetes (79% vs 100%; p < 0.001) vs those with young-onset disease. Conclusions/interpretation: Type 1 diabetes diagnosed over 30 years of age, defined by severe insulin deficiency, has similar clinical and biological characteristics to that occurring at younger ages, but is frequently not identified. Clinicians should be aware that patients progressing to insulin within 3 years of diagnosis have a high likelihood of type 1 diabetes, regardless of initial diagnosis. [ABSTRACT FROM AUTHOR]

Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such favorable adiposity alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined MRI data with genome-wide association studies of body fat percentage (%) and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity but a favorable metabolic profile. Consistent with previous studies, individuals carrying more favorable adiposity alleles had higher body fat % and higher BMI but lower risk of type 2 diabetes, heart disease, and hypertension. These individuals also had higher subcutaneous fat but lower liver fat and a lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14, and IRS1, whereas the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified favorable adiposity alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglycerides in metabolically low-risk depots. [ABSTRACT FROM AUTHOR]

Purpose of study: An overlooked question in precision / stratified medicine and when evaluating new medications is: are the benefits and risks of a drug associated? Joint longitudinal-survival models can be applied to answer this question when, as in type 2 diabetes, drug response is measured by a longitudinal biomarker (HbA1c) and risks of side-effects can be represented as a time-to-event outcome. What did we do and find? We used joint longitudinal–survival models to show novel associations between the benefit of greater drug response and the risk of common side effects for three glucose-lowering medications for patients with type 2 diabetes. Greater drug response was associated with an increased risk of hypoglycemia with sulfonylureas and edema with thiazolidinediones. In contrast, there was no evidence of an increased risk of gastrointestinal side effects with metformin. What do the findings mean? Joint models provide a novel, flexible, and robust approach to study the associations between the risks and benefits of drug therapy. Precision/stratified medicine studies seeking to identify patients or subgroups likely to respond well to a drug should also evaluate whether the same patients are at increased risk of side effects. Introduction There is an increasing interest in applying a precision medicine approach to select the most appropriate drug for a patient or subgroup of patients, in order to either improve response or reduce side effects. 1 , 2 An important but overlooked question, particularly if side effects are a result of the primary pharmacological effect of the drug, is whether the patients most likely to benefit are also at greatest risk of side effects. Type 2 diabetes is an ideal candidate for precision medicine, as there are many drug options to lower blood glucose (as measured by HbA1c), but each drug has a different mechanism of action and specific side effects. However, the association between HbA1c response and side effects is unknown for all drug options. If patients likely to have a greater HbA1c response to a specific drug are also at increased risk of side effects, this may limit the clinical utility of any precision approach to type 2 diabetes therapy. To date, no robust framework has been proposed to evaluate the association between drug response and risk of side effects. In type 2 diabetes, HbA1c is measured repeatedly over time (a longitudinal process), while side effect risk can be modeled as a time-to-event process. In this scenario, joint longitudinal–survival modeling is the preferred approach to evaluate the association between both processes. 3 – 6 Joint models attempt to capture the true, unobserved, longitudinal trajectory (in reality, HbA1c is measured intermittently and is subjected to measurement error from random noise and biological variation). This means that joint models can reduce bias and improve efficiency compared with simpler approaches. 5 , 7 Joint models have been applied in many diseases including recently in type 1 diabetes (autoantibodies and time to disease onset), 8 – 11 but not to our knowledge in type 2 diabetes, or more broadly to evaluate the association between drug response and the risk of side effects. In this study, we applied joint modeling to evaluate the association between drug response and the risk of established side effects for three widely used type 2 diabetes drugs and, thus, further evaluate the potential for precision drug therapy in type 2 diabetes. Methods Overview Our aim was to understand whether the degree of glycemic response to three common glucose-lowering drugs altered the risk of developing a side effect. To answer this question, we examined the association between the following two outcomes: 1) HbA1c response (as measured by change from baseline in HbA1c) and 2) risk of developing a side effect (gastrointestinal [GI] events, hypoglycemia, edema, and fracture). Setting and design We used individual participant level data from A Diabetes Outcome Progressing Trial (ADOPT) randomized trial, 12 accessed through the Clinical Trial Data Transparency Portal under approval from GlaxoSmithKline (GSK) (Proposal-930). 13 ADOPT was a prospective head-to-head drug trial including treatment-naive participants with type 2 diabetes who were randomized to metformin (MFN), the sulfonylurea (SU) glyburide, or the thiazolidinedione (TZD) rosiglitazone (n=4,351 participants). The aim of ADOPT was to evaluate the long-term efficacy of TZD therapy compared to SU and MFN, and the primary outcome was time to therapy failure (confirmed fasting plasma glucose ≥180 mg/dL). Study visits were every 2 months in year 1, then every 3 months up to 5 years. Clinically determined adverse events were recorded at each study visit, including GI events, hypoglycemia, edema, and fracture. Biomarkers including HbA1c were recorded at each visit. ADOPT participants in the intention to treat population with a valid baseline HbA1c were eligible for our study. Participants were censored if they reached the trial primary endpoint of glycemic failure, trial-recorded study withdrawal, or at 5 years after starting therapy as in the ADOPT main analysis. Study outcomes Our time-to-event outcomes were the first occurrence of four established drug-specific side effects, over a 5-year period. For MFN, the outcome of interest was a GI event, for SU, it was hypoglycemia (patient self-reported), and for TZD, we evaluated edema and bone fractures. 12 Each drug and side effect combination was analyzed separately. We excluded patients with a pre-trial history of edema from the edema analysis (6% of patients), but pre-trial hypoglycemia, GI, and fracture records were not available to do the same for other side effects. Due to the high number of GI events, we repeated the GI analysis restricted to only moderate/severe and severe events as sensitivity analysis. The longitudinal outcome of interest was HbA1c response as measured by change from baseline in HbA1c (HbA1c at each study visit [%] – baseline HbA1c [%]). Throughout HbA1c percentages refer to absolute values rather than percentage changes. To test the specificity of our findings, we repeated the analysis for each side effect for the other drugs. Statistical analysis We used a joint model with two parameterizations (Models 1 and 2) and two standard time-to-event models (Models 3 and 4), for comparison, to evaluate the association between HbA1c response and the risk of developing a side effect. A fundamental difference between each model was in the method to estimate HbA1c response, as illustrated in Figure 1. Each side effect was evaluated separately, and the same modeling approach was applied for each side effect. Participants were followed up for up to 5 years from randomization. As we were assessing the association between side effects and response, all participants required at least one pre-side effect HbA1c measure (meaning that participants with very early side effects were excluded: 4% of participants with edema, 3% of participants with fracture, 20% of participants with hypoglycemia, and 12% of participants with GI events). All models were adjusted for baseline HbA1c. 14 Model setups were as follows. Figure 1 Approaches to estimating HbA1c (%) response. Notes: Model 1: estimate current HbA1c response using a joint model (red line with black dotted 95% CIs). Model 2: estimate cumulative HbA1c response using a joint model (gray-shaded area). Model 3: carry forward the most recently observed value of HbA1c response until the next measurement (LOCF approach, black step function). Model 4: take the observed HbA1c response at a single time point of 6 months (blue line). Abbreviation: LOCF, last observation carried forward. Joint longitudinal–survival models We used a maximum likelihood joint longitudinal–survival model to simultaneously assess the association between HbA1c response (longitudinal process) and the risk of developing a side effect (survival process). The joint model consisted of the following two parts: a longitudinal submodel and a survival submodel linked through shared subject-specific random effects. 6 In the general survival submodel, the hazard for patient i (hi(t)) can be represented as hi(t) = h0 (t) exp (wiT γ + αmi(t)) where h0 (t) is the baseline hazard, wi are baseline covariates, γ are regression coefficients, mi(t) is the "true, unobserved" longitudinal biomarker (estimated from the longitudinal submodel), and α quantifies the association between the longitudinal biomarker and the time-to-event process. 6 We derived mi(t) from the observed HbA1c response data using a linear mixed effects model with a nonlinear term for time (as HbA1c response is typically nonlinear): yi(t) = mi(t) + εi (t) = β0 + β1N(ti)1 + β2N(ti)2 + β3 Baseline HbA1c + bi0 + bi1N(ti)1 + bi2N(ti)2 + εi (t) where yi is the observed HbA1c change from baseline and mi is the "true", unobserved HbA1c change from baseline. N(ti)1 and N(ti)2 denote the basis for a nonlinear natural cubic spline of time with one internal knot at the 50th percentile of follow-up time (included in both the fixed and random effect parts of the longitudinal HbA1c submodel), bi is a vector of subject-specific random effects, bi~ N (O,) where is the unstructured covariance matrix of random effects, εi is the vector of residuals, and εi ~ N(O,σ2), where σ2 is the covariance matrix of the residuals. 6 For models of hypoglycemia with MFN and edema with SUs, we used a linear term for the random effect of time to achieve model convergence. Model 1: joint model current value (JMcv). To assess the association between the current value of HbA1c response and the risk of side effects (the standard formulation of the joint model), we incorporated mi from the longitudinal submodel as a time-dependent covariate in the survival submodel: hi(t) = h0 (t) exp {γ0Baseline HbA1c + αmi (t)} Model 2: joint model cumulative HbA1c (JMcum). To evaluate whether the risk of side effects was associated with total rather than current HbA1c response, we specified a second formulation of the joint model to assess the association between cumulative HbA1c response (total HbA1c response estimated as area under the curve) and the risk of side effects, by including ∫t0 mi(s) ds, the integral of the longitudinal HbA1c response trajectory up to time t, in the time-to-event submodel: 6 , 15 hi(t) = h0 (t) exp {γ0Baseline HbA1c + α ∫t0 mi(s) ds} For Models 1 and 2, we used a B-spline with five internal knots to flexibly model the baseline hazard function. We examined the fit of submodels using residual plots. Models 1 and 2 were fitted using the JM package in R. 16 Model 3: last observation carried forward (LOCF) analysis. We included observed HbA1c response (HbA1c at time t – baseline HbA1c) as a time-dependent covariate in a Cox proportional hazards model. This approach does not correct for measurement error and assumes that HbA1c response is constant between measurements. HRs represent the increased risk of a side effect for a 1-unit (%) absolute increase in the most recent value of HbA1c change from baseline at time t. Model 4: single estimate of HbA1c response at 6 months (6mR). We evaluated the association between HbA1c response at 6 months and the subsequent risk of developing a side-effect. In this two-stage approach, we first estimated a single estimate of HbA1c response as a change score at 6 months. In the second stage, we used this estimate as the exposure in a Cox hazards survival model with delayed entry to 6 months. Participants who developed a side effect prior to 6 months or had no HbA1c record at 6 months were excluded from this analysis. Results The most common side effects were GI side effects with MFN (37%), followed by hypoglycemia with SU therapy (26%). TZD side effects were less common (edema 13% and fracture 7%; Table 1). The median follow-up was greater than 2.5 years in each cohort (for other participant characteristics, refer Table S1). Each side effect occurred more frequently on these therapies than on the comparator drugs (Table S2). Table 1 Participant numbers and study follow-up for each primary drug: side effect cohort (Models 1–3) Note: Data are median (IQR) unless stated (refer Table S4 for participants included in Model 4). Abbreviations: GI, gastrointestinal; SU, sulfonylurea; TZD, thiazolidinedione. Joint model associations between HbA1c response and risk of side effects GI events With MFN, we found consistent evidence for an association between greater HbA1c response and reduced risk of a GI side effect (Figure 2A). We observed a similar association for moderate/severe GI events (20% of patients) and no association for severe GI events (3% of patients) (Table S3). We found no evidence of an association with TZDs and SUs (Tables 2 and S3). Figure 2 HRs for the association between HbA1c response and the risk of a drug-specific side effect (models 1–3). Notes: HRs (95% CI) represent the increase in the risk of side effect for a 1% greater absolute HbA1c response. A HR of greater than 1 indicates an increased risk of side effect with greater HbA1c response. Abbreviations: JMcum, joint model cumulative HbA1c; JMcv, joint model current value; LOCF, last observation carried forward; MFN, metformin; SU, sulfonylurea; TZD, thiazolidinedione. Table 2 HRs for the association between HbA1c response and risk of side effects (models 1–3) Notes: HRs (95% CI) represent the increase in risk of a side effect for a 1% greater absolute HbA1c response. A HR of greater than 1 indicates an increased risk of a side effect with greater HbA1c response. Abbreviations: GI, gastrointestinal; JMcum, joint model cumulative HbA1c; JMcv, joint model current value; LOCF, last observation carried forward; MFN, metformin; SU, sulfonylurea; TZD, thiazolidinedione. Hypoglycemia With SUs, we found that greater current HbA1c response was associated with an increased risk of hypoglycemia (Model 1: JMcv; Figure 2B). We found no evidence for an association between the risk of hypoglycemia and cumulative HbA1c response (Model 2: JMcum). With TZD therapy, although the absolute risk of hypoglycemia was much lower than with SU therapy (8 vs 26%), greater current and cumulative HbA1c responses were associated with an increased risk of hypoglycemia. There was no evidence of an association between response and hypoglycemia with MFN (Table 2). Edema With TZDs, greater current (Model 1: JMcv) and cumulative (Model 2: JMcum) HbA1c responses were associated with an increased risk of edema (Figure 2C). We found no evidence of an association between HbA1c response and the risk of edema with MFN and SUs (Table 2). Fracture With TZDs, we found no evidence for an association between HbA1c response and the risk of a fracture (Figure 2D). There was also no evidence of an association with MFN and SUs (Tables 2). Associations using standard time-to-event approaches Results using the LOCF approach (Model 3: LOCF) were generally consistent with those from the current value joint models (Model 1: JMcv) (Table 2 and Figure 2). The exception was for TZDs and edema, for which, in contrast to the joint model, we found no evidence of an association using the LOCF model. Using Model 4: 6mR (where HbA1c response was estimated from a single 6 month value), we found no evidence of any association between HbA1c response and the risk of side effects except for GI events with MFN (HR per 1% absolute increase in 6-month HbA1c response, 0.74 [95% CI 0.60, 0.91], Table S5). Discussion Our study shows that joint modeling can be a useful approach for evaluating associations between the benefits and risks of drug therapy. Using joint models for longitudinal and time-to-event data, we were able to show important differences in the associations between drug response and the risk of established side effects for three widely used type 2 diabetes drugs. We also found differences in the association between each of current and cumulative drug response and the risk of side-effects, suggesting underlying differences in the nature of associations for different drugs. Our results have implications for any precision medicine approach to type 2 diabetes therapy. More generally, they highlight the potential for the widespread application of joint longitudinal–survival modeling to evaluate the benefits and risks of both new and established medications. Advantages and disadvantages of joint models to evaluate the association between drug response and risk of side effects We found a key advantage of joint models to be their flexibility. Different specifications of the joint model gave important additional insight into the underlying nature of associations between HbA1c response and side effects. These insights fitted with what is known about the pharmacological action of the different drugs. Current, but not cumulative, HbA1c response was associated with an increased risk of hypoglycemia with SUs. This is expected as hypoglycemia is a side effect related to short-term fluctuations in blood glucose, rather than long-term exposure. In contrast, for edema with TZDs, which is less likely to relate to short-term fluctuations in blood glucose, we observed associations for both current and cumulative HbA1c responses. We also found associations with joint models that were missed by simpler approaches. With edema with TZD therapy, there was no association using the LOCF approach but a clear association using both specifications of the joint model. This is likely due to the reduced bias and increased efficiency of the joint model compared with the LOCF approach, which does not correct for measurement error in the longitudinal HbA1c response. 5 , 7 In general, HRs using the LOCF approach had the same direction of association but were attenuated compared with those obtained from the current value joint model, in keeping with previous comparisons. 4 , 17 We found that a single measure of HbA1c at 6 months was insufficient to show the evidence of an association between HbA1c response and side effects, with the exception of GI side effects with MFN where the association was consistent with the joint model. There are some settings where joint models may be more limited. ADOPT was a large randomized, double-blinded trial, and in this dataset, we found joint models to be useful to evaluate the association between response and relatively common side effects. Increasingly, similar trial datasets are available for researchers to address secondary research questions. 13 , 18 It may be more challenging to apply joint modeling in other datasets. In particular, the potential of recording bias should be considered if conducting similar studies in electronic health records, although greater sample size may offer the opportunity to study rarer side effects. Testing the specificity of results to drugs known to cause the side effect by comparison with "negative control" drugs may be a useful starting point. Joint models may also be harder to apply to study associations between drug response and acute or allergic side effects that occur immediately after starting therapy. This was apparent in our analysis, as although we included over 1,000 participants for each drug, participants who developed an early side effect prior to a first on-therapy HbA1c were excluded, and this is a particular limitation of our analysis of hypoglycemia with SUs. Another limitation of the joint modeling framework applied in this study is the assumption of a fixed association between longitudinal HbA1c and the risk of each side effect. While inspection of residual plots indicated that this was an appropriate strategy, it is certainly plausible that associations could change with therapy duration, and incorporating duration of therapy as a time-varying effect within the joint modeling framework would be of considerable interest. Similarly, an extension of the joint modeling framework to robustly incorporate drug dose could yield further insight to complement the response:side effect associations evaluated in this study. Evaluating the impact of dose is a particular challenge in trials of drug efficacy such as ADOPT, as participants could be both uptitrated based on reaching glycemic thresholds and downtitrated if a randomized medication was poorly tolerated. Implications for a precision medicine approach to type 2 diabetes therapy Our findings for the different drugs have implications for any future precision medicine approach to type 2 diabetes therapy. Greater MFN drug response was not associated with an increased risk of GI side effects, and this suggests great potential to target therapy if patients likely to have greater drug response can be robustly identified. 19 However, targeting SUs and TZDs to patients may be difficult as good responders are likely to be at increased risk of, respectively, hypoglycemia and edema. Our findings highlight the vital importance of considering both differential drug response and the risk of side effects in precision medicine studies, and this has been overlooked in previous work. 20 , 21 Our findings do not however preclude a precision medicine approach for SUs and TZDs. Identification of characteristics associated with either, but not both, improved drug response or lower risk of side effects may allow the targeting of these therapies. Furthermore, decisions on therapy should ultimately be informed by absolute rather than relative risks of benefit or harm. 1 For example, if patients likely to respond well to a TZD can be identified, then, a TZD may still be an appropriate option for patients whose absolute risk of developing a side effect is sufficiently low. Comparison with other studies To our knowledge, this is the first evaluation of the association between HbA1c response and the risk of side effects for any of the three drugs, except for hypoglycemia with SUs. Our results for SUs are consistent with previous observational studies that have examined the association between hypoglycemia and achieved on-therapy HbA1c (rather than HbA1c response). 22 , 23 In the ACCORD trial, participants with the greatest HbA1c response at 4 months had a reduced rather than increased risk of hypoglycemia, although this can be explained by the fact that, in ACCORD, the participants with least initial response were more likely to be on insulin, the therapy with by far the strongest association with hypoglycemia. 24 In this study, we found an unexpected association between greater response to TZD therapy and an increased risk of hypoglycemia, but no evidence of an association with MFN response, which would have indicated a positive association between increased drug response and increased risk of hypoglycemia was a more general characteristic of glucose-lowering therapy. This is an interesting finding for which there is no clear biological explanation, and it would be of interest to examine whether the association can be replicated in other datasets. The association between edema and HbA1c response with TZDs is not unexpected as the mechanisms underlying both glucose-lowering and fluid retention are thought to relate to Peroxisome proliferator-activated receptor gamma (PPAR-g) stimulation. 25 With MFN, there is no clear biological reason for the association between greater HbA1c response and a lower risk of GI events. One possible explanation is decreased drug adherence in patients experiencing mild GI symptoms prior to the event being recorded. Future work There is great potential to apply joint modeling to evaluate the association between drug response and the risk of side effects for the other drug options in type 2 diabetes and to study drug therapy in other diseases. Our findings also suggest a potential application of joint modeling as an efficient tool for understanding the risk–benefit trade-off at the individual level in drug development. 26 For precision medicine, the joint models used in this study could be extended to explore clinical features and biomarkers associated with drug response, the risk of side effects, or both. 27 , 28 Alternative model specifications, such as evaluation of the effect of HbA1c response slope, 6 the weighting of cumulative HbA1c effects by recency, 15 the incorporation of multiple longitudinal biomarkers, 29 and exploration of time-varying drug effects, may provide further insight into the nature of associations between response and side effects. Similarly, incorporation of robust dose adjustment within the joint modeling framework, for example, testing weighted cumulative drug associations, 30 , 31 could allow much greater understanding of the impact of different levels of drug exposure on both response and adverse events. Many of these are areas of current methodological development; a general mathematical presentation of joint modeling for simultaneously evaluating risks and benefits of medication would be a useful next step. Conclusion Joint modeling is a useful and efficient method to evaluate associations between continuous drug response and time to side effects. Our study suggests the potential for the application of joint modeling in both drug development and precision medicine research to evaluate the benefits and risks of medications. In type 2 diabetes, any future precision approach to SU and TZD therapy should consider the likely increased risk of, respectively, hypoglycemia and edema, if targeting these therapies at patients likely to have the greatest drug response. Abbreviations ADOPT, A Diabetes Outcome Progressing Trial; GSK, GlaxoSmithKline; MFN, metformin; SU, sulfonylurea; TZD, thiazolidinedione Data statement No additional data are available from the authors, although the individual participant data from the ADOPT trial used in this study are available from GlaxoSmithKline on application via www.clinicalstudydatarequest.com. Acknowledgment This work was supported by the Medical Research Council (UK) (Grant MR/N00633X/1). Author contributions JMD and WEH led the data analysis. All authors had full access to all of the data and take responsibility for the integrity of the data and the accuracy of the data analysis. WEH is the guarantor. All authors contributed to the study design, data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. Disclosure ATH is a NIHR Senior Investigator and a Wellcome Trust Senior Investigator. ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). AGJ is supported by an NIHR Clinician Scientist award. ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. WEH received additional support from IQVIA and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The funders had no role in any part of the study or in any decision about publication. WEH receives a grant from IQVIA, and ERP receives personal fees from Lily, Novo Nordisk, and Astra Zeneca. The authors report no other conflicts of interest in this work. References 1. Hingorani AD, Windt DA, Riley RD, et al; PROGRESS Group. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793. 2. Dahabreh IJ, Hayward R, Kent DM. Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence. Int J Epidemiol. 2016;45(6):2184–2193. 3. Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000;1(4):4 65–480. 4. Asar Ö, Ritchie J, Kalra PA, Diggle PJ. Joint modelling of repeated measurement and time-to-event data: an introductory tutorial. Int J Epidemiol. 2015;44(1):334–344. 5. Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol. 2010;28(16):2796–2801. 6. Rizopoulos D. Joint Models for Longitudinal and Time-To-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC; 2012. 7. Lawrence Gould A, Boye ME, Crowther MJ, et al. Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group. Stat Med. 2015;34(14):2181–2195. 8. Köhler M, Beyerlein A, Vehik K, et al; TEDDY study group. Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study. Acta Diabetol. 2017;54(11):1009–1017. 9. Fournier MC, Foucher Y, Blanche P, Buron F, Giral M, Dantan E. A joint model for longitudinal and time-to-event data to better assess the specific role of donor and recipient factors on long-term kidney transplantation outcomes. Eur J Epidemiol. 2016;31(5):469–479. 10. Andersson TM, Crowther MJ, Czene K, Hall P, Humphreys K. Mammographic density reduction as a prognostic marker for postmenopausal breast cancer: results using a joint longitudinal-survival modeling approach. Am J Epidemiol. 2017;186(9):1065–1073. 11. Tsiatis AA, Degruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error. applications to survival and CD4 counts in patients with AIDS. J Am Stat Assoc. 1995;90(429):27–37. 12. Kahn SE, Haffner SM, Heise MA, et al; ADOPT Study Group. Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N Engl J Med. 2006;355(23):2427–2443. 13. Clinical Study Data Request [homepage on the Internet]. Available from: https://clinicalstudydatarequest.com/. Accessed February 15, 2018. 14. Jones AG, Lonergan M, Henley WE, et al. Should studies of diabetes treatment stratification correct for baseline HbA1c? PLoS One. 2016;11(4):e0152428. 15. Mauff K, Steyerberg EW, Nijpels G, van der Heijden A, Rizopoulos D. Extension of the association structure in joint models to include weighted cumulative effects. Stat Med. 2017;36(23):3746–3759. 16. Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw. 2010;35(9):1–33. 17. Sweeting MJ, Thompson SG. Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture. Biom J. 2011;53(5):750–763. 18. The YODA Project [homepage on the Internet]. Available from: http://yoda.yale.edu/. Accessed February 15, 2018. 19. GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group, Wellcome Trust Case Control Consortium 2, Zhou K, et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet. 2011;43(2):117–120. 20. Dennis JM, Shields BM, Hill AV, et al; MASTERMIND Consortium. Precision medicine in type 2 diabetes: clinical markers of insulin resistance are associated with altered short- and long-term glycemic response to DPP-4 inhibitor therapy. Diabetes Care. 2018;41(4):705–712. 21. Jones AG, McDonald TJ, Shields BM, et al; PRIBA Study Group. Markers of β-cell failure predict poor glycemic response to GLP-1 receptor agonist therapy in type 2 diabetes. Diabetes Care. 2016;39(2):250–257. 22. Lipska KJ, Warton EM, Huang ES, et al. HbA1c and risk of severe hypoglycemia in type 2 diabetes: the Diabetes and Aging Study. Diabetes Care. 2013;36(11):3535–3542. 23. Yu S, Fu AZ, Engel SS, Shankar RR, Radican L. Association between hypoglycemia risk and hemoglobin A1C in patients with type 2 diabetes mellitus. Curr Med Res Opin. 2016;32(8):1409–1416. 24. Miller ME, Bonds DE, Gerstein HC, et al; ACCORD Investigators. The effects of baseline characteristics, glycaemia treatment approach, and glycated haemoglobin concentration on the risk of severe hypoglycaemia: post hoc epidemiological analysis of the ACCORD study. BMJ. 2010;340:b5444. 25. Davidson MA, Mattison DR, Azoulay L, Krewski D. Thiazolidinedione drugs in the treatment of type 2 diabetes mellitus: past, present and future. Crit Rev Toxicol. 2018;48(1):52–108. 26. Costa MJ, Drury T. Bayesian joint modelling of benefit and risk in drug development. Pharm Stat. 2018;17(3):248–263. 27. Hattersley AT, Patel KA. Precision diabetes: learning from monogenic diabetes. Diabetologia. 2017;60(5):769–777. 28. Dennis JM, Henley WE, Weedon MN, et al. Sex and BMI; MASTERMIND Consortium Alter the benefits and risks of sulfonylureas and thiazolidinediones in type 2 diabetes: a framework for evaluating stratification using routine clinical and individual trial data. Diabetes Care. 2018;41(9):1844–1853. 29. Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R. Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Med Res Methodol. 2016;16(1):117. 30. Abrahamowicz M, Beauchamp ME, Sylvestre MP. Comparison of alternative models for linking drug exposure with adverse effects. Stat Med. 2012;31(11–12):1014–1030. 31. Abrahamowicz M, Bartlett G, Tamblyn R, du Berger R. Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries. J Clin Epidemiol. 2006;59(4):393–403. [ABSTRACT FROM AUTHOR]

There is wide variation in the age at diagnosis of diabetes in individuals with maturity-onset diabetes of the young (MODY) due to a mutation in the HNF1A gene. We hypothesized that common variants at the HNF1A locus (rs1169288 [I27L], rs1800574 [A98V]), which are associated with type 2 diabetes susceptibility, may modify age at diabetes diagnosis in individuals with HNF1A-MODY. Meta-analysis of two independent cohorts, comprising 781 individuals with HNF1A-MODY, found no significant associations between genotype and age at diagnosis. However after stratifying according to type of mutation (protein-truncating variant [PTV] or missense), we found each 27L allele to be associated with a 1.6-year decrease (95% CI -2.6, -0.7) in age at diagnosis, specifically in the subset (n = 444) of individuals with a PTV. The effect size was similar and significant across the two independent cohorts of individuals with HNF1A-MODY. We report a robust genetic modifier of HNF1A-MODY age at diagnosis that further illustrates the strong effect of genetic variation within HNF1A upon diabetes phenotype. [ABSTRACT FROM AUTHOR]

Aims: To measure the variation in prescribing of second‐line non‐insulin diabetes drugs. Materials and Methods: We evaluated time trends for the period 1998 to 2016, using England's publicly available prescribing datasets, and stratified these by the order in which they were prescribed to patients using the Clinical Practice Research Datalink. We calculated the proportion of each class of diabetes drug as a percentage of the total per year. We evaluated geographical variation in prescribing using general practice‐level data for the latest 12 months (to August 2017), with aggregation to Clinical Commissioning Groups. We calculated percentiles and ranges, and plotted maps. Results: Prescribing of therapy after metformin is changing rapidly. Dipeptidyl peptidase‐4 (DPP‐4) inhibitor use has increased markedly, with DPP‐4 inhibitors now the most common second‐line drug (43% prescriptions in 2016). The use of sodium‐glucose co‐transporter‐2 (SGLT‐2) inhibitors also increased rapidly (14% new second‐line, 27% new third‐line prescriptions in 2016). There was wide geographical variation in choice of therapies and average spend per patient. In contrast, metformin was consistently used as a first‐line treatment in accordance with guidelines. Conclusions: In England there is extensive geographical variation in the prescribing of diabetes drugs after metformin, and increasing use of higher‐cost DPP‐4 inhibitors and SGLT‐2 inhibitors compared with low‐cost sulphonylureas. Our findings strongly support the case for comparative effectiveness trials of current diabetes drugs. [ABSTRACT FROM AUTHOR]

Objective: The choice of therapy for type 2 diabetes after metformin is guided by overall estimates of glycemic response and side effects seen in large cohorts. A stratified approach to therapy would aim to improve on this by identifying subgroups of patients whose glycemic response or risk of side effects differs markedly. We assessed whether simple clinical characteristics could identify patients with differing glycemic response and side effects with sulfonylureas and thiazolidinediones.Research Design and Methods: We studied 22,379 patients starting sulfonylurea or thiazolidinedione therapy in the U.K. Clinical Practice Research Datalink (CPRD) to identify features associated with increased 1-year HbA1c fall with one therapy class and reduced fall with the second. We then assessed whether prespecified patient subgroups defined by the differential clinical factors showed differing 5-year glycemic response and side effects with sulfonylureas and thiazolidinediones using individual randomized trial data from ADOPT (A Diabetes Outcome Progression Trial) (first-line therapy, n = 2,725) and RECORD (Rosiglitazone Evaluated for Cardiovascular Outcomes and Regulation of Glycemia in Diabetes) (second-line therapy, n = 2,222). Further replication was conducted using routine clinical data from GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) (n = 1,977).Results: In CPRD, male sex and lower BMI were associated with greater glycemic response with sulfonylureas and a lesser response with thiazolidinediones (both P < 0.001). In ADOPT and RECORD, nonobese males had a greater overall HbA1c reduction with sulfonylureas than with thiazolidinediones (P < 0.001); in contrast, obese females had a greater HbA1c reduction with thiazolidinediones than with sulfonylureas (P < 0.001). Weight gain and edema risk with thiazolidinediones were greatest in obese females; however, hypoglycemia risk with sulfonylureas was similar across all subgroups.Conclusions: Patient subgroups defined by sex and BMI have different patterns of benefits and risks on thiazolidinedione and sulfonylurea therapy. Subgroup-specific estimates can inform discussion about the choice of therapy after metformin for an individual patient. Our approach using routine and shared trial data provides a framework for future stratification research in type 2 diabetes. [ABSTRACT FROM AUTHOR]

Abstract: Background: Accurate diabetes prevalence estimates are important for health service planning and prioritisation. Available data have limitations, suggesting that the contemporary burden of diabetes in Australia is best assessed from multiple sources. Aims: To use systematic active detection of diabetes cases in a postcode‐defined urban area through the Fremantle Diabetes Study Phase II (FDS2) to complement other epidemiological and survey data in estimating the national prevalence of diabetes and its types. Methods: People with known diabetes in a population of 157 000 were identified (n = 4639) from a variety of sources and those providing informed consent (n = 1668 or 36%) were recruited to the FDS2 between 2008 and 2011. All FDS2 participants were assigned a type of diabetes based on clinical and laboratory (including serological and genetic) features. Data from people identified through the FDS2 were used to complement Australian Health Survey and National Diabetes Services Scheme prevalence estimates (the proportions of people well controlled on no pharmacotherapy and registering with the National Diabetes Services Scheme respectively) in combination with Australian Bureau of Statistics data to generate the prevalence of diabetes in Australia. Results: Based on data from multiple sources, 4.8% or 1.1 million Australians had diabetes in 2011–2012, of whom 85.8% had type 2 diabetes, 7.9% type 1 diabetes and 6.3% other types (latent autoimmune diabetes of adults, monogenic diabetes and secondary diabetes). Conclusions: Approximately 1 in 20 Australians has diabetes. Although most have type 2 diabetes, one in seven has other types that may require more specialised diagnosis and/or management. [ABSTRACT FROM AUTHOR]

Objective: A precision approach to type 2 diabetes therapy would aim to target treatment according to patient characteristics. We examined if measures of insulin resistance and secretion were associated with glycemic response to dipeptidyl peptidase 4 (DPP-4) inhibitor therapy.Research Design and Methods: We evaluated whether markers of insulin resistance and insulin secretion were associated with 6-month glycemic response in a prospective study of noninsulin-treated participants starting DPP-4 inhibitor therapy (Predicting Response to Incretin Based Agents [PRIBA] study; n = 254), with replication for routinely available markers in U.K. electronic health care records (Clinical Practice Research Datalink [CPRD]; n = 23,001). In CPRD, we evaluated associations between baseline markers and 3-year durability of response. To test the specificity of findings, we repeated analyses for glucagon-like peptide 1 (GLP-1) receptor agonists (PRIBA, n = 339; CPRD, n = 4,464).Results: In PRIBA, markers of higher insulin resistance (higher fasting C-peptide [P = 0.03], HOMA2 insulin resistance [P = 0.01], and triglycerides [P < 0.01]) were associated with reduced 6-month HbA1c response to DPP-4 inhibitors. In CPRD, higher triglycerides and BMI were associated with reduced HbA1c response (both P < 0.01). A subgroup defined by obesity (BMI ≥30 kg/m2) and high triglycerides (≥2.3 mmol/L) had reduced 6-month response in both data sets (PRIBA HbA1c reduction 5.3 [95% CI 1.8, 8.6] mmol/mol [0.5%] [obese and high triglycerides] vs. 11.3 [8.4, 14.1] mmol/mol [1.0%] [nonobese and normal triglycerides]; P = 0.01). In CPRD, the obese, high- triglycerides subgroup also had less durable response (hazard ratio 1.28 [1.16, 1.41]; P < 0.001). There was no association between markers of insulin resistance and response to GLP-1 receptor agonists.Conclusions: Markers of higher insulin resistance are consistently associated with reduced glycemic response to DPP-4 inhibitors. This finding provides a starting point for the application of a precision diabetes approach to DPP-4 inhibitor therapy. [ABSTRACT FROM AUTHOR]

Objective: Monogenic diabetes, a young-onset form of diabetes, is often misdiagnosed as type 1 diabetes, resulting in unnecessary treatment with insulin. A screening approach for monogenic diabetes is needed to accurately select suitable patients for expensive diagnostic genetic testing. We used C-peptide and islet autoantibodies, highly sensitive and specific biomarkers for discriminating type 1 from non-type 1 diabetes, in a biomarker screening pathway for monogenic diabetes.Research Design and Methods: We studied patients diagnosed at age 30 years or younger, currently younger than 50 years, in two U.K. regions with existing high detection of monogenic diabetes. The biomarker screening pathway comprised three stages: 1) assessment of endogenous insulin secretion using urinary C-peptide/creatinine ratio (UCPCR); 2) if UCPCR was ≥0.2 nmol/mmol, measurement of GAD and IA2 islet autoantibodies; and 3) if negative for both autoantibodies, molecular genetic diagnostic testing for 35 monogenic diabetes subtypes.Results: A total of 1,407 patients participated (1,365 with no known genetic cause, 34 with monogenic diabetes, and 8 with cystic fibrosis-related diabetes). A total of 386 out of 1,365 (28%) patients had a UCPCR ≥0.2 nmol/mmol, and 216 out of 386 (56%) were negative for GAD and IA2 and underwent molecular genetic testing. Seventeen new cases of monogenic diabetes were diagnosed (8 common Maturity Onset Diabetes of the Young [Sanger sequencing] and 9 rarer causes [next-generation sequencing]) in addition to the 34 known cases (estimated prevalence of 3.6% [51/1,407] [95% CI 2.7-4.7%]). The positive predictive value was 20%, suggesting a 1-in-5 detection rate for the pathway. The negative predictive value was 99.9%.Conclusions: The biomarker screening pathway for monogenic diabetes is an effective, cheap, and easily implemented approach to systematically screening all young-onset patients. The minimum prevalence of monogenic diabetes is 3.6% of patients diagnosed at age 30 years or younger. [ABSTRACT FROM AUTHOR]

Objective: Monogenic diabetes is rare but is an important diagnosis in pediatric diabetes clinics. These patients are often not identified as this relies on the recognition of key clinical features by an alert clinician. Biomarkers (islet autoantibodies and C-peptide) can assist in the exclusion of patients with type 1 diabetes and allow systematic testing that does not rely on clinical recognition. Our study aimed to establish the prevalence of monogenic diabetes in U.K. pediatric clinics using a systematic approach of biomarker screening and targeted genetic testing.Research Design and Methods: We studied 808 patients (79.5% of the eligible population) <20 years of age with diabetes who were attending six pediatric clinics in South West England and Tayside, Scotland. Endogenous insulin production was measured using the urinary C-peptide creatinine ratio (UCPCR). C-peptide-positive patients (UCPCR ≥0.2 nmol/mmol) underwent islet autoantibody (GAD and IA2) testing, with patients who were autoantibody negative undergoing genetic testing for all 29 identified causes of monogenic diabetes.Results: A total of 2.5% of patients (20 of 808 patients) (95% CI 1.6-3.9%) had monogenic diabetes (8 GCK, 5 HNF1A, 4 HNF4A, 1 HNF1B, 1 ABCC8, 1 INSR). The majority (17 of 20 patients) were managed without insulin treatment. A similar proportion of the population had type 2 diabetes (3.3%, 27 of 808 patients).Conclusions: This large systematic study confirms a prevalence of 2.5% of patients with monogenic diabetes who were <20 years of age in six U.K. clinics. This figure suggests that ∼50% of the estimated 875 U.K. pediatric patients with monogenic diabetes have still not received a genetic diagnosis. This biomarker screening pathway is a practical approach that can be used to identify pediatric patients who are most appropriate for genetic testing. [ABSTRACT FROM AUTHOR]

HNF4A mutations cause increased birth weight, transient neonatal hypoglycemia, and maturity onset diabetes of the young (MODY). The most frequently reported HNF4A mutation is p.R114W (previously p.R127W), but functional studies have shown inconsistent results; there is a lack of cosegregation in some pedigrees and an unexpectedly high frequency in public variant databases. We confirm that p.R114W is a pathogenic mutation with an odds ratio of 30.4 (95% CI 9.79-125, P = 2 × 10(-21)) for diabetes in our MODY cohort compared with control subjects. p.R114W heterozygotes did not have the increased birth weight of patients with other HNF4A mutations (3,476 g vs. 4,147 g, P = 0.0004), and fewer patients responded to sulfonylurea treatment (48% vs. 73%, P = 0.038). p.R114W has reduced penetrance; only 54% of heterozygotes developed diabetes by age 30 years compared with 71% for other HNF4A mutations. We redefine p.R114W as a pathogenic mutation that causes a distinct clinical subtype of HNF4A MODY with reduced penetrance, reduced sensitivity to sulfonylurea treatment, and no effect on birth weight. This has implications for diabetes treatment, management of pregnancy, and predictive testing of at-risk relatives. The increasing availability of large-scale sequence data is likely to reveal similar examples of rare, low-penetrance MODY mutations. [ABSTRACT FROM AUTHOR]

Maturity onset diabetes of the young ( MODY) is a monogenic form of diabetes caused by a mutation in a single gene, often not requiring insulin. The aim of this study was to estimate the frequency and clinical characteristics of MODY at the Barbara Davis Center. A total of 97 subjects with diabetes onset before age 25, a random C-peptide ≥0.1 ng/ mL, and negative for all diabetes autoantibodies ( GADA, IA-2, ZnT8, and IAA) were enrolled, after excluding 21 subjects with secondary diabetes or refusal to participate. Genetic testing for MODY 1-5 was performed through Athena Diagnostics, and all variants of unknown significance were further analyzed at Exeter, UK. A total of 22 subjects [20 (21%) when excluding two siblings] were found to have a mutation in hepatocyte nuclear factor 4A (n = 4), glucokinase (n = 8), or hepatocyte nuclear factor 1A (n = 10). Of these 22 subjects, 13 had mutations known to be pathogenic and 9 (41%) had novel mutations, predicted to be pathogenic. Only 1 of the 22 subjects had been given the appropriate MODY diagnosis prior to testing. Compared with MODY-negative subjects, the MODY-positive subjects had lower hemoglobin A1c level and no diabetic ketoacidosis at onset; however, these characteristics are not specific for MODY. In summary, this study found a high frequency of MODY mutations with the majority of subjects clinically misdiagnosed. Clinicians should have a high index of suspicion for MODY in youth with antibody-negative diabetes. [ABSTRACT FROM AUTHOR]

Distinguishing patients with monogenic diabetes from those with type 1 diabetes (T1D) is important for correct diagnosis, treatment, and selection of patients for gene discovery studies. We assessed whether a T1D genetic risk score (T1D-GRS) generated from T1D-associated common genetic variants provides a novel way to discriminate monogenic diabetes from T1D. The T1D-GRS was highly discriminative of proven maturity-onset diabetes of young (MODY) (n = 805) and T1D (n = 1,963) (receiver operating characteristic area under the curve 0.87). A T1D-GRS of >0.280 (>50th T1D centile) was indicative of T1D (94% specificity, 50% sensitivity). We then analyzed the T1D-GRS of 242 white European patients with neonatal diabetes (NDM) who had been tested for all known NDM genes. Monogenic NDM was confirmed in 90, 59, and 8% of patients with GRS <5th T1D centile, 50-75th T1D centile, and >75th T1D centile, respectively. Applying a GRS 50th T1D centile cutoff in 48 NDM patients with no known genetic cause identified those most likely to have a novel monogenic etiology by highlighting patients with probable early-onset T1D (GRS >50th T1D centile) who were diagnosed later and had less syndromic presentation but additional autoimmune features compared with those with proven monogenic NDM. The T1D-GRS is a novel tool to improve the use of biomarkers in the discrimination of monogenic diabetes from T1D. [ABSTRACT FROM AUTHOR]

Background: Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management of patients, but can be challenging, especially when treating with insulin. The 2010 UK Practical Classification Guidelines for Diabetes were developed to help make the differentiation.Aim: To assess diagnostic accuracy of the UK guidelines against 'gold standard' definitions of type 1 and type 2 diabetes based on measured C-peptide levels.Design and Setting: In total, 601 adults with insulin-treated diabetes and diabetes duration ≥5 years were recruited in Devon, Northamptonshire, and Leicestershire.Method: Baseline information and home urine sample were collected. Urinary C-peptide creatinine ratio (UCPCR) measures endogenous insulin production. Gold standard type 1 diabetes was defined as continuous insulin treatment within 3 years of diagnosis and absolute insulin deficiency (UCPCR<0.2 nmol/mmol ≥5 years post-diagnosis); all others classed as having type 2 diabetes. Diagnostic performance of the clinical criteria was assessed and other criteria explored using receiver operating characteristic (ROC) curves.Results: UK guidelines correctly classified 86% of participants. Most misclassifications occurred in patients classed as having type 1 diabetes who had significant endogenous insulin levels (57 out of 601; 9%); most in those diagnosed ≥35 years and treated with insulin from diagnosis, where 37 out of 66 (56%) were misclassified. Time to insulin and age at diagnosis performed best in predicting long-term endogenous insulin production (ROC AUC = 0.904 and 0.871); BMI was a less strong predictor of diabetes type (AUC = 0.824).Conclusion: Current UK guidelines provide a pragmatic clinical approach to classification reflecting long-term endogenous insulin production; caution is needed in older patients commencing insulin from diagnosis, where misclassification rates are increased. [ABSTRACT FROM AUTHOR]

Aims: Baseline HbA1c is a major predictor of response to glucose lowering therapy and therefore a potential confounder in studies aiming to identify other predictors. However, baseline adjustment may introduce error if the association between baseline HbA1c and response is substantially due to measurement error and regression to the mean. We aimed to determine whether studies of predictors of response should adjust for baseline HbA1c. Methods: We assessed the relationship between baseline HbA1c and glycaemic response in 257 participants treated with GLP-1R agonists and assessed whether it reflected measurement error and regression to the mean using duplicate ‘pre-baseline’ HbA1c measurements not included in the response variable. In this cohort and an additional 2659 participants treated with sulfonylureas we assessed the relationship between covariates associated with baseline HbA1c and treatment response with and without baseline adjustment, and with a bias correction using pre-baseline HbA1c to adjust for the effects of error in baseline HbA1c. Results: Baseline HbA1c was a major predictor of response (R2 = 0.19,β = -0.44,p<0.001).The association between pre-baseline and response was similar suggesting the greater response at higher baseline HbA1cs is not mainly due to measurement error and subsequent regression to the mean. In unadjusted analysis in both cohorts, factors associated with baseline HbA1c were associated with response, however these associations were weak or absent after adjustment for baseline HbA1c. Bias correction did not substantially alter associations. Conclusions: Adjustment for the baseline HbA1c measurement is a simple and effective way to reduce bias in studies of predictors of response to glucose lowering therapy. [ABSTRACT FROM AUTHOR]

Objective: With rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes.Research Design and Methods: We developed genetic risk scores (GRSs) from published T1D- and T2D-associated variants. We first tested whether the scores could distinguish clinically defined T1D and T2D from the Wellcome Trust Case Control Consortium (WTCCC) (n = 3,887). We then assessed whether the T1D GRS correctly classified young adults (diagnosed at 20-40 years of age, the age-group with the most diagnostic difficulty in clinical practice; n = 223) who progressed to severe insulin deficiency <3 years from diagnosis.Results: In the WTCCC, the T1D GRS, based on 30 T1D-associated risk variants, was highly discriminative of T1D and T2D (area under the curve [AUC] 0.88 [95% CI 0.87-0.89]; P < 0.0001), and the T2D GRS added little discrimination (AUC 0.89). A T1D GRS >0.280 (>50th centile in those with T1D) is indicative of T1D (50% sensitivity, 95% specificity). A low T1D GRS (<0.234, <5th centile T1D) is indicative of T2D (53% sensitivity, 95% specificity). Most discriminative ability was obtained from just nine single nucleotide polymorphisms (AUC 0.87). In young adults with diabetes, T1D GRS alone predicted progression to insulin deficiency (AUC 0.87 [95% CI 0.82-0.92]; P < 0.0001). T1D GRS, autoantibody status, and clinical features were independent and additive predictors of severe insulin deficiency (combined AUC 0.96 [95% CI 0.94-0.99]; P < 0.0001).Conclusions: A T1D GRS can accurately identify young adults with diabetes who will require insulin treatment. This will be an important addition to correctly classifying individuals with diabetes when clinical features and autoimmune markers are equivocal. [ABSTRACT FROM AUTHOR]

Objective: The impact of taking oral glucose-lowering medicines intermittently, rather than as recommended, is unclear. We conducted a retrospective cohort study using community-acquired U.K. clinical data (Clinical Practice Research Database [CPRD] and GoDARTS database) to examine the prevalence of nonadherence to treatment for type 2 diabetes and investigate its potential impact on HbA1c reduction stratified by type of glucose-lowering medication.Research Design and Methods: Data were extracted for patients treated between 2004 and 2014 who were newly prescribed metformin, sulfonylurea, thiazolidinedione, or dipeptidyl peptidase 4 inhibitors and who continued to obtain prescriptions over 1 year. Cohorts were defined by prescribed medication type, and good adherence was defined as a medication possession ratio ≥0.8. Linear regression was used to determine potential associations between adherence and 1-year baseline-adjusted HbA1c reduction.Results: In CPRD and GoDARTS, 13% and 15% of patients, respectively, were nonadherent. Proportions of nonadherent patients varied by the oral glucose-lowering treatment prescribed (range 8.6% [thiazolidinedione] to 18.8% [metformin]). Nonadherent, compared with adherent, patients had a smaller HbA1c reduction (0.4% [4.4 mmol/mol] and 0.46% [5.0 mmol/mol] for CPRD and GoDARTs, respectively). Difference in HbA1c response for adherent compared with nonadherent patients varied by drug (range 0.38% [4.1 mmol/mol] to 0.75% [8.2 mmol/mol] lower in adherent group). Decreasing levels of adherence were consistently associated with a smaller reduction in HbA1c.Conclusions: Reduced medication adherence for commonly used glucose-lowering therapies among patients persisting with treatment is associated with smaller HbA1c reductions compared with those taking treatment as recommended. Differences observed in HbA1c responses to glucose-lowering treatments may be explained in part by their intermittent use. [ABSTRACT FROM AUTHOR]

Objective: To assess whether clinical characteristics and simple biomarkers of β-cell failure are associated with individual variation in glycemic response to GLP-1 receptor agonist (GLP-1RA) therapy in patients with type 2 diabetes.Research Design and Methods: We prospectively studied 620 participants with type 2 diabetes and HbA1c ≥58 mmol/mol (7.5%) commencing GLP-1RA therapy as part of their usual diabetes care and assessed response to therapy over 6 months. We assessed the association between baseline clinical measurements associated with β-cell failure and glycemic response (primary outcome HbA1c change 0-6 months) with change in weight (0-6 months) as a secondary outcome using linear regression and ANOVA with adjustment for baseline HbA1c and cotreatment change.Results: Reduced glycemic response to GLP-1RAs was associated with longer duration of diabetes, insulin cotreatment, lower fasting C-peptide, lower postmeal urine C-peptide-to-creatinine ratio, and positive GAD or IA2 islet autoantibodies (P ≤ 0.01 for all). Participants with positive autoantibodies or severe insulin deficiency (fasting C-peptide ≤0.25 nmol/L) had markedly reduced glycemic response to GLP-1RA therapy (autoantibodies, mean HbA1c change -5.2 vs. -15.2 mmol/mol [-0.5 vs. -1.4%], P = 0.005; C-peptide <0.25 nmol/L, mean change -2.1 vs. -15.3 mmol/mol [-0.2 vs. -1.4%], P = 0.002). These markers were predominantly present in insulin-treated participants and were not associated with weight change.Conclusions: Clinical markers of low β-cell function are associated with reduced glycemic response to GLP-1RA therapy. C-peptide and islet autoantibodies represent potential biomarkers for the stratification of GLP-1RA therapy in insulin-treated diabetes. [ABSTRACT FROM AUTHOR]

Aims: Defining responders to glucose lowering therapy can be important for both clinical care and for the development of a stratified approach to diabetes management. Response is commonly defined by either HbA1c change after treatment or whether a target HbA1c is achieved. We aimed to determine the extent to which the individuals identified as responders and non-responders to glucose lowering therapy, and their characteristics, depend on the response definition chosen. Methods: We prospectively studied 230 participants commencing GLP-1 agonist therapy. We assessed participant characteristics at baseline and repeated HbA1c after 3 months treatment. We defined responders (best quartile of response) based on HbA1c change or HbA1c achieved. We assessed the extent to which these methods identified the same individuals and how this affected the baseline characteristics associated with treatment response. Results: Different definitions of response identified different participants. Only 39% of responders by one definition were also good responders by the other. Characteristics associated with good response depend on the response definition chosen: good response by HbA1c achieved was associated with low baseline HbA1c (p<0.001), high C-peptide (p<0.001) and shorter diabetes duration (p = 0.01) whereas response defined by HbA1c change was associated with high HbA1c (p<0.001) only. We describe a simple novel method of defining treatment response based on a combination of HbA1c change and HbA1c achieved that defines response groups with similar baseline glycaemia. Conclusions: The outcome of studies aiming to identify predictors of treatment response to glucose lowering therapy may depend on how response is defined. Alternative definitions of response should be considered which minimise influence of baseline glycaemia. [ABSTRACT FROM AUTHOR]

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

IMPORTANCE Glycemic targets in diabetes have been developed to minimize complication risk. Patients with heterozygous, inactivating glucokinase (GCK) mutations have mild fasting hyperglycemia from birth, resulting in an elevated glycated hemoglobin (HbAlc) level that mimics recommended levels for type 1 and type 2 diabetes. OBJECTIVE To assess the association between chronic, mild hyperglycemia and complication prevalence and severity in patients with GCK mutations. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study in the United Kingdom between August 2008 and December 2010. Assessment of microvascular and macrovascular complications in participants 35 years or older was conducted in 99 GCK mutation carriers (median age, 48.6 years), 91 nondiabetic, familial, nonmutation carriers (control) (median age, 52.2 years), and 83 individuals with young-onset type 2 diabetes (YT2D), diagnosed at age 45 years or younger (median age, 54.7 years). MAIN OUTCOMES AND MEASURES Prevalence and severity of nephropathy, retinopathy, peripheral neuropathy, peripheral vascular disease, and cardiovascular disease. RESULTS Median HbAlc was 6.9% in patients with the GCK mutation, 5.8% in controls, and 7.8% in patients with YT2D. Patients with GCK had a low prevalence of clinically significant microvascular complications (1% [95% Cl, 0%-5%]) that was not significantly different from controls (2% [95% Cl, 0.3%-8%], P=.52) and lower than in patients with YT2D (36% [95% Cl, 25%-47%], P<.001). Thirty percent of patients with GCK had retinopathy (95% Cl, 21%-41%) compared with 14% of controls (95% Cl. 7%-23%, P=.007) and 63% of patients with YT2D (95% Cl, 51%-73%, P<.001). Neither patients with GCK nor controls required laser therapy for retinopathy compared with 28% (95% Cl, 18%-39%) of patients with YT2D (P<.001). Neither patients with GCK patients nor controls had proteinuria and microalbuminuria was rare (GCK, 1% [95% Cl, 0.2%-6%]; controls, 2% [95% Cl, 0.2%-8%]), whereas 10% (95% Cl, 4%-19%) of YT2D patients had proteinuria (P<.001 vs G00 and 21% (95% Cl, 13%-32%) had microalbuminuria (P<.001). Neuropathy was rare in patients with GCK (2% [95% Cl, 0.3%-8%]) and controls (95% Cl, 0% [0%-4%]) but present in 29% (95% Cl, 20%-50%) of YT2D patients (P<.001). Patients with GCK had a low prevalence of clinically significant macrovascular complications (4% [95% Cl, 1%-10%]) that was not significantly different from controls (11% [95% Cl, 5%-19%]; P=.09), and lower in prevalence than patients with YT2D (30% [95% Cl. 21%-41%], P<.001). CONCLUSIONS AND RELEVANCE Despite a median duration of 48.6 years of hyperglycemia, patients with a GCK mutation had low prevalence of microvascular and macrovascular complications. These findings may provide insights into the risks associated with isolated, mild hyperglycemia. [ABSTRACT FROM AUTHOR]

Background The UK Biobank study provides a unique opportunity to study the causes and consequences of disease. We aimed to use the UK Biobank data to study the well-established, but poorly understood, association between low birthweight and type 2 diabetes.Methods We used logistic regression to calculate the odds ratio for participants’ risk of type 2 diabetes given a one standard deviation increase in birthweight. To test for an association between parental diabetes and birthweight, we performed linear regression of self-reported parental diabetes status against birthweight. We performed path and mediation analyses to test the hypothesis that birthweight partly mediates the association between parental diabetes and participant type 2 diabetes status.Results Of the UK Biobank participants, 277 261 reported their birthweight. Of 257 715 individuals of White ethnicity and singleton pregnancies, 6576 had type 2 diabetes, 19 478 reported maternal diabetes (but not paternal), 20 057 reported paternal diabetes (but not maternal) and 2754 participants reported both parents as having diabetes. Lower birthweight was associated with type 2 diabetes in the UK Biobank participants. A one kilogram increase in birthweight was associated with a lower risk of type 2 diabetes (odds ratio: 0.74; 95% CI: 0.71, 0.76; P = 2 × 10−57). Paternal diabetes was associated with lower birthweight (45 g lower; 95% CI: 36, 54; P = 2 × 10−23) relative to individuals with no parental diabetes. Maternal diabetes was associated with higher birthweight (59 g increase; 95% CI: 50, 68; P = 3 × 10−37). Participants’ lower birthweight was a mediator of the association between reported paternal diabetes and participants’ type 2 diabetes status, explaining 1.1% of the association, and participants’ higher birthweight was a mediator of the association between reported maternal diabetes and participants’ type 2 diabetes status, explaining 1.2% of the association.Conclusions Data from the UK Biobank provides the strongest evidence by far that paternal diabetes is associated with lower birthweight, whereas maternal diabetes is associated with increased birthweight. Our findings with paternal diabetes are consistent with a role for the same genetic factors influencing foetal growth and type 2 diabetes. [ABSTRACT FROM PUBLISHER]

OBJECTIVE--The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance. RESEARCH DESIGN AND METHODS--We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies. RESULTS--Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52-0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97-1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI -0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [-0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45-0.83]; P = 0.002). CONCLUSIONS--Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal. [ABSTRACT FROM AUTHOR]

In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals ( P-value = 5 × 10). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings. [ABSTRACT FROM AUTHOR]

Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10-4, permutation p = 1.0×10-3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10-7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases. [ABSTRACT FROM AUTHOR]

OBJECTIVE--Wolfram syndrome 1 (WFS1) single nucleotide polymorphisms (SNPs) are associated with risk of type 2 diabetes. In this study we aimed to refine this association and investigate the role of low-frequency WFS1 variants in type 2 diabetes risk. RESEARCH DESIGN AND METHODS--For fine-mapping, we sequenced WFS1 exons, splice junctions, and conserved noncoding sequences in samples from 24 type 2 diabetic case and 68 control subjects, selected tagging SNPs, and genotyped these in 959 U.K. type 2 diabetic case and 1,386 control subjects. The same genomic regions were sequenced in samples from 1,235 type 2 diabetic case and 1,668 control subjects to compare the frequency of rarer variants between case and control subjects. RESULTS--Of 31 tagging SNPs, the strongest associated was the previously untested 3′ untranslated region rs1046320 (P = 0.008); odds ratio 0.84 and P = 6.59 x 10[sup -7] on further replication in 3,753 case and 4,198 control subjects. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r[sup 2] = 0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (minor allele frequency [MAF] <0.01) nonsynonymous variants between type 2 diabetic case and control subjects (P = 0.79). Two intermediate frequency (MAF 0.01-0.05) nonsynonymous changes also showed no statistical association with type 2 diabetes. CONCLUSIONS--We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing. Diabetes 59:741-746, 2010 [ABSTRACT FROM AUTHOR]