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Real-world prescribing of drugs differs from the experimental systems, physiological-pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug-gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug-gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior-drug-stop or dose-decrease. Using this approach, we replicate 11 known drug-gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C-allele at rs4918758 in CYP2C9 was associated with a 25% (15-44%) lower odds of dose reduction of quinine, P = 1.6 × 10-5 ; the A-allele at rs9895420 in ABCC3 was associated with a 46% (24-62%) reduction in odds of dose reduction with doxazosin, P = 1.2 × 10-4 , and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18-40%) reduction in odds of stopping ramipril treatment, P = 1.01 × 10-5 , with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug-gene interactions at scale.

Aims/Hypothesis Studies in permanent neonatal diabetes suggest that sulphonylureas lower blood glucose without causing hypoglycemia, in part by augmenting the incretin effect. This mechanism has not previously been attributed to sulphonylureas in patients with type 2 diabetes (T2DM). We therefore aimed to evaluate the impact of low-dose gliclazide on beta-cell function and incretin action in patients with T2DM. Methods Paired oral glucose tolerance tests and isoglycemic infusions were performed to evaluate the difference in the classical incretin effect in the presence and absence of low-dose gliclazide in 16 subjects with T2DM (hemoglobin A1c Results A single dose of 20 mg gliclazide reduced mean glucose during the oral glucose tolerance test from 12.01 ± 0.56 to 10.82 ± 0.5mmol/l [P = 0.0006; mean ± standard error of the mean (SEM)]. The classical incretin effect was augmented by 20 mg gliclazide, from 35.5% (lower quartile 27.3, upper quartile 61.2) to 54.99% (34.8, 72.8; P = 0.049). Gliclazide increased beta-cell glucose sensitivity by 46% [control 22.61 ± 3.94, gliclazide 33.11 ± 7.83 (P = 0.01)] as well as late-phase incretin potentiation [control 0.92 ± 0.05, gliclazide 1.285 ± 0.14 (P = 0.038)]. Conclusions/Interpretation Low-dose gliclazide reduces plasma glucose in response to oral glucose load, with concomitant augmentation of the classical incretin effect. Beta-cell modeling shows that low plasma concentrations of gliclazide potentiate late-phase insulin secretion and increase glucose sensitivity by 50%. Further studies are merited to explore whether low-dose gliclazide, by enhancing incretin action, could effectively lower blood glucose without risk of hypoglycemia.

Individualization of therapy based on a person’s specific type of diabetes is one key element of a “precision medicine” approach to diabetes care. However, applying such an approach remains difficult because of barriers such as disease heterogeneity, difficulties in accurately diagnosing different types of diabetes, multiple genetic influences, incomplete understanding of pathophysiology, limitations of current therapies, and environmental, social, and psychological factors. Monogenic diabetes, for which single gene mutations are causal, is the category most suited to a precision approach. The pathophysiological mechanisms of monogenic diabetes are understood better than those of any other form of diabetes. Thus, this category offers the advantage of accurate diagnosis of nonoverlapping etiological subgroups for which specific interventions can be applied. Although representing a small proportion of all diabetes cases, monogenic forms present an opportunity to demonstrate the feasibility of precision medicine strategies. In June 2019, the editors of Diabetes Care convened a panel of experts to discuss this opportunity. This article summarizes the major themes that arose at that forum. It presents an overview of the common causes of monogenic diabetes, describes some challenges in identifying and treating these disorders, and reports experience with various approaches to screening, diagnosis, and management. This article complements a larger American Diabetes Association effort supporting implementation of precision medicine for monogenic diabetes, which could serve as a platform for a broader initiative to apply more precise tactics to treating the more common forms of diabetes.

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Gastrointestinal fistulation has been widely reported as an adverse effect of nicorandil therapy in Europe. People who have underlying diverticular disease are most at risk of this side effect. In Western countries, diverticular disease is highly prevalent and can be clinically silent. This study aimed to identify diverticular disease genetic risk scores (GRSs) associated with early nicorandil stoppage, a surrogate marker for drug intolerance. A case-control study was carried out on 1,077 patients from the Genetics of Diabetes Audit and Research Tayside Scotland (GoDARTS) database. Cases were defined as having < 9 nicorandil prescriptions with no identifiable reason for stopping (n = 230). Controls had either ≥ 9 prescriptions, treatment continuation to death/study end or stoppage post-myocardial infarction. Two diverticular GRSs were created and used in logistic regression models. Isosorbide mononitrate was used as a control analysis. Patients with a raised diverticular GRS, based on 23 replicable loci, had increased risk of stopping nicorandil therapy early (univariate (odds ratio (OR) 2.26; P = 0.04], multivariate (OR 3.96; P = 0.01)). Similar trends were noted when using the full 42 variant diverticular score but statistical significance was not reached. The isosorbide control analysis did not reach statistical significance. Our analysis demonstrates a novel positive association between a raised diverticular GRS and early stoppage of nicorandil therapy.

OBJECTIVE To determine the effects of dapagliflozin in patients with heart failure (HF) and type 2 diabetes mellitus (T2DM) on left ventricular (LV) remodeling using cardiac MRI. RESEARCH DESIGN AND METHODS We randomized 56 patients with T2DM and HF with LV systolic dysfunction to dapagliflozin 10 mg daily or placebo for 1 year, on top of usual therapy. The primary end point was difference in LV end-systolic volume (LVESV) using cardiac MRI. Key secondary end points included other measures of LV remodeling and clinical and biochemical parameters. RESULTS In our cohort, dapagliflozin had no effect on LVESV or any other parameter of LV remodeling. However, it reduced diastolic blood pressure and loop diuretic requirements while increasing hemoglobin, hematocrit, and ketone bodies. There was a trend toward lower weight. CONCLUSIONS We were unable to determine with certainty whether dapagliflozin in patients with T2DM and HF had any effect on LV remodeling. Whether the benefits of dapagliflozin in HF are due to remodeling or other mechanisms remains unknown.

Aims/hypothesis It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.

Aim To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF+PS+MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF+PS+MPs and PS+MPs (TF+PS+MPs/PS+MPs). Results We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS+MPs, TF+PS+MPs and TF+PS+MPs/PS+MPs were elevated in patients with T1DM (PS+MPs: 1078.5 ± 158.08 vs 686.84 ± 122.04/μL, P

ObjectiveTo establish whether clinical patient characteristics routinely measured in primary care can identify people with differing short-term benefits and risks for SGLT2-inhibitor and DPP4-inhibitor therapies, and to derive and validate a treatment selection algorithm to identify the likely optimal therapy for individual patients.DesignProspective cohort study.SettingRoutine clinical data from United Kingdom general practice (Clinical Practice Research Datalink [CPRD]), and individual-level clinical trial data from 14 multi-country trials of SGLT2-inhibitor and DPP4-inhibitor therapies.Participants26,877 new users of SGLT2-inhibitor and DPP4-inhibitor therapy in CPRD over 2013-2019, and 10,414 participants randomised to SGLT2-inhibitor or DPP4-inhibitor therapy in 14 clinical trials, including 3 head-to-head trials of the two therapies (n=2,499).Main outcome measuresThe primary outcome was achieved HbA1c 6 months after initiating therapy. Clinical features associated with differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies were identified in routine clinical data, with associations then tested in trial data. A multivariable treatment selection algorithm to predict differential HbA1c outcomes was developed in a CPRD derivation cohort (n=14,069), with validation in a CPRD validation cohort (n=9,376) and the head-to-head trials. In CPRD, we further explored the relationship between model predictions and secondary outcomes of weight loss and treatment discontinuation.ResultsThe final treatment selection algorithm included HbA1c, eGFR, ALT, age, and BMI, which were identified as predictors of differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies using both routine and trial data. In validation cohorts, patient strata predicted to have a ≥5 mmol/mol HbA1c reduction with SGLT2-inhibitor therapy compared with DPP4-inhibitor therapy (38.8% of CPRD validation sample) had an observed greater reduction of 8.8 mmol/mol [95%CI 7.8-9.8] in the CPRD validation sample, a 5.8 mmol/mol (95%CI 3.9-7.7) greater reduction in the Cantata D/D2 trials, and a 6.6 mmol/mol [95%CI 2.2-11.0]) greater reduction in the BI1245.20 trial. In CPRD, there was a greater weight reduction with SGLT2-inhibitor therapy regardless of predicted glycaemic benefit. Strata predicted to have greater reduction in HbA1c on SGLT2-inhibitor therapy had a similar risk of discontinuation as on DPP4-inhibitor therapy. In contrast, strata predicted to have greater reduction in HbA1c with DPP4-inhibitor therapy were half as likely to discontinue DPP4-inhibitor therapy than SGLT2-inhibitor therapy.ConclusionsRoutinely measured clinical features are robustly associated with differential glycaemic responses to SGLT2-inhibitor and DPP4-inhibitor therapies. Combining features into a treatment selection algorithm can inform clinical decisions concerning optimal type 2 diabetes treatment choices.Key messagesWhat is already known on this subjectDespite there being multiple glucose-lowering treatment options available for people with type 2 diabetes, current guidelines do not provide clear advice on selecting the optimal treatment for most patients.It is unknown whether routinely measured clinical features modify the risks and benefits of two common treatment options, DPP4-inhibitor or SGLT2-inhibitor therapy, and which could be used to target these treatments to those patients most likely to benefit.What this study addsUsing data from 10,414 participants in 14 randomised trials, and 26,877 patients in UK primary care, we show several routinely available clinical features, notably glycated haemoglobin (HbA1c) and kidney function, are robustly associated with differential HbA1c responses to initiating SGLT2-inhibitor and DPP4-inhibitor therapies.Combining clinical features into a multivariable treatment selection model identifies validated patient strata with 1) a >5 mmol/mol HbA1c benefit for SGLT2-i therapy compared with DPP4-inhibitor therapy ; 2) a 50% reduced risk of early treatment discontinuation with DPP4-inhibitor therapy compared with SGLT2-inhibitor therapy.Our findings demonstrate a precision medicine approach based on routine clinical features can inform clinical decisions concerning optimal type 2 diabetes treatment choices.

AimsAtrial fibrillation (AF) is a risk for patients receiving thyroid hormone replacement therapy. No published work has focused on pharmacogenetics relevant to thyroid dysfunction and AF risk. We aimed to assess the effect of L-thyroxine on AF risk stratified by a variation in a candidate gene.Methods and ResultsA retrospective follow-up study was done among European Caucasian patients from the Genetics of Diabetes Audit and Research in Tayside Scotland cohort (Scotland, United Kingdom). Linked data on biochemistry, prescribing, hospital admissions, demographics, and genetic biobank were used to ascertain patients on L-thyroxine and diagnosis of AF. A GWAS-identified insulin receptor-INSR locus (rs4804416) was the candidate gene. Cox survival models and sensitivity analyses by taking competing risk of death into account were used. Replication was performed in additional sample (The Genetics of Scottish Health Research register, GoSHARE), and meta-analyses across the results of the study and replication cohorts were done. We analyzed 962 exposed to L-thyroxine and 5,840 unexposed patients who were rs4804416 genotyped. The rarer G/G genotype was present in 18% of the study population. The total follow-up was up to 20 years, and there was a significant increased AF risk for patients homozygous carriers of the G allele exposed to L-thyroxine (RHR = 2.35, P = 1.6e–02). The adjusted increased risk was highest within the first 3 years of exposure (RHR = 9.10, P = 8.5e–04). Sensitivity analysis yielded similar results. Effects were replicated in GoSHARE (n = 3,190).ConclusionHomozygous G/G genotype at the INSR locus (rs4804416) is associated with an increased risk of AF in patients on L-thyroxine, independent of serum of free thyroxine and thyroid-stimulating hormone serum concentrations.

OBJECTIVE The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes (T2D), glycemic traits, and risk of heart failure (HF). RESEARCH DESIGN AND METHODS Summary-level data were obtained from genome-wide association studies of T2D, insulin resistance (IR), glycated hemoglobin, fasting insulin and glucose, and HF. MR was conducted using the inverse-variance weighted method. Sensitivity analyses included the MR-Egger method, weighted median and mode methods, and multivariable MR conditioning on potential mediators. RESULTS Genetic liability to T2D was causally related to higher risk of HF (odds ratio [OR] 1.13 per 1-log unit higher risk of T2D; 95% CI 1.11–1.14; P < 0.001); however, sensitivity analysis revealed evidence of directional pleiotropy. The relationship between T2D and HF was attenuated when adjusted for coronary disease, BMI, LDL cholesterol, and blood pressure in multivariable MR. Genetically instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1-log unit higher risk of IR; 95% CI 1.00–1.41; P = 0.041). There were no notable associations identified between fasting insulin, glucose, or glycated hemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of T2D (OR 1.49; 95% CI 1.01–2.19; P = 0.042), although again with evidence of pleiotropy. CONCLUSIONS These findings suggest a possible causal role of T2D and IR in HF etiology, although the presence of both bidirectional effects and directional pleiotropy highlights potential sources of bias that must be considered.

OBJECTIVE To investigate the association between visit-to-visit HbA1c variability and cardiovascular events and microvascular complications in patients with newly diagnosed type 2 diabetes. RESEARCH DESIGN AND METHODS This retrospective cohort study analyzed patients from Tayside and Fife in the Scottish Care Information–Diabetes Collaboration (SCI-DC) who were observable from the diagnosis of diabetes and had at least five HbA1c measurements before the outcomes were evaluated. We used the previously reported HbA1c variability score (HVS), calculated as the percentage of the number of changes in HbA1c >0.5% (5.5 mmol/mol) among all HbA1c measurements within an individual. The association between HVS and 10 outcomes was assessed using Cox proportional hazards models. RESULTS We included 13,111–19,883 patients in the analyses of each outcome. The patients with HVS >60% were associated with elevated risks of all outcomes compared with the lowest quintile (for example, HVS >80 to ≤100 vs. HVS ≥0 to ≤20, hazard ratio 2.38 [95% CI 1.61–3.53] for major adverse cardiovascular events, 2.4 [1.72–3.33] for all-cause mortality, 2.4 [1.13–5.11] for atherosclerotic cardiovascular death, 2.63 [1.81–3.84] for coronary artery disease, 2.04 [1.12–3.73] for ischemic stroke, 3.23 [1.76–5.93] for heart failure, 7.4 [3.84–14.27] for diabetic retinopathy, 3.07 [2.23–4.22] for diabetic peripheral neuropathy, 5.24 [2.61–10.49] for diabetic foot ulcer, and 3.49 [2.47–4.95] for new-onset chronic kidney disease). Four sensitivity analyses, including adjustment for time-weighted average HbA1c, confirmed the robustness of the results. CONCLUSIONS Our study shows that higher HbA1c variability is associated with increased risks of all-cause mortality, cardiovascular events, and microvascular complications of diabetes independently of high HbA1c.

Type 2 diabetes is a complex disease usually diagnosed with little regard to aetiology. In the broader sense, it is a mix of different clearly defined aetiologies, such as monogenic diabetes, that we need to be better at identifying as this has major implications for treatment and patient management. Beyond this, however, type 2 diabetes is a highly heterogeneous polygenic disease. This review outlines the recent developments that recognise this heterogeneity by deconvoluting the aetiology of type 2 diabetes into pathophysiological processes, either by measuring physiological variables (such as beta cell function or insulin resistance) or using partitioned polygenic scores, and addresses recent work that clusters type 2 diabetes into distinct subgroups. Increasing evidence suggests that considering the aetiological components of type 2 diabetes matters, in terms of progression rates, treatment response and complications. In other words, clinicians need to recognise that type 2 diabetes is multifaceted and that its characteristics are important for how patients are managed. Electronic supplementary material The online version of this article (10.1007/s00125-019-4909-y) contains a slide of the figure for download, which is available to authorised users.

OBJECTIVE Gastrointestinal adverse effects occur in 20–30% of patients with metformin-treated type 2 diabetes, leading to premature discontinuation in 5–10% of the cases. Gastrointestinal intolerance may reflect localized high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe gastrointestinal adverse effects. RESEARCH DESIGN AND METHODS The study included 286 severe metformin-intolerant and 1,128 metformin-tolerant individuals from the IMI DIRECT (Innovative Medicines Initiative: DIabetes REsearCh on patient straTification) consortium. We assessed the association of patient characteristics, concomitant medication, and the burden of mutations in the SLC29A4 and SLC22A1 genes on odds of intolerance. RESULTS Women (P < 0.001) and older people (P < 0.001) were more likely to develop metformin intolerance. Concomitant use of transporter-inhibiting drugs increased the odds of intolerance (odds ratio [OR] 1.72, P < 0.001). In an adjusted logistic regression model, the G allele at rs3889348 (SLC29A4) was associated with gastrointestinal intolerance (OR 1.34, P = 0.005). rs3889348 is the top cis-expression quantitative trait locus for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with transporter-inhibiting drugs had more than three times higher odds of intolerance compared with carriers of no G allele and not treated with inhibiting drugs (OR 3.23, P < 0.001). Use of a genetic risk score derived from rs3889348 and SLC22A1 variants found that the odds of intolerance were more than twice as high in individuals who carry three or more risk alleles compared with those carrying none (OR 2.15, P = 0.01). CONCLUSIONS These results suggest that intestinal metformin transporters and concomitant medications play an important role in the gastrointestinal adverse effects of metformin.

© 2020 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UKAim: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. Methods: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. Results: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1–4. Participants in Subgroups 2–4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13–0.58), Subgroup 3 (β = 0.30; 95% CI 0.10–0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04–0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. Conclusions: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

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.

Context Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity. Objective To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies. Design We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models. Results Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10−9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10−9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity. Conclusion We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.

Background: Maturity Onset Diabetes of the Young (MODY) is a rare monogenic form of diabetes. In 2009, >80% of UK cases were estimated to be misdiagnosed. Since then, there have been a number of initiatives to improve detection of MODY: the Genetic Diabetes Nurse (GDN) education programme, the MODY probability calculator, and targeted next generation sequencing (tNGS). We aimed to determine an up-to-date prevalence estimate for UK MODY and assess how these initiatives have impacted case finding. Methods:UK referrals for genetic testing for monogenic diabetes diagnosed >1y of age from 1/1/1996 to 31/12/2019 were examined. Positive-test rates were compared for referrals reporting involvement of the GDNs/MODY calculator with those that did not. Findings: A diagnosis of MODY was confirmed in 3860 individuals, >3-fold higher than 2009 (n=1177). Median age at diagnosis in probands was 21y. GDN involvement was reported in 21% of referrals; these referrals had a higher positive-test rate than those without GDN involvement (32% v 23%, p

PurposeNeuropathic pain is a common disorder of the somatosensory system that affects 7%–10% of the general population. The disorder places a large social and economic burden on patients as well as healthcare services. However, not everyone with a relevant underlying aetiology develops corresponding pain. DOLORisk Dundee, a European Union-funded cohort, part of the multicentre DOLORisk consortium, was set up to increase current understanding of this variation in onset. In particular, the cohort will allow exploration of psychosocial, clinical and genetic predictors of neuropathic pain onset.ParticipantsDOLORisk Dundee has been constructed by rephenotyping two pre-existing Scottish population cohorts for neuropathic pain using a standardised ‘core’ study protocol: Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) (n=5236) consisting of predominantly type 2 diabetics from the Tayside region, and Generation Scotland: Scottish Family Health Study (GS:SFHS; n=20 221). Rephenotyping was conducted in two phases: a baseline postal survey and a combined postal and online follow-up survey. DOLORisk Dundee consists of 9155 participants (GoDARTS=1915; GS:SFHS=7240) who responded to the baseline survey, of which 6338 (69.2%; GoDARTS=1046; GS:SFHS=5292) also responded to the follow-up survey (18 months later).Findings to dateAt baseline, the proportion of those with chronic neuropathic pain (Douleur Neuropathique en 4 Questions questionnaire score ≥3, duration ≥3 months) was 30.5% in GoDARTS and 14.2% in Generation Scotland. Electronic record linkage enables large scale genetic association studies to be conducted and risk models have been constructed for neuropathic pain.Future plansThe cohort is being maintained by an access committee, through which collaborations are encouraged. Details of how to do this will be available on the study website (http://dolorisk.eu/). Further follow-up surveys of the cohort are planned and funding applications are being prepared to this effect. This will be conducted in harmony with similar pain rephenotyping of UK Biobank.

Background: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. Methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white European adults with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy in each cohort using linear regression followed by meta-analysis. These results were supported by cis-eQTLs and functional validation in cellular models. Findings: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction. The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.4×10−8) lower HbA1c reduction. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). The C-allele of rs10770791, near SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater HbA1c reduction (p=4.8×10 −8 ). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users (but not users), C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor. Interpretation: We have identified clinically important genetic effects at genomewide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs. Funding Statement: Innovative Medicines Initiative, the National Institutes of Health, the Geisinger Health Plan Quality Pilot Fund. Declaration of Interests: ERP has received honoraria for speaking from Lilly and Sanofi. JCF has received honoraria for speaking at scientific conferences from Novo, and for consulting from Goldfinch Bio. All other authors declare no competing interest. Ethics Approval Statement: This study was approved by respective research ethics review boards and participants provided written informed consent.

The hepatokine follistatin is elevated in patients with type 2 diabetes (T2D) and promotes hyperglycemia in mice. Here we explore the relationship of plasma follistatin levels with incident T2D and mechanisms involved. Adjusted hazard ratio (HR) per standard deviation (SD) increase in follistatin levels for T2D is 1.24 (CI: 1.04–1.47, p, Follistatin promotes in type 2 diabetes (T2D) pathogenesis in model animals and is elevated in patients with T2D. Here the authors report that plasma follistatin associates with increased risk of incident T2D in two longitudinal cohorts, and show that follistatin regulates insulin-induced suppression lipolysis in cultured human adipocytes.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.

OBJECTIVES Several small studies indicate the sulphonamide component of the drug sulfasalazine lowers HbA1c. We investigated reduction of HbA1c following incident prescription of sulfasalazine and related aminosalicylates, lacking the sulphonamide group, in an observational cohort. RESEARCH DESIGN AND METHODS Individuals in the Scottish Care Information Diabetes Collaboration (SCI-Diabetes) with type 2 diabetes and incident prescription for an aminosalicylate drug (sulfasalazine, mesalazine, olsalazine or balsalazide) were identified. Baseline and 6-month HbA1c were required for eligibility, to calculate HbA1c response. To investigate association with haemolysis, change in components of full blood count was assessed. Paired t-tests compared difference in baseline and treatment HbA1c measures and other clinical variables. RESULTS In all, 113 individuals treated with sulfasalazine and 103 with mesalazine (lacking the sulphonamide group) were eligible, with no eligible individuals treated with olsalazine or balsalazide. Baseline characteristics were similar. Mean (SD) HbA1c reduction at 6 months was -9 ± 16 mmol/mol (-0.9 ± 1.4%) (p

IntroductionPharmaceutical treatment options for patients with type 2 diabetes mellitus (T2DM) have increased to include multiple classes of oral glucose-lowering agents but without accompanying guidance on which of these may most benefit individual patients. Clinicians lack information for treatment intensification after first-line metformin therapy. Stratifying patients by simple clinical characteristics may improve care by targeting treatment options to those in whom they are most effective. This academically designed and run three-way crossover trial aims to test a stratification approach using three standard oral glucose-lowering agents.Methods and analysisTriMaster is a randomised, double-blind, crossover trial taking place at up to 25 clinical sites across England, Scotland and Wales. 520 patients with T2DM treated with either metformin alone, or metformin and a sulfonylurea who have glycated haemoglobin (HbA1c) >58 mmol/mol will be randomised to receive 16 weeks each of a dipeptidyl peptidase‐4 inhibitor, sodium-glucose co-transporter-2 inhibitor and thiazolidinedione in random order. Participants will be assessed at the end of each treatment period, providing clinical and biochemical data, and their experience of side effects. Participant preference will be assessed on completion of all three treatments. The primary endpoint is HbA1c after 4 months of therapy (allowing a range of 12–18 weeks for analysis). Secondary endpoints include participant-reported preference between the three treatments, tolerability and prevalence of side effects.Ethical approvalThis study was approved by National Health Service Health Research Authority Research Ethics Committee South Central—Oxford A, study 16/SC/0147. Written informed consent will be obtained from all participants. Results will be submitted to a peer-reviewed journal and presented at relevant scientific meetings. A lay summary of results will be made available to all participants.Trial registration numbers12039221; 2015-002790-38 and NCT02653209.

Objectives GLUT2 is a major facilitative glucose transporter, expressed from the SLC2A2 gene, with essential roles in the liver. Recent work in mice has shown that preventing Glut2 production in specific neuronal populations increases sugar-seeking behaviour, highlighting the importance of Slc2a2 gene expression in the brain. It implies that reduced GLUT2 in the brain, due to genetic polymorphisms or disease, impacts health through behaviour change. Defects in glucose transport in the brain are observed in conditions including type-2 diabetes and dementia. Few studies have directly examined the effect of modulating neuronal glucose transporter expression on cognitive function. The aim of this study was to investigate whether inactivating one Slc2a2 allele throughout the body had major effects on cognition. Cognitive tests to assess recognition memory, spatial working memory and anxiety were performed in Slc2a2 whole-body heterozygous mice (i.e. reduced Glut2 mRNA and protein), alongside littermates expressing normal levels of the transporter. Results No significant effects on neurological functions and cognitive capabilities were observed in mice lacking one Slc2a2 allele when fed a chow diet. This suggests that the minor variations in GLUT2 levels that occur in the human population are unlikely to influence behaviour and basic cognition.

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.

Background Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. Methods and findings In 1995–2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1–9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8–13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3–49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03–1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06–1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10−8) but not glycemic progression (HR: 1.01 [95% CI 0.96–1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. Conclusions Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression., Author summary Why was this study done? There is marked heterogeneity in the rate of progression to insulin requirement in patients with type 2 diabetes (T2D). Whereas some patients can maintain optimal glycemic control with oral glucose-lowering drugs for prolonged periods, others experience rapid deterioration in glycemia, requiring early insulin treatment. It is important to gain insight into what factors are associated with glycemic progression. Previous studies identified baseline HbA1c, young age, and weight gain as independent clinical predictors for glycemic progression in White populations, but there is a paucity of data in Asians. Besides environmental and lifestyle factors including obesity, it is well known that genetic factors play a pivotal role in the onset of diabetes. However, it remains unclear whether genetic variants of body mass index (BMI) and T2D predicts glycemic progression once T2D develops. Moreover, genetic variants associated with response to oral glucose-lowering drugs might also predict glycemic progression. What did the researchers do and find? We investigated glycemic progression among 7,091 Chinese patients with T2D from the Hong Kong Diabetes Register enrolled between 1995 and 2007. We explored the associations of clinical variables and different polygenic risk scores (PRSs) of obesity, T2D, and response to oral glucose-lowering drugs with glycemic progression and validated the associations of PRSs in another multicenter prospective cohort of Hong Kong Diabetes Biobank (HKDB). We found that there was an approximately 1.3-year delay of insulin use after oral glucose-lowering drug failure in real-world practice. We observed a U-shape association between BMI and glycemic progression and found that young age of diagnosis, high HbA1c and triglyceride, use of tobacco, and presence of microvascular complications also predicted rapid glycemic progression. We found that the PRS derived from 123 known risk variants for T2D was associated with early age of diagnosis and rapid glycemic progression, with validation in the replication cohort of HKDB, whereas the PRS derived from 63 BMI-related variants was associated with BMI but not glycemic progression. What do these findings mean? This study highlighted the delay in insulin treatment after oral glucose-lowering drug failure, which represents an important treatment gap in clinical practice. This work emphasized the importance of both genetic and modifiable risk factors, notably lipo-glucotoxicity, obesity, and tobacco use on glycemic progression, which enables identification of high-risk patients for optimal control of risk factors to improve glycemic durability. This study provides novel insights toward the underlying pathophysiology underlining glycemic progression, highlighting some overlap with pathogenesis of T2D. Our work highlights the potential utility of incorporating PRSs for drug response to guide clinical management of T2D.

The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as “diabetes.” Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

Background and Aims Acute kidney injury (AKI) is associated with adverse long term outcomes such as increased mortality. It is unclear however whether the increased mortality is due to cardiovascular events. The aim of this study was to examine major adverse cardiovascular events (MACE) following AKI in a population cohort in Scotland (GoDARTS) specifically examining a “low risk” younger age group without previous cardiovascular events. Method An observational cohort study of people in Tayside & Fife (Scotland, UK) recruited into GoDARTS from December 1998 to July 2017 was performed. Data were linked between the following datasets: population demography files, Scottish Morbidity Record of hospital admissions (SMR01); biochemical laboratory results, medicines dispensed by community pharmacies, the Scottish Care Initiative-Diabetes Collaboration (SCI-DC), the Scottish Renal Registry (SRR) and Scottish death registry data held by the Scottish General Records Office (GRO). AKI was defined using the creatinine based Kidney Disease Improving Global Outcomes (KDIGO) definition. MACE events were defined as hospitalisations or death due to myocardial infarction, ischaemic stroke, or coronary artery disease using International Classification of Diseases codes. Follow-up began at the time of first AKI occurrence for cases, and first recorded creatinine measurement for controls. Analyses were restricted to those who were 45 years or younger at the start of follow-up and who had no history of MACE. Survival analyses were performed using cox models adjusted for sex, age, CKD stage at baseline, Scottish index for multiple deprivation, medication use and diabetes. A forward step-wise method was used for variable selection. Analyses stratified by type 2 status were also performed. Results Data were available for 18,163 individuals in GoDARTS of which 15,884 individuals had creatinine measurements which could be used to classify whether an AKI event occurred. Of these, 6534 had at least one AKI event and 8807 never had an event. AKI was associated with more than two-fold increase in risk of MACE, independent of age, sex, type 2 diabetes, deprivation, anti-hypertensives, prior MACE and baseline renal function, HR 2.10 (1.90, 2.26), p Conclusion AKI is associated with significantly increased risk of prospective MACE with a two-fold increase when adjusted for age, prior MACE, baseline renal function, type 2 diabetes, deprivation and blood pressure. In a low-risk group (relatively younger and with no prior MACE), this risk persists. It is unclear whether AKI leads to CV events or AKI is more likely in this population but this finding highlights that cardiovascular risk reduction in patients following AKI is vital.

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 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.

ObjectivesTo evaluate and compare the lifetime costs associated with strategies to identify individuals with monogenic diabetes and change their treatment to more appropriate therapy.DesignA decision analytical model from the perspective of the National Health Service (NHS) in England and Wales was developed and analysed. The model was informed by the literature, routinely collected data and a clinical study conducted in parallel with the modelling.SettingSecondary care in the UK.ParticipantsSimulations based on characteristics of patients diagnosed with diabetes InterventionsFour test-treatment strategies to identify individuals with monogenic diabetes in a prevalent cohort of diabetics diagnosed under the age of 30 years were modelled: clinician-based genetic test referral, targeted genetic testing based on clinical prediction models, targeted genetic testing based on biomarkers, and blanket genetic testing. The results of the test-treatment strategies were compared with a strategy of no genetic testing.Primary and secondary outcome measuresDiscounted lifetime costs, proportion of cases of monogenic diabetes identified.ResultsBased on current evidence, strategies using clinical characteristics or biomarkers were estimated to save approximately £100–£200 per person with diabetes over a lifetime compared with no testing. Sensitivity analyses indicated that the prevalence of monogenic diabetes, the uptake of testing, and the frequency of home blood glucose monitoring had the largest impact on the results (ranging from savings of £400–£50 per person), but did not change the overall findings. The model is limited by many model inputs being based on very few individuals, and some long-term data informed by clinical opinion.ConclusionsCosts to the NHS could be saved with targeted genetic testing based on clinical characteristics or biomarkers. More research should focus on the economic case for the use of such strategies closer to the time of diabetes diagnosis.Trial registration numberNCT01238380.

IntroductionTo identify risk factors, absolute risk, and impact on treatment discontinuation of genital infections with sodium-glucose co-transporter-2 inhibitors (SGLT2i).Research design and methodsWe assessed the relationship between baseline characteristics and genital infection in 21 004 people with type 2 diabetes initiating SGLT2i and 55 471 controls initiating dipeptidyl peptidase-4 inhibitors (DPP4i) in a UK primary care database. We assessed absolute risk of infection in those with key risk factors and the association between early genital infection and treatment discontinuation.ResultsGenital infection was substantially more common in those treated with SGLT2i (8.1% within 1 year) than DPP4i (1.8%). Key predictors of infection with SGLT2i were female sex (HR 3.64; 95% CI 3.23 to 4.11) and history of genital infection; 5 years (HR 1.79; 1.55 to 2.07). Baseline HbA1c was not associated with infection risk for SGLT2i, in contrast to DPP4i where risk increased with higher HbA1c. One-year absolute risk of genital infection with SGLT2i was highest for those with a history of prior infection (females 23.7%, males 12.1%), compared with those without (females 10.8%, males 2.7%). Early genital infection was associated with a similar discontinuation risk for SGLT2i (HR 1.48; 1.21–1.80) and DPP4i (HR 1.58; 1.21–2.07).ConclusionsFemale sex and history of prior infection are simple features that can identify subgroups at greatly increased risk of genital infections with SGLT2i therapy. These data can be used to risk-stratify patients. High HbA1c is not a risk factor for genital infections with SGLT2i.

Aims/hypothesis Treatment change following a genetic diagnosis of MODY is frequently indicated, but little is known about the factors predicting future treatment success. We therefore conducted the first prospective study to determine the impact of a genetic diagnosis on individuals with GCK-, HNF1A- or HNF4A-MODY in the UK, and to identify clinical characteristics predicting treatment success (i.e. HbA1c ≤58 mmol/mol [≤7.5%]) with the recommended treatment at 2 years. Methods This was an observational, prospective, non-selective study of individuals referred to the Exeter Molecular Genetic Laboratory for genetic testing from December 2010 to December 2012. Individuals from the UK with GCK- or HNF1A/HNF4A-MODY who were not on recommended treatment at the time of genetic diagnosis, and who were diagnosed below the age of 30 years and were currently aged less than 50 years, were eligible to participate. Results A total of 44 of 58 individuals (75.9%) changed treatment following their genetic diagnosis. Eight individuals diagnosed with GCK-MODY stopped all diabetes medication without experiencing any change in HbA1c (49.5 mmol/mol [6.6%] both before the genetic diagnosis and at a median of 1.25 years’ follow-up without treatment, p = 0.88). A total of 36 of 49 individuals (73.5%) diagnosed with HNF1A/HNF4A-MODY changed treatment; however, of the 21 of these individuals who were being managed with diet or sulfonylurea alone at 2 years, only 13 (36.1% of the population that changed treatment) had an HbA1c ≤58 mmol/mol (≤7.5%). These individuals had a shorter diabetes duration (median 4.6 vs 18.1 years), lower HbA1c (58 vs 73 mmol/mol [7.5% vs 8.8%]) and lower BMI (median 24.2 vs 26.0 kg/m2) at the time of genetic diagnosis, compared with individuals (n = 23/36) with an HbA1c >58 mmol/mol (>7.5%) (or 11 years and an HbA1c of >69 mmol/mol (>8.5%) at the time of genetic diagnosis. Conclusions/interpretation In participants with GCK-MODY, treatment cessation was universally successful, with no change in HbA1c at follow-up. In those with HNF1A/HNF4A-MODY, a shorter diabetes duration, lower HbA1c and lower BMI at genetic diagnosis predicted successful treatment with sulfonylurea/diet alone, supporting the need for early genetic diagnosis and treatment change. Our study suggests that, in individuals with HNF1A/HNF4A-MODY with a longer duration of diabetes (>11 years) at time of genetic test, rather than ceasing current treatment, a sulfonylurea should be added to existing therapy, particularly in those who are overweight or obese and have a high HbA1c. Electronic supplementary material The online version of this article (10.1007/s00125-018-4728-6) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

BACKGROUND The pathophysiology of type 2 diabetes differs markedly by ethnicity. PURPOSE A systematic review and meta-analysis was conducted to assess the impact of ethnicity on the glucose-lowering efficacy of the newer oral agents, sodium–glucose cotransporter 2 inhibitors (SGLT-2i), glucagon-like peptide 1 receptor agonists (GLP-1RA), and dipeptidyl peptidase 4 inhibitors (DPP-4i), using evidence from randomized clinical trials (RCTs). DATA SOURCES A literature search was conducted in PubMed of all randomized, placebo-controlled trials of DPP-4i, SGLT-2i, and GLP-1RA. The search strategy was developed based on Medical Subject Headings (MeSH) terms and keywords. STUDY SELECTION A total of 64 studies that qualified for meta-analysis after full-text review based on predefined inclusion and exclusion criteria—RCTs with at least 50 patients in each arm, >70% of population from Asian or white group, duration ≥24 weeks, and publication up to March 2019—were selected for systematic review and meta-analysis. DATA EXTRACTION Data extraction was done for aggregated study-level data by two independent researchers. Absolute changes in HbA1c (%) from baseline to 24 weeks between the drug and placebo were considered as the primary end point of the study. DATA SYNTHESIS Change in HbA1c was evaluated by computing mean differences and 95% CIs between treatment and placebo arms. LIMITATIONS The study is based on summarized data and could not be separated based on East Asians and South Asians. CONCLUSIONS The glucose-lowering efficacy of SGLT-2i, and to a lesser extent DPP-4i, was greater in studies of predominantly Asian ethnicity compared with studies of predominantly white ethnicity. There was no difference seen by ethnicity for GLP-1RA.

Supplemental Digital Content is available in the text., Objective: The retina may provide readily accessible imaging biomarkers of global cardiovascular health. Increasing evidence suggests variation in retinal vascular traits is highly heritable. This study aimed to identify the genetic determinants of retinal vascular traits. Approach and Results: We conducted a meta-analysis of genome-wide association studies for quantitative retinal vascular traits derived using semi-automatic image analysis of digital retinal photographs from the GoDARTS (Genetics of Diabetes Audit and Research in Tayside; N=1736) and ORCADES (Orkney Complex Disease Study; N=1358) cohorts. We identified a novel genome-wide significant locus at 19q13 (ACTN4/CAPN12) for retinal venular tortuosity (TortV), and one at 13q34 (COL4A2) for retinal arteriolar tortuosity (TortA); these 2 loci were subsequently confirmed in 3 independent cohorts (Ntotal=1413). In the combined analysis of discovery and replication cohorts, the lead single-nucleotide polymorphism in ACTN4/CAPN12 was rs1808382 (βs.d.=-0.109; SE=0.015; P=2.39×10-13) and in COL4A2 was rs7991229 (βs.d.=0.103; SE=0.015; P=4.66×10-12). Notably, the ACTN4/CAPN12 locus associated with TortV is also associated with coronary artery disease, heart rate, and atrial fibrillation. Conclusions: Genetic determinants of retinal vascular tortuosity are also linked to cardiovascular health. These findings provide a molecular pathophysiological foundation for the use of retinal vascular traits as biomarkers for cardiovascular diseases.

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P < 5 × 10−6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10−8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/−) knockout mice have increased total body fat (P = 1.78 × 10−6) and increased fasted circulating glucose (P = 5.73 × 10−6). Furthermore, rare variants in STAT3 associated with worse metformin response (q

Aims/hypothesis The aim of the study was to examine the association between lipoprotein-associated phospholipase A2 (Lp-PLA2) activity levels and incident diabetic retinopathy and change in retinopathy grade. Methods This was a cohort study of diabetic participants with serum collected at baseline and routinely collected diabetic retinal screening data. Participants with type 2 diabetes from the GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) cohort were used. This cohort is composed of individuals of white Scottish ancestry from the Tayside region of Scotland. Survival analysis accounting for informative censoring by modelling death as a competing risk was performed for the development of incident diabetic retinopathy from a disease-free state in a 3 year follow-up period (n = 1364) by stratified Lp-PLA2 activity levels (in quartiles). The same analysis was performed for transitions to more severe grades. Results The hazard of developing incident diabetic retinopathy was 2.08 times higher (95% CI 1.64, 2.63) for the highest quartile of Lp-PLA2 activity compared with the lowest. Higher Lp-PLA2 activity levels were associated with a significantly increased risk for transitions to all grades. The hazards of developing observable (or more severe) and referable (or more severe) retinopathy were 2.82 (95% CI 1.71, 4.65) and 1.87 (95% CI 1.26, 2.77) times higher for the highest quartile of Lp-PLA2 activity compared with the lowest, respectively. Conclusions/interpretation Higher Lp-PLA2 levels are associated with increased risk of death and the development of incident diabetic retinopathy, as well as transitions to more severe grades of diabetic retinopathy. These associations are independent of calculated LDL-cholesterol and other traditional risk factors. Further, this biomarker study shows that the association is temporally sensitive to the proximity of the event to measurement of Lp-PLA2. Electronic supplementary material The online version of this article (10.1007/s00125-018-4601-7) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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.

Aims/hypothesis There is considerable variability in how diabetes progresses after diagnosis. Progression modelling has largely focused on ‘time to failure’ methods, yet determining a ‘coefficient of failure’ has many advantages. We derived a rate of glycaemic deterioration in type 2 diabetes, using a large real-world cohort, and aimed to investigate the clinical, biochemical, pharmacological and immunological variables associated with fast and slow rates of glycaemic deterioration. Methods An observational cohort study was performed using the electronic medical records from participants in the Genetics of Diabetes Audit and Research in Tayside Study (GoDARTS). A model was derived based on an individual’s observed HbA1c measures from the first eligible HbA1c after the diagnosis of diabetes through to the study end (defined as insulin initiation, death, leaving the area or end of follow-up). Each HbA1c measure was time-dependently adjusted for the effects of non-insulin glucose-lowering drugs, changes in BMI and corticosteroid use. GAD antibody (GADA) positivity was defined as GAD titres above the 97.5th centile of the population distribution. Results The mean (95% CI) glycaemic deterioration for type 2 diabetes and GADA-positive individuals was 1.4 (1.3, 1.4) and 2.8 (2.4, 3.3) mmol/mol HbA1c per year, respectively. A younger age of diagnosis, lower HDL-cholesterol concentration, higher BMI and earlier calendar year of diabetes diagnosis were independently associated with higher rates of glycaemic deterioration in individuals with type 2 diabetes. The rate of deterioration in those diagnosed at over 70 years of age was very low, with 66% having a rate of deterioration of less than 1.1 mmol/mol HbA1c per year, and only 1.5% progressing more rapidly than 4.4 mmol/mol HbA1c per year. Conclusions/interpretation We have developed a novel approach for modelling the progression of diabetes in observational data across multiple drug combinations. This approach highlights how glycaemic deterioration in those diagnosed at over 70 years of age is minimal, supporting a stratified approach to diabetes management. Electronic supplementary material The online version of this article (10.1007/s00125-017-4519-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Metformin is a widely-used drug that results in clear benefits in relation to glucose metabolism and diabetes-related complications. The mechanisms underlying these benefits are complex and still not fully understood. Physiologically, metformin has been shown to reduce hepatic glucose production, yet not all of its effects can be explained by this mechanism and there is increasing evidence of a key role for the gut. At the molecular level the findings vary depending on the doses of metformin used and duration of treatment, with clear differences between acute and chronic administration. Metformin has been shown to act via both AMP-activated protein kinase (AMPK)-dependent and AMPK-independent mechanisms; by inhibition of mitochondrial respiration but also perhaps by inhibition of mitochondrial glycerophosphate dehydrogenase, and a mechanism involving the lysosome. In the last 10 years, we have moved from a simple picture, that metformin improves glycaemia by acting on the liver via AMPK activation, to a much more complex picture reflecting its multiple modes of action. More work is required to truly understand how this drug works in its target population: individuals with type 2 diabetes. Electronic supplementary material The online version of this article (doi:10.1007/s00125-017-4342-z) contains a slideset of the figures for download, which is available to authorised users.

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.

We would like to thank Singh et al. (1) for their correspondence, which makes some important points regarding the prescribing of sodium–glucose cotransporter 2 inhibitors (SGLT-2i) and glucagon-like peptide 1 receptor agonists (GLP-1RA) in Asian patients with type 2 diabetes. We performed a systematic review and meta-analysis to assess the impact of ethnicity on the glucose-lowering efficacy of relatively newer antihyperglycemic agents SGLT-2i, GLP-1RA, and dipeptidyl peptidase 4 inhibitors (DPP-4i) using previously published evidence from randomized clinical trials (2). Careful reading of our article will reveal that we do not compare the drugs, nor do we infer that one drug class is better than another, but …

AIMS: To investigate, in the Carotid Atherosclerosis: Metformin for Insulin Resistance (CAMERA) trial (NCT00723307), whether the influence of metformin on the glucagon-like peptide (GLP)-1 axis in individuals with and without type 2 diabetes (T2DM) is sustained and related to changes in glycaemia or weight, and to investigate basal and post-meal GLP-1 levels in patients with T2DM in the cross-sectional Diabetes Research on Patient Stratification (DIRECT) study.MATERIALS AND METHODS: CAMERA was a double-blind randomized placebo-controlled trial of metformin in 173 participants without diabetes. Using 6-monthly fasted total GLP-1 levels over 18 months, we evaluated metformin's effect on total GLP-1 with repeated-measures analysis and analysis of covariance. In the DIRECT study, we examined active and total fasting and 60-minute post-meal GLP-1 levels in 775 people recently diagnosed with T2DM treated with metformin or diet, using Student's t-tests and linear regression.RESULTS: In CAMERA, metformin increased total GLP-1 at 6 (+20.7%, 95% confidence interval [CI] 4.7-39.0), 12 (+26.7%, 95% CI 10.3-45.6) and 18 months (+18.7%, 95% CI 3.8-35.7), an overall increase of 23.4% (95% CI 11.2-36.9; P < .0001) vs placebo. Adjustment for changes in glycaemia and adiposity, individually or combined, did not attenuate this effect. In the DIRECT study, metformin was associated with higher fasting active (39.1%, 95% CI 21.3-56.4) and total GLP-1 (14.1%, 95% CI 1.2-25.9) but not post-meal incremental GLP-1. These changes were independent of potential confounders including age, sex, adiposity and glycated haemoglobin.CONCLUSIONS: In people without diabetes, metformin increases total GLP-1 in a sustained manner and independently of changes in weight or glycaemia. Metformin-treated patients with T2DM also have higher fasted GLP-1 levels, independently of weight and glycaemia.

Aims/hypothesis Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). Methods From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6–24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. Results Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants’ clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants’ clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. Conclusions/interpretation The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes. Electronic supplementary material The online version of this article (10.1007/s00125-019-4906-1) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Aims: Ataxia telangiectasia (A-T) is a rare genetic condition associated with diabetes. OGTT data show that nondiabetic adults with A-T are insulin resistant. ATM-ko mice are glucose intolerant, insulin resistant with abnormal fat distribution, which improve with TZD treatment. The RAMP study assessed the effects of pioglitazone in A-T. Methods: This open label, non-randomised, crossover study compared 8 nondiabetic people with A-T and 15 healthy controls. Dual tracer mixed meal tests, indirect calorimetry, and adipocyte function were used to assess response to treatment. Results: Groups were matched for age, BMI and HbA1c. Pioglitazone reduced fasting (61 [IQR 26 - 135] to 28.0 [IQR 20.9 - 110.1] pmol/l, p=0.008) and meal (393 [IQR 275 - 2103] to 252 [IQR 195 - 823], p=0.02) insulin in the A-T group, with unchanged fasting but reduced mean glucose (6.3 [IQR 6.1 - 6.7] to 5.9 [IQR 5.7 - 6.2] mmol/l, p=0.02). Pioglitazone did not affect glucose or insulin in controls. Pioglitazone improved insulin sensitivity in the A-T group, with reduced HOMA-IR (2.19 [IQR 0.96 - 4.14] to 0.97 [IQR 0.76 - 3.54], p=0.023) and increased Matsuda index (2.7 [IQR 1.2 - 5.9] to 5.3 [IQR 1.6 - 7.1], p=0.008). Pioglitazone increased adiponectin in both groups (A-T: 2528 [IQR 2008 - 4887] to 12461 [IQR 4451 - 15843] ng/ml, p=0.008; control 6262 [IQR 4051 - 89940] to 15123 [IQR 9594 - 19384] ng/ml, p=0.001). NEFAs were reduced in A-T (0.25 [IQR 0.22 - 0.28] to 0.18 [IQR 0.14 - 0.20] mmol/l, p=0.016). Pioglitazone increased the fasting respiratory quotient (RQ) in A-T (0.9 [IQR 0.8 - 1.0] to 1.1 [IQR 1.0 - 1.2], p=0.04). A mixed effects model of insulin sensitivity identified a significant gene * drug interaction (p=0.001) between A-T and pioglitazone. Conclusion: Pioglitazone treatment improved insulin sensitivity and adipocyte function in A-T. Pioglitazone reduced NEFAs and increased RQ, indicating a reduction in fatty acid oxidation. These data suggest that pioglitazone may be an appropriate first line treatment for diabetes in A-T. Disclosure L.J. McCreight: None. A. Mari: Consultant; Self; Eli Lilly and Company. Research Support; Self; Boehringer Ingelheim International GmbH. E. Pearson: None. Funding UK Wellcome Trust (to E.P.)

Aims: Metformin uptake is transporter-dependent. Observational data indicates that reduced-function variants in organic cation transporter 1 (OCT1) and OCT1-inhibiting drugs increase risk of metformin intolerance. The ImpOCT study investigated impact of OCT1 genotype and use of omeprazole (an OCT1-inhibiting drug) on an individual’s metformin tolerance. Methods: This recruit-by-genotype, double-blind, randomised, placebo-controlled, crossover study recruited 61 nondiabetic individuals with homozygous reduced-function (null) or wild type (WT) OCT1 genotype. During two 4-week treatment periods, participants received metformin and concurrent omeprazole or placebo. Metformin was titrated weekly according to tolerance, assessed by interview and symptom severity scoring. The primary outcome was maximum tolerated dose of metformin (MTD) in each treatment period. Results were analysed using mixed effects modelling, assessing effect of genotype, treatment and gene x drug interaction on MTD. Results: Participants were well matched for age and gender. Median BMI was higher in OCT1 null (26.3 [IQR 24.1 - 28.1] vs. 29 [IQR 26.5 - 31.8], p Conclusions: Reduced function SNPs in OCT1 are relatively common and can affect transporter efficacy. This study identified a gene x drug interaction, between OCT1, metformin and omeprazole. Our data indicates that OCT1 WT individuals may benefit from PPI treatment to improve symptoms of metformin intolerance - this benefit is lost in those with reduced OCT1 function. Disclosure L.J. McCreight: None. S.M. Hapca: None. E. Pearson: None. Funding UK Wellcome Trust (to E.P.)

Plasma proteins can provide valuable insights on human health and disease states. Within the framework of the EU IMI project DIRECT (https://www.direct-diabetes.org), we used a set of affinity proteomic methods to profile > 3100 study participants at baseline. Multiplexed assays quantified more than 600 unique proteins in EDTA plasma from this multi-center cohort that included 2300 subjects at risk of developing T2D (HbA1c ~ 6-6.5%) as well as 800 with early T2D (HbA1c > 6.5%). Using extensive clinical and other omics metadata available, the aim of the investigation was to identify plasma proteins associated with baseline traits. An initial analysis highlighted the importance of considering sample-related and pre-analytical variables as possible confounders in the data analysis. Hence, we used linear mixed models that included several parameters such as age, sex, study center and collection date. Next, we defined proteins associating with any of the >50 quantitative clinical traits at baseline. We found more than 300 proteins in plasma that were associated with diabetes related traits (adjusted p-value < 0.0001), many of which were prominently associated with BMI. The shortlisted candidates included leptin which associates with waist circumference and BMI; IGFBP1 and IGFBP2 to Matsuda; adiponectin to basal insulin secretion rate and fasting HDL; LDL receptor proteins to fasting triglycerides; APOM to fasting cholesterol; or IL8 and MCP-1 to fasting AST. In addition, we performed pQTL analysis to assess any connection between the protein values in plasma and genetic variants. We observed >400 cis-pQTLs (q-value < 0.05), such as for APOM (rs2736163, p = 5.15 e-24), which illustrated that many of the studied protein profiles are affected by a genetic component. With follow-up samples collected 3-4 years after starting the study, the baseline values will serve as valuable indicators of progression and allow study of how each participant's disease phenotype changes over time or due to treatment. Disclosure M. Hong: None. A. Viñuela: None. R.S. Häussler: None. M. Dale: None. R.W. Koivula: None. J. Fernandez-Tajes: None. A. Mahajan: None. R. Bizzotto: Research Support; Self; GlaxoSmithKline plc. A. Mari: Consultant; Self; Eli Lilly and Company. Research Support; Self; Boehringer Ingelheim International GmbH. E. Dermitzakis: Advisory Panel; Self; DNAnexus LTD. Board Member; Self; Hybridstat LTD. M. McCarthy: Advisory Panel; Self; European Association for the Study of Diabetes, Pfizer Inc. Consultant; Self; Eli Lilly and Company, Merck & Co., Inc. Consultant; Spouse/Partner; Merck & Co., Inc. Research Support; Self; AbbVie Inc., Boehringer Ingelheim International GmbH. Research Support; Spouse/Partner; Diabetes UK. Research Support; Self; Janssen Pharmaceuticals, Inc., Merck & Co., Inc., National Institutes of Health. Research Support; Spouse/Partner; National Institutes of Health. Research Support; Self; Novo Nordisk A/S. Research Support; Spouse/Partner; Novo Nordisk A/S. Research Support; Self; Novo Nordisk Foundation, Roche Pharma, Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. P.W. Franks: Board Member; Self; Zoe Ltd. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Novo Nordisk Foundation, Sanofi, Servier. E. Pearson: None. J.M. Schwenk: None. Funding Innovative Medicines Initiative Joint Undertaking (115317)

The response to metformin, the most commonly used drug for the treatment of type 2 diabetes (T2D), is highly variable. The common variant rs7903146 C>T within the transcription factor 7-like 2 gene (TCF7L2) is the strongest genetic risk factor associated with T2D to date. In this study, we explored the effects of the TCF7L2 rs7903146 genotype on metformin response in T2D. The study included 86 newly diagnosed patients with T2D, incident users of metformin. Levels of fasting glucose, insulin, HbA(1c), total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and anthropometric parameters were measured prior to metformin therapy, and 6 and 12 months after the treatment. Genotyping of the TCF7L2 rs7903146 was performed by the Sequenom MassARRAY(®) iPLEX(®) platform. At baseline, the diabetes risk allele (T) showed an association with lower triglyceride levels (p = 0.037). After 12 months of metformin treatment, the T allele was associated with 25.9% lower fasting insulin levels (95% CI 10.9–38.3%, p = 0.002) and 29.1% lower HOMA-IR index (95% CI 10.1–44.1%, p = 0.005), after adjustment for baseline values. Moreover, the T allele was associated with 6.7% lower fasting glucose levels (95% CI 1.1–12.0%, p = 0.021), adjusted for baseline glucose and baseline HOMA-%B levels, after 6 months of metformin treatment. This effect was more pronounced in the TT carriers who had 16.8% lower fasting glucose levels (95% CI 7.0–25.6%, p = 0.002) compared to the patients with CC genotype. Our results suggest that the TCF7L2 rs7903146 variant affects markers of insulin resistance and glycemic response to metformin in newly diagnosed patients with T2D within the first year of metformin treatment.