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Abstract Background BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability. Methods We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome. Results After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08–1.17, P

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

IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma.MethodsCirculating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.ResultsFrom a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.ConclusionsUsing an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Abstract Background A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. Results Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. Conclusions Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

Abstract We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Abstract Background Diabetic retinopathy (DR) is a major sight-threatening microvascular complication in individuals with diabetes. Systemic inflammation combined with oxidative stress is thought to capture most of the complexities involved in the pathology of diabetic retinopathy. A high level of neutrophil–lymphocyte ratio (NLR) is an indicator of abnormal immune system activity. Current estimates of the association of NLR with diabetes and its complications are almost entirely derived from cross-sectional studies, suggesting that the nature of the reported association may be more diagnostic than prognostic. Therefore, in the present study, we examined the utility of NLR as a biomarker to predict the incidence of DR in the Scottish population. Methods The incidence of DR was defined as the time to the first diagnosis of R1 or above grade in the Scottish retinopathy grading scheme from type 2 diabetes diagnosis. The effect of NLR and its interactions were explored using a competing risks survival model adjusting for other risk factors and accounting for deaths. The Fine and Gray subdistribution hazard model (FGR) was used to predict the effect of NLR on the incidence of DR. Results We analysed data from 23,531 individuals with complete covariate information. At 10 years, 8416 (35.8%) had developed DR and 2989 (12.7%) were lost to competing events (death) without developing DR and 12,126 individuals did not have DR. The median (interquartile range) level of NLR was 2.04 (1.5 to 2.7). The optimal NLR cut-off value to predict retinopathy incidence was 3.04. After accounting for competing risks at 10 years, the cumulative incidence of DR and deaths without DR were 50.7% and 21.9%, respectively. NLR was associated with incident DR in both Cause-specific hazard (CSH = 1.63; 95% CI: 1.28–2.07) and FGR models the subdistribution hazard (sHR = 2.24; 95% CI: 1.70–2.94). Both age and HbA1c were found to modulate the association between NLR and the risk of DR. Conclusions The current study suggests that NLR has a promising potential to predict DR incidence in the Scottish population, especially in individuals less than 65 years and in those with well-controlled glycaemic status.

Abstract Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein–protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Abstract We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Objectives: Metformin is the first line therapy recommended for type 2 diabetes. However, the precise mechanism of action remains unclear and up to a quarter of patients show some degree of intolerance to the drug, with a similar number showing poor response to treatment, limiting its effectiveness. A better understanding of the mechanism of action of metformin may improve its clinical use. SLC2A2 (GLUT2) is a transmembrane facilitated glucose transporter, with important roles in the liver, gut and pancreas. Our group previously identified single nucleotide polymorphisms in the human SLC2A2 gene, which were associated with reduced transporter expression and an improved response to metformin treatment. The aims of this study were to model Slc2a2 deficiency and measure the impact on glucose homoeostasis and metformin response in mice. Methods: We performed extensive metabolic phenotyping and 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG)-positron emission tomography (PET) analysis of gut glucose uptake in high-fat diet-fed (HFD) mice with whole-body reduced Slc2a2 (Slc2a2+/−) and intestinal Slc2a2 KO, to assess the impact of metformin treatment. Results: Slc2a2 partial deficiency had no major impact on body weight and insulin sensitivity, however mice with whole-body reduced Slc2a2 expression (Slc2a2+/−) developed an age-related decline in glucose homoeostasis (as measured by glucose tolerance test) compared to wild-type (Slc2a2+/+) littermates. Glucose uptake into the gut from the circulation was enhanced by metformin exposure in Slc2a2+/+ animals fed HFD and this action of the drug was significantly higher in Slc2a2+/− animals. However, there was no effect of specifically knocking-out Slc2a2 in the mouse intestinal epithelial cells. Conclusions: Overall, this work identifies a differential metformin response, dependent on expression of the SLC2A2 glucose transporter, and also adds to the growing evidence that metformin efficacy includes modifying glucose transport in the gut. We also describe a novel and important role for this transporter in maintaining efficient glucose homoeostasis during ageing.

Wang, Liang, Huang et al. evaluate associations between mitochondrial DNA copy number (mtDNA-CN) and HbA1c and weight loss in patients with type 2 diabetes treated with metformin or acarbose, as part of the MARCH study. They find that patients with higher mtDNA-CN lost more weight on metformin, but did not see the same with acarbose.

Abstract The association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17–1.38; P

Abstract. Background:. Current practice uses the latest measure of glycated hemoglobin (HbAlc) to facilitate clinical decision-making. Studies have demonstrated that HbAlc variability links the risk of death and complications of diabetes. However, the role of HbAlc variability is unclear in clinical practice. This systematic review summarized the evidence of visit-to-visit HbAlc variability regarding different metrics in micro- and macro-vascular complications and death in people with type 2 diabetes. Methods:. We searched PubMed, EMBASE (via OVID), and Cochrane Central Register (CENTRAL, via OVID) for studies investigating the association between HbAlc variability and adverse outcomes in patients with type 2 diabetes and performed random-effects meta-analysis stratified by HbAlc variability metrics in terms of standard deviation (SD), coefficient of variation (CV), and HbAlc variability score (HVS). Results:. In people with type 2 diabetes, the highest quantile of all three HbAlc variability metrics (HbAlc-standard deviation [HbAlc-SD], HbAlc-coefficient of variance [HbAlc-CV], and HVS) is associated with increased risks of all-cause mortality, cardiovascular events, progression to chronic kidney disease, amputation, and peripheral neuropathy. For example, the hazard ratio of HbAlc-SD on all-cause mortality was l.89 with 95% confidence interval (95% CI) l.46–2.45 (HbAlc-CV l.47, 95% CI l.26–l.72; HVS l.67, 95% CI l.34–2.09). Conclusions:. High HbAlc variability leads to micro- and macro-vascular complications of type 2 diabetes and related death. People with type 2 diabetes and high HbAlc variability need additional attention and care for the potential adverse outcomes.

Aim: To study the health and socioeconomic impact of the COVID-19 pandemic and to assess the barriers to self-management of diabetes during the lockdown, in rural South India. Materials and Methods: Details of demographic, social, economic, migration and health status were collected using a structured questionnaire from participants aged ≥18 years belonging to the 21 villages of Chengalpattu and Kancheepuram districts of Tamil Nadu state in south India as part of the Telemedicine pRoject for screENing Diabetes and its complications in rural Tamil Nadu (TREND) study. From the 11,249 TREND participants, a random list of 25% (n = 2812) was system-generated using random numbers and 2812 participants were contacted for the study, of whom 2511 individuals participated. Telephonic interviews were conducted during the lockdown from June to August 2020. Further, qualitative interviews(Focus group discussions) were conducted among 27 individuals with diabetes between September and December 2020. Data were analyzed using thematic analysis. Results: The mean age of the study population was 43 ± 14 years and 50.4% were women. Diabetes was present in 14.7%, hypertension in 31.9%, generalized and abdominal obesity in 33.3% and 46.5% respectively. When the lockdown was implemented in March 2020, 37% had migrated from urban to rural areas. Lack of daily wage jobs (68%), price of essential commodities (41.7%), social distancing/curfew (34.8%), mental fatigue/depression (14.7%), and loss of job (7.1%) were some reasons stated for their adverse social and financial circumstances. People with diabetes stated that they had to avoid or cut down their regular hospital visits due to travel restrictions. Many of the patients took the same medications for almost a year. Conclusion: Unemployment, poor mental health, and reduced household income were the most significant negative impacts faced by rural residents during the lockdown due to COVID-19. People with diabetes experienced disruptions in diabetes management due to the pandemic.

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.

Purpose The Scottish Diabetes Research Network (SDRN)-diabetes research platform was established to combine disparate electronic health record data into research-ready linked datasets for diabetes research in Scotland. The resultant cohort, ‘The SDRN-National Diabetes Dataset (SDRN-NDS)’, has many uses, for example, understanding healthcare burden and socioeconomic trends in disease incidence and prevalence, observational pharmacoepidemiology studies and building prediction tools to support clinical decision making.Participants We estimate that >99% of those diagnosed with diabetes nationwide are captured into the research platform. Between 2006 and mid-2020, the cohort comprised 472 648 people alive with diabetes at any point in whom there were 4 million person-years of follow-up. Of the cohort, 88.1% had type 2 diabetes, 8.8% type 1 diabetes and 3.1% had other types (eg, secondary diabetes). Data are captured from all key clinical encounters for diabetes-related care, including diabetes clinic, primary care and podiatry and comprise clinical history and measurements with linkage to blood results, microbiology, prescribed and dispensed drug and devices, retinopathy screening, outpatient, day case and inpatient episodes, birth outcomes, cancer registry, renal registry and causes of death.Findings to date There have been >50 publications using the SDRN-NDS. Examples of recent key findings include analysis of the incidence and relative risks for COVID-19 infection, drug safety of insulin glargine and SGLT2 inhibitors, life expectancy estimates, evaluation of the impact of flash monitors on glycaemic control and diabetic ketoacidosis and time trend analysis showing that diabetic ketoacidosis (DKA) remains a major cause of death under age 50 years. The findings have been used to guide national diabetes strategy and influence national and international guidelines.Future plans The comprehensive SDRN-NDS will continue to be used in future studies of diabetes epidemiology in the Scottish population. It will continue to be updated at least annually, with new data sources linked as they become available.

Objective The aim of this study was to investigate the association between obesity, diabetes and metabolic related liver dysfunction and the incidence of cancer.Design This study was conducted with health record data available from the National Health Service in Tayside and Fife. Genetics of Diabetes Audit and Research Tayside, Scotland (GoDARTS), Scottish Health Research Register (SHARE) and Tayside and Fife diabetics, three Scottish cohorts of 13 695, 62 438 and 16 312 patients, respectively, were analysed in this study. Participants in GoDARTS were a volunteer sample, with half having type 2 diabetes mellitus(T2DM). SHARE was a volunteer sample. Tayside and Fife diabetics was a population-level cohort. Metabolic dysfunction-related liver disease (MDLD) was defined using alanine transaminase measurements, and individuals with alternative causes of liver disease (alcohol abuse, viruses, etc) were excluded from the analysis.Results MDLD associated with increased cancer incidence with a HR of 1.31 in a Cox proportional hazards model adjusted for sex, type 2 diabetes, body mass index(BMI), and smoking status (95% CI 1.27 to 1.35, p

Abstract 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.

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.

Abstract Background It is unclear what to do when people with type 2 diabetes have had no or a limited glycemic response to a recently introduced medication. Intra-individual HbA1c variability can obscure true response. Some guidelines suggest stopping apparently ineffective therapy, but no studies have addressed this issue. Methods In a retrospective cohort analysis using the UK Clinical Practice Research Datalink (CPRD), we assessed the outcome of 55,530 patients with type 2 diabetes starting their second or third non-insulin glucose-lowering medication, with a baseline HbA1c > 58 mmol/mol (7.5%). For those with no HbA1c improvement or a limited response at 6 months (HbA1c fall

Purpose Neuropathic 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.Participants DOLORisk 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 date At 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 plans The 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.

The biguanide, metformin, is the first-choice therapeutic agent for type-2 diabetes, although the mechanisms that underpin metformin clinical efficacy remain the subject of much debate, partly due to the considerable variation in patient response to metformin. Identification of poor responders by genotype could avoid unnecessary treatment and provide clues to the underlying mechanism of action. GWAS identified SNPs associated with metformin treatment success at a locus containing the NPAT (nuclear protein, ataxia-telangiectasia locus) and ATM (ataxia-telangiectasia mutated) genes. This implies that gene sequence dictates a subsequent biological function to influence metformin action. Hence, we modified expression of NPAT in immortalized cell lines, primary mouse hepatocytes and mouse tissues, and analysed the outcomes on metformin action using confocal microscopy, immunoblotting and immunocytochemistry. In addition, we characterised the metabolic phenotype of npat heterozygous knockout mice and established the metformin response following development of insulin resistance. NPAT protein was localised in the nucleus at discrete loci in several cell types, but over-expression or depletion of NPAT in immortalised cell models did not change cellular responses to biguanides. In contrast, metformin regulation of respiratory exchange ratio (RER) was completely lost in animals lacking one allele of npat. There was also a reduction in metformin correction of impaired glucose tolerance, however no other metabolic abnormalities, or response to metformin, were found in the npat heterozygous mice. In summary, we provide methodological advancements for the detection of NPAT, demonstrate that minor reductions in NPAT mRNA levels (20-40%) influence metformin regulation of RER, and propose that the association between NPAT SNPs and metformin response observed in GWAS, could be due to loss of metformin modification of cellular fuel usage.

Summary: 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.

BackgroundType 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 findingsIn 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.ConclusionsOur 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.

BackgroundNon-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.Methods and findingsWe utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (ConclusionsIn this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.Trial registrationClinicalTrials.gov NCT03814915.

Introduction To identify risk factors, absolute risk, and impact on treatment discontinuation of genital infections with sodium-glucose co-transporter-2 inhibitors (SGLT2i).Research design and methods We 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.Results Genital 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).Conclusions Female 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.

Abstract Aim To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression. Methods We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined. Results Our search yielded 2785 articles; based on title, abstract and full‐text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: ‘Initiation of insulin’, ‘Initiation of oral antidiabetic drug’, ‘treatment intensification’, ‘antidiabetic therapy failure’, ‘glycaemic deterioration’, ‘decline in beta‐cell function’ and ‘change in insulin dose’. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi‐ethnic studies. Conclusion We have identified the major determinants of diabetes progression—younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large‐scale studies of diabetes progression in different ethnic groups.

AimSubclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.MethodsThe study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.ResultsAt baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.ConclusionsDifferent glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.

Recall-by-Genotype (RbG) is an approach to recall participants from genetic studies based on their specific genotype for further, more extensive phenotyping. Here, the authors discuss examples of RbG as well as practical and ethical considerations and provide an online tool to aid in designing RbG studies.

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, HbA1c, 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.

Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P < 0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secretion.