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Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies.In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 mAmong 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbAA validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes.BHF-Turing Cardiovascular Data Science Award and the UK Medical Research Council.

OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. RESULTS Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Background and Aims The efficacy of statin therapy is hindered by intolerance to the therapy, leading to discontinuation. Variants in SLCO1B1, which encodes the hepatic transporter OATB1B1, influence statin pharmacokinetics, resulting in altered plasma concentration of the drug and its metabolites. Current pharmacogenetic guidelines require sequencing of the SLCO1B1 gene, which is more expensive and less accessible than genotyping. In this study we aimed to develop an easy, clinically implementable functional gene risk score (GRS) of common variants in SLCO1B1 to identify patients at risk of statin intolerance. Methods and Results A GRS was developed from four common variants in SLCO1B1. In statin users from Tayside Scotland, UK, those with a high-risk GRS had increased odds across three phenotypes of statin intolerance (general statin intolerance: ORGSI 2.42[95%CI:1.29, 4.31], p = 0.003; statin-related myopathy ORSRM 2.51[95%CI:1.28, 4.53], p = 0.004; statin-related suspected rhabdomyolysis: ORSRSR 2.85[95%CI:1.03, 6.65], p = 0.02). In contrast, using the Val174Ala genotype alone or the recommended OATP1B1 functional phenotypes produced weaker and less reliable results. A meta-analysis with results from adjudicated cases of statin-induced myopathy in the PREDICTION-ADR Consortium confirmed these findings (ORVal174Ala 1.99 [95%CI:1.01, 3.95], p = 0.048; ORGRS 1.76 [95%CI:1.16, 2.69], p = 0.008). For those requiring high-dose statin therapy, the high-risk GRS was more consistently associated with the time to onset of statin intolerance amongst the three phenotypes compared to Val174Ala (general statin intolerance: HRVal174Ala 2.49 [95%CI:1.09, 5.68], p = 0.03; HRGRS 2.44 [95%CI:1.46, 4.08], p Conclusions We provide evidence that a gene risk score based on four common SLCO1B1 variants provides an easily implemented genetic tool that is more reliable than the current recommended practice in estimating the risk and predicting early onset statin intolerance.

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.

BackgroundEjection fraction (EF) is a key component of heart failure (HF) classification, including the increasingly codified HF with mildly reduced EF (HFmrEF) category. However, the biologic basis of HFmrEF as an entity distinct from HF with preserved EF (HFpEF) and reduced EF (HFrEF) has not been well characterized.MethodsThe EXSCEL trial randomized participants with type 2 diabetes (T2DM) to once-weekly exenatide (EQW) vs. placebo. For this study, profiling of ∼5000 proteins using the SomaLogic SomaScan platform was performed in baseline and 12-month serum samples from N=1199 participants with prevalent HF at baseline. Principal component analysis (PCA) and ANOVA (FDR p55% [HFpEF], EF 40-55% [HFmrEF], EFResultsOf N=1199 EXSCEL participants with prevalent HF, 284 (24%), 704 (59%) and 211 (18%) had HFpEF, HFmrEF and HFrEF, respectively. Eight PCA protein factors and 221 individual proteins within these factors differed significantly across the three EF groups. Levels of the majority of proteins (83%) demonstrated concordance between HFmrEF and HFpEF, but higher levels in HFrEF, predominated by the domain of extracellular matrix regulation,e.g.COL28A1 and tenascin C [TNC]; pi.e.HFmrEF-HFpEF concordance. Baseline levels of 208 (94%) of the 221 proteins were associated with time-to-incident HF hospitalization including domains of extracellular matrix (COL28A1, TNC), angiogenesis (ANG2, VEGFa, VEGFd), myocyte stretch (NT-proBNP), and renal function (cystatin-C). Change in levels of 10 of the 221 proteins from baseline to 12 months (including increase in TNC) predicted incident HF hospitalization (pConclusionsIn this HF substudy of a large clinical trial of people with T2DM, we found that serum levels of most proteins across multiple biologic domains were similar between HFmrEF and HFpEF. HFmrEF may be more biologically similar to HFpEF than HFrEF, and specific related biomarkers may offer unique data on prognosis and pharmacotherapy modification with variability by EF.

OBJECTIVE To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five RHAPSODY data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide and HDL) and four risk-driven subgroups using fixed cut-offs for HbA1c and risk of cardiovascular disease based on guidelines. The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist’s subgroups. RESULTS Under care-as-usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost 22.0% and 25.3% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to ten-fold increases in QALYs gained. CONCLUSIONS Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Patients with type 2 diabetes vary in their response to currently available therapeutic agents (including GLP-1 receptor agonists) leading to suboptimal glycemic control and increased risk of complications. We show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase(PAM),as well asPam-knockout mice, display increased resistance to GLP-1in vivo.Paminactivation in mice leads to reduced gastric GLP-1R expression and faster gastric emptying: this persists during GLP-1R agonist treatment and is rescued when GLP-1R activity is antagonized, indicating resistance to GLP-1’s gastric slowing properties. Meta-analysis of human data from studies examining GLP-1R agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphicPAMalleles p.S539W and p.D536G treated with GLP-1R agonist. Genetic variation inPAMhas effects on incretin signaling that alters response to medication used commonly for treatment of T2D.(Funded by the Wellcome, Medical Research Council, European Union, NIHR Oxford Biomedical Research Centre, United Kingdom, Registered on ClinicalTrials.gov,NCT02723110.)Summary ParagraphType 2 diabetes (T2D) is a leading cause of morbidity and mortality globally1. Current management of T2D patients focuses on lowering glycemic exposure and reducing complications with lifestyle and pharmacological interventions2. Despite the availability of multiple medications to lower glycated hemoglobin (HbA1c), only 53% of individuals with T2D reach the glycemic target (HbA1c 3, 4. There is potential to improve medication selection through “precision medicine” where patient specific factors (e.g. genetic markers) are used to indicate whether a patient is more or less likely to respond to a medication. Here we show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase(PAM),as well asPam-knockout mice, have reduced PAM enzyme activity, display increased resistance to glucagon like peptide 1 (GLP-1)in vivoand have reduced response to the GLP-1 receptor agonist. Meta-analysis of human data from studies examining GLP-1 receptor agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphicPAMalleles p.S539W and p.D536G treated with GLP-1 receptor agonist. Genetic variation inPAMhas effects on incretin signaling that alters response to medication used commonly for treatment of T2D.

Objective To assess the real-world cardiovascular (CV) safety for SU, in comparison with dipeptidylpeptidase-4 inhibitors (DPP4i) and thiazolidinediones (TZD) through development of robust methodology for causal inference in a whole nation study. Research Design and Methods A cohort study was performed including people with type 2 diabetes diagnosed in Scotland before 31 December 2017, who failed to reach HbA1c 48 mmol/mol despite metformin monotherapy and initiated second-line pharmacotherapy (SU/DPP4i/TZD) on or after 1 January 2010. The primary outcome was the composite major adverse cardiovascular events (MACE), including hospitalization for myocardial infarction (MI), ischemic stroke, heart failure, and CV death. Secondary outcomes were each individual endpoint and all-cause death. Multivariable Cox proportional hazards regression and an instrumental variable (IV) approach were used to control confounding in a similar way to the randomization process in a randomized control trial. Results Comparing SU to non-SU (DPP4i/TZD), the hazard ratio (HR) for MACE was 1.00 (95% CI: 0.91 to 1.09) from the multivariable Cox regression and 1.02 (0.91 - 1.13) and 1.03 (0.91- 1.16) using two different IVs. For all-cause death, the HR from Cox regression and the two IV analyses was 1.03 (0.94 - 1.13), 1.04 (0.93 - 1.17), and 1.03 (0.90 - 1.17). Conclusion Our findings contribute to the understanding that second-line SU for glucose lowering are unlikely to increase CV risk or all-cause mortality. Given their potent efficacy, microvascular benefits, cost effectiveness and widespread use, this study supports that SU should remain a part of the global diabetes treatment portfolio.Article

Aims/hypothesis South Asians in general, and Asian Indians in particular, have higher risk of type 2 diabetes compared with white Europeans, and a younger age of onset. The reasons for the younger age of onset in relation to obesity, beta cell function and insulin sensitivity are under-explored. Methods Two cohorts of Asian Indians, the ICMR-INDIAB cohort (Indian Council of Medical Research-India Diabetes Study) and the DMDSC cohort (Dr Mohan’s Diabetes Specialties Centre), and one of white Europeans, the ESDC (East Scotland Diabetes Cohort), were used. Using a cross-sectional design, we examined the comparative prevalence of healthy, overweight and obese participants with young-onset diabetes, classified according to their BMI. We explored the role of clinically measured beta cell function in diabetes onset in Asian Indians. Finally, the comparative distribution of a partitioned polygenic score (pPS) for risk of diabetes due to poor beta cell function was examined. Replication of the genetic findings was sought using data from the UK Biobank. Results The prevalence of young-onset diabetes with normal BMI was 9.3% amongst white Europeans and 24–39% amongst Asian Indians. In Asian Indians with young-onset diabetes, after adjustment for family history of type 2 diabetes, sex, insulin sensitivity and HDL-cholesterol, stimulated C-peptide was 492 pmol/ml (IQR 353–616, pp Conclusions/interpretation The prevalence of lean BMI in young-onset diabetes is over two times higher in Asian Indians compared with white Europeans. This phenotype of lean, young-onset diabetes appears driven in part by lower beta cell function. We demonstrate that Asian Indians with diabetes also have lower genetically determined beta cell function. Graphical abstract

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Background Considerable variability exists in response to metformin with few effective biomarkers to guide the treatment. Here we evaluated whether whole blood derived mitochondrial DNA copy number (mtDNA-CN) is a biomarker of metformin response as measured by glucose reduction or weight loss. Methods Using data from the trial of Metformin (n = 304) and AcaRbose (n = 300) in Chinese as the initial Hypoglycaemic treatment (MARCH), we examined the association between mtDNA-CN and two metformin response outcomes of HbA1c reduction and weight loss. The acarbose arm was used as a comparator group. Whole blood mtDNA-CN was estimated by deep whole genome sequencing with adjustments for confounders. Multiple linear regression and repeated measurement analyses were used to evaluate the association between mtDNA-CN and drug response outcomes. Results Here we show that glucose reduction is not significantly associated with mtDNA-CN and in either treatment arm. In the metformin arm, each increase of 1 SD in mtDNA-CN is significantly (P = 0.006) associated with a 0.43 kg more weight loss. Repeated measurement analysis shows that after 16 weeks of metformin monotherapy, patients in the top tertile of mtDNA-CN consistently lost 1.21 kg more weight than those in the bottom tertile (P Conclusions Patients with higher mtDNA-CN are likely to lose more weight upon metformin treatment, suggesting mtDNA-CN as a potential novel biomarker for more effective weight management in type 2 diabetes.

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

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 2 = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03–1.60; P 2 = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09–1.59; P = 0.0037; I2 = 97.14%), stroke (RR = 1.21; 95% CI 1.19–1.24; P 2 = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08–1.73; P = 0.01; I2 = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD.

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (pENOSF1 (minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], p=2.8×10-12). Rs145591055 near OMSR (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], p=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (p(G×T)

Background: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. Methods: In this genome-wide analysis we included adults (aged ≥18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA 1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA 1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. Findings: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA 1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G→A (Gly168Ser) in the GLP1R (0·08% [95% CI 0·04–0·12] or 0·9 mmol/mol lower reduction in HbA 1c per serine, p=6·0 × 10 −5) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6·7 × 10 −8), largely driven by rs140226575G→A (Thr370Met; 0·25% [SE 0·06] or 2·7 mmol/mol [SE 0·7] greater HbA 1c reduction per methionine, p=5·2 × 10 −6). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6–11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA 1c than the 9% of the population with the worse response. Interpretation: This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists.

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.

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (p

Neuropathic pain is difficult to treat, and an understanding of the risk factors for its onset and resolution is warranted. This study aimed to develop and externally validate two clinical risk models to predict onset and resolution of chronic neuropathic pain. Participants of Generation Scotland: Scottish Family Health Study (GS; general Scottish population; n = 20,221) and Genetic of Diabetes Audit and Research in Tayside Scotland (GoDARTS; n = 5236) were sent a questionnaire on neuropathic pain and followed- -up 18 months later. Chronic neuropathic pain was defined using DN4 scores (≥ 3/7) and pain for 3 months or more. The models were developed in GS using logistic regression with backward elimination based on the Akaike information criterion. External validation was conducted in GoDARTS and assessed model discrimination (ROC and Precision-Recall curves), calibration and clinical utility (decision curve analysis [DCA]). Analysis revealed incidences of neuropathic pain onset (6.0% in GS [236/3903] and 10.7% in GoDARTS [61/571]) and resolution (42.6% in GS [230/540] and 23.7% in GoDARTS [56/236]). Psychosocial and lifestyle factors were included in both onset and resolved prediction models. In GoDARTS, these models showed adequate discrimination (ROC = 0.636 and 0.699), but there was evidence of miscalibration (Intercept = − 0.511 and − 0.424; slope = 0.623 and 0.999). The DCA indicated that the models would provide clinical benefit over a range of possible risk thresholds. To our knowledge, these are the first externally validated risk models for neuropathic pain. The findings are of interest to patients and clinicians in the community, who may take preventative or remedial measures.

PurposeThe 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.ParticipantsWe 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 dateThere 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 plansThe 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.

Precision medicine aims to treat an individual based on their clinical characteristics. A differential drug response, critical to using these features for therapy selection, has never been examined directly in type 2 diabetes. In this study, we tested two hypotheses: (1) individuals with body mass index (BMI) 30 kg/m

South Asians are diagnosed with type 2 diabetes (T2D) more than a decade earlier in life than seen in European populations. We hypothesised that studying the genomics of age of onset in these populations may give insight into earlier age at onset of T2D among individuals of South Asian descent. We conducted a meta-analysis of GWAS of age at diagnosis of T2D in 34,001 individuals from four independent cohorts of European and Asian Indians. We identified two signals near the TCF7L2 and CDKAL1 associated with age at onset of T2D. The strongest genome-wide significant variants at chromosome 10q25.3 in TCF7L2 (lead SNP rs7903146; p = 2.4 ×10-12; p-het =0.01; Beta = -0.436; SE = 0.02) and chromosome 6 p22.3 in CDKAL1 (rs9368219; p = 2.29 ×10-8; p-het =0.007; Beta = -0.053; SE=0.01) were directionally consistent across ethnic groups and present at similar frequencies, however both loci harboured additional independent signals that were only present in the South Asian cohorts. A genome wide signal was also obtained at chromosome 10q26.12 in WDR11 (rs3011366; p = 2.4 ×10-8; p-het =0.25; Beta = 1.44; SE=0.25) specifically in the South Asian Indian cohorts. Our study estimated 17% of heritability (h2) for age onset of T2D among South Indians; higher than the 5% heritability we observed in Europeans suggesting different genetic architectures between these populations. We further estimated the Asian Indian genome-wide polygenic risk score for T2D risk and identified that the variance explained by the Indian specific polygenic risk score (PRS) for age at onset of T2D is substantially higher (2%) in an independent cohort of Asian Indians compared to that seen in White Europeans (Author SummaryRecent large multi-ancestry genome-wide meta-analyses have identified over 277 genetic loci associated with type 2 diabetes (T2D). Most association studies for T2D have focused on populations of European ancestry, and the specific genetic architecture of T2D in the South Asian population has not been extensively investigated. Increasing evidence indicates that the prevalence of T2D is higher in South Asians with a strikingly earlier age at diagnosis when compared to Europeans. Genome-wide association studies (GWAS) on age at age at diagnosis of T2D are limited and can help elucidate underlying mechanisms. We conducted the largest meta-analysis to date of genome-wide association studies of age at diagnosis as a surrogate for age of onset of T2D in South Indians and Europeans. Our trans-ancestry meta-analysis identified genetic loci near TCF7L2 and CDKAL1 with substantial ethnic heterogeneity in both allele frequencies and effect sizes in early diagnosis with T2D. Two novel variants near TCF7L2 (rs570193324) and CDKAL1 (rs143316471) were associated with age of diagnosis of T2D only in South Indians and were present at much lower frequency in the European populations. Heritability estimates for age of onset were much stronger in Asian Indians compared to Europeans and a polygenic risk score was constructed using a South Indians, which explained about 2% trait variance compared to European ancestry (

Type 2 diabetes is a complex, multifactorial disease with varying presentation and underlying pathophysiology. Recent studies using data-driven cluster analysis have led to a stratification of type 2 diabetes into novel subgroups based on six clinical measurements. Whether these subgroups truly correspond to the underlying phenotypic differences is nevertheless unclear. Here, we apply an unsupervised, data-driven clustering method (Similarity Network Fusion) to characterize type 2 diabetes in two independent cohorts involving 1,134 subjects in total based on integrated plasma lipidomics and peptidomics data without pre-selection. Logistic regression was then used to explore clustering based on ≥ 180 circulating lipids and 1,195 protein biomarkers, alongside clinical signatures. Two subgroups were identified, one of which associated with elevated C-peptide levels, diabetic complications and more severe insulin resistance compared to the other. GWAS analysis against 403 type 2 diabetes risk variants revealed associations of several SNPs with clusters and altered molecular profiles. We thus demonstrate that heterogeneity in type 2 diabetes can be captured by circulating omics alone using an unsupervised bottom-up approach. Such multiomics signatures could reflect pathological mechanisms underlying type 2 diabetes and thus may help inform on precision medicine approaches to disease management.

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.

Disturbances affecting early development have broad repercussions on the individual’s health during infancy and adulthood. Multiple observational studies throughout the years have shown that alterations of fetal growth are associated with increased cardiometabolic disease risks. However, the genetic component of this association only started to be investigated in the last 40 years, when single genes with distinct effects were investigated. Birth weight (BW), commonly reported as the outcome of developmental growth, has been estimated to be 20% to 60% heritable. Through Genome-Wide Association (GWA) meta-analyses, 190 different loci have been identified being associated with BW, and while many of these loci designate genes involved in glucose and lipid metabolism, with clear ties to fetal development, the role of others is not yet understood. In addition, due to its influence over the intrauterine environment, the maternal genotype also plays an important part in the determination of offspring BW, with the same loci having independent effects of different magnitude or even direction. There is still much to uncover regarding the genetic determinants of BW and the interactions between maternal, offspring, and even paternal genotype. To fully understand these, diverse and novel cohorts from multiple ancestries collecting extensive neonatal phenotype will be needed. This review compiles, chronologically, the main findings in the investigation of the genetics of BW.

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.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).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).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.

Background and AimsThe efficacy of statin therapy is hindered by adverse drug reactions, most frequently musculoskeletal symptoms. Variants in SLCO1B1, which encodes the hepatic transporter OATB1B1, influence statin pharmacokinetics, resulting in altered plasma concentration of the drug and its metabolites. While pharmacogenetic testing of the loss-of-function Val174Ala (rs4149056T>C) is recommended to reduce risk of statin intolerance, current guidelines acknowledge the potential role of gain-of-function variants. This study tests the hypothesis that accounting for gain-of-function variants in SLCO1B1, in addition to Val174Ala, will provide more reliable estimates of statin intolerance.Methods and ResultsHigh-risk haplotypes were derived from Val174Ala and three common gain-of-function SLCO1B1 variants and compared to low-risk haplotypes. In statin users from Tayside Scotland, UK, those with high-risk haplotypes had increased odds across three phenotypes of statin intolerance (general statin intolerance: ORGSI 2.42[95%CI:1.29, 4.31], p=0.003; statin-related myopathy ORSRM 2.51[95%CI:1.28, 4.53],p=0.004; statin-related suspected rhabdomyolysis: ORSRSR 2.85[95%CI:1.03, 6.65],p=0.02). In contrast, using the Val174Ala genotype alone produced weaker results. A meta-analysis with results from adjudicated cases of statin-induced myopathy in the PREDICTION-ADR Consortium confirmed these findings (ORVal174Ala 1.99 [95%CI:1.01, 3.95],p=0.048; ORrisk-haplotypes 1.76 [95%CI:1.16, 2.69],p=0.008). For those requiring high-dose statin therapy, high-risk haplotypes were more consistently associated with the time to onset of statin intolerance amongst the three phenotypes compared to Val174Ala (general statin intolerance: HRVal174Ala 2.49 [95%CI:1.09, 5.68],p=0.03; HRrisk-haplotypes 2.44 [95%CI:1.46, 4.08],pConclusionsWe demonstrate that accounting for gain-of-function variants in SLCO1B1, in addition to Val174Ala, provides more reliable estimates of statin intolerance.

Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.

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.

Objective: We aimed to validate the association of genome-wide association study (GWAS)-identified loci and polygenic risk score with serum thyroid-stimulating hormone (TSH) concentrations and the diagnosis of hypothyroidism. Then, the causal relationship between serum TSH and osteoporotic bone fracture risk was tested. Methods: A cross-sectional study was done among patients of European Caucasian ethnicity recruited in Tayside (Scotland, UK). Electronic medical records (EMRs) were used to identify patients and average serum TSH concentration and linked to genetic biobank data. Genetic associations were performed by linear and logistic regression models. One-sample Mendelian randomization (MR) was used to test causality of serum TSH on bone fracture risk. Results: Replication in 9,452 euthyroid individuals confirmed known loci previously reported. The 58 polymorphisms accounted for 11.08% of the TSH variation (p < 1e−04). TSH-GRS was directly associated with the risk of hypothyroidism with an odds ratio (OR) of 1.98 for the highest quartile compared to the first quartile (p = 2.2e−12). MR analysis of 5,599 individuals showed that compared with those in the lowest tertile of the TSH-GRS, men in the highest tertile had a decreased risk of osteoporotic bone fracture (OR = 0.59, p = 2.4e−03), while no difference in a similar comparison was observed in women (OR = 0.93, p = 0.61). Sensitivity analysis yielded similar results. Conclusions: EMRs linked to genomic data in large populations allow replication of GWAS discoveries without additional genotyping costs. This study suggests that genetically raised serum TSH concentrations are causally associated with decreased bone fracture risk in men.

The association between body weight fluctuation 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 fluctuation 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 fluctuation 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 fluctuation were found to have significantly increased risk of any CV events (RR = 1.27; 95% Confidence Interval (CI) 1.17–1.38; P

Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service Tayside (NHS Tayside) health board who provide healthcare services to the population. In Tayside, Scotland a comprehensive informatics network for diabetes care and research has been established for over 25 years, expanding more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information-Diabetesor SCI-Diabetes. This has enabled improved diabetes screening, integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. This review describes how health informatics has been used to improve care of people diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and co-morbidities.

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.