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Chenghui Zhang,1,2,* Qing Ou,1,* Yan Gu,1,* Gaiping Cheng,3 Rong Du,1,2 Li Yuan,1 Ruth LM Cordiner,4 Deying Kang,5 Jiaying Zhang,6 Qiaorong Huang,7 Chuan Yu,8 Li Kang,9 Xuan Wang,4,10 Xin Sun,5 Xianming Mo,7 Haoming Tian,1 Ewan R Pearson,4 Wentong Meng,7 Sheyu Li1,4 1Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 2Department of Endocrinology and Metabolism, Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region, Chengdu 610041, People’s Republic of China; 3Department of Clinical Nutrition, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 4Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, Scotland, UK; 5Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 6Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 7Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 8Department of Health-Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu 610041, People’s Republic of China; 9Division of Systems Medicine, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 9SY, Scotland, UK; 10Science for Life Laboratory, Department of Medical Cell Biology, Uppsala University, Uppsala 75123, Sweden*These authors contributed equally to this workCorrespondence: Sheyu LiDepartment of Endocrinology and Metabolism, West China Hospital, Sichuan University, 37# Guoxue Road, Chengdu 610041, ChinaTel +86-13194874843Fax +86-28-85422982Email lisheyu@gmail.comWentong MengLaboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guoxue Road, Chengdu, Sichuan 610041, People’s Republic of ChinaTel +86-18980601256Fax +86-28-85422982Email mwentong@sina.comAim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM).Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF+PS+MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF+PS+MPs and PS+MPs (TF+PS+MPs/PS+MPs).Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS+MPs, TF+PS+MPs and TF+PS+MPs/PS+MPs were elevated in patients with T1DM (PS+MPs: 1078.5 ± 158.08 vs 686.84 ± 122.04/μL, P

John M Dennis,1 Beverley M Shields,2 Angus G Jones,2 Ewan R Pearson,3 Andrew T Hattersley,2 William E Henley1 On behalf of the MASTERMIND consortium 1Health Statistics Group, University of Exeter Medical School, Exeter, UK; 2National Institute for Health Research Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK; 3Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK Objective: Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal–survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy. Study design and setting: Participants were randomized to metformin (MFN), sulfonylurea (SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change). Results: With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture. Conclusion: Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications. Keywords: diabetes mellitus, type 2, drug-related side effects, HbA1c, hypoglycemia, joint model, precision medicine, thiazolidinediones, metformin, sulfonylurea compounds, ADOPT, edema

Adem Yesuf Dawed, Kaixin Zhou, Ewan Robert Pearson Division of Cardiovascular and Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, Scotland, UK Abstract: Type 2 diabetes is one of the leading causes of morbidity and mortality, consuming a significant proportion of public health spending. Oral hypoglycemic agents (OHAs) are the frontline treatment approaches after lifestyle changes. However, huge interindividual variation in response to OHAs results in unnecessary treatment failure. In addition to nongenetic factors, genetic factors are thought to contribute to much of such variability, highlighting the importance of the potential of pharmacogenetics to improve therapeutic outcome. Despite the presence of conflicting results, significant progress has been made in an effort to identify the genetic markers associated with pharmacokinetics, pharmacodynamics, and ultimately therapeutic response and/or adverse outcomes to OHAs. As such, this article presents a comprehensive review of current knowledge on pharmacogenetics of OHAs and provides insights into knowledge gaps and future directions. Keywords: pharmacogenetics, type 2 diabetes, oral hypoglycemic agents, pharmacokinetics, pharmacodynamics, response

Therapeutic response to metformin, a first-line drug for type 2 diabetes (T2D), is highly variable, in part likely due to genetic factors. To date, metformin pharmacogenetic studies have mainly focused on the impact of variants in metformin transporter genes, with inconsistent results. To clarify the significance of these variants in glycemic response to metformin in T2D, we performed a large-scale meta-analysis across the cohorts of the Metformin Genetics Consortium (MetGen). Nine candidate polymorphisms in five transporter genes (organic cation transporter [OCT]1, OCT2, multidrug and toxin extrusion transporter [MATE]1, MATE2-K, and OCTN1) were analyzed in up to 7,968 individuals. None of the variants showed a significant effect on metformin response in the primary analysis, or in the exploratory secondary analyses, when patients were stratified according to possible confounding genotypes or prescribed a daily dose of metformin. Our results suggest that candidate transporter gene variants have little contribution to variability in glycemic response to metformin in T2D.

OBJECTIVE: There is considerable interindividual variation in sulfonylurea response in type 2 diabetes. Transcription factor 7-like 2 (TCF7L2) variants have been identified to be strongly associated with type 2 diabetes risk, probably due to decreased beta-cell function. We hypothesized that variation in TCF7L2 would influence response to sulfonylureas but not metformin. We studied the effect of TCF7L2 rs12255372 and rs7903146 genotypes on glycemic response. RESEARCH DESIGN AND METHODS: The DARTS/MEMO (Diabetes Audit and Research Tayside/Medicines Monitoring Unit) collaboration database includes prescribing, biochemistry, and clinical phenotype of all patients with diabetes within Tayside, Scotland, from 1992. Of these, the TCF7L2 genotype was determined in 4,469 patients with type 2 diabetes recruited to GoDARTS (Genetics of Diabetes Audit and Research Tayside) between 1997 and July 2006. A total of 901 incident sulfonylurea users and 945 metformin users were identified. A logistic regression was used with treatment failure defined as an A1C >7% within 3-12 months after treatment initiation. Covariates included the TCF7L2 genotype, BMI, sex, age diagnosed, drug adherence, and drug dose. A1C pretreatment was available in a subset of patients (sulfonylurea n = 579; metformin n = 755). RESULTS: Carriers of the risk allele were less likely to respond to sulfonylureas with an odds ratio (OR) for failure of 1.95 (95% CI 1.23-3.06; P = 0.005), comparing rs12255372 T/T vs. G/G. Including the baseline A1C strengthened this association (OR 2.16 [95% CI 1.21-3.86], P = 0.009). A similar, although slightly weaker, association was seen with rs7903146. No association was seen between metformin response and either single nucleotide polymorphism, after adjustment for baseline A1C. CONCLUSIONS: TCF7L2 variants influence therapeutic response to sulfonylureas but not metformin. This study establishes that genetic variation can alter response to therapy in type 2 diabetes. [ABSTRACT FROM AUTHOR]

Heterozygous mutations in the transcription factors hepatocyte nuclear factor (HNF)-1alpha and -1beta result in MODY (maturity-onset diabetes of the young). Despite structural similarity between HNF-1alpha and -1beta, HNF-1beta mutation carriers have hyperinsulinemia, whereas HNF-1alpha mutation carriers have normal or reduced insulin concentrations. We examined whether HNF-1beta mutation carriers are insulin resistant. The endogenous glucose production rate and rate of glucose uptake were measured with a two-step, low-dose (0.3 mU . kg(-1) . min(-1)) and high-dose (1.5 mU . kg(-1) . min(-1)) hyperinsulinemic-euglycemic clamp, with an infusion of [6,6-(2)H(2)]glucose, in six subjects with HNF-1alpha mutations, six subjects with HNF-1beta mutations, and six control subjects, matched for age, sex, and BMI. Endogenous glucose production rate was not suppressed by low-dose insulin in HNF-1beta subjects but was suppressed by 89% in HNF-1alpha subjects (P = 0.004) and 80% in control subjects (P < 0.001). Insulin-stimulated glucose uptake and suppression of lipolysis were similar in all groups at low- and high-dose insulin. Subjects with HNF-1beta mutations have reduced insulin sensitivity of endogenous glucose production but normal peripheral insulin sensitivity. This is likely to reflect reduced action of HNF-1beta in the liver and possibly the kidney. This may be mediated through regulation by HNF-1beta of the key gluconeogenic enzymes glucose-6-phosphatase or PEPCK. [ABSTRACT FROM AUTHOR]

AIMS: In treating Type 2 diabetes (T2DM), UK guidelines recommend metformin in obese and overweight patients, and either sulphonylureas or metformin in normal weight patients. Although other factors influence prescribing choice, a key objective in treating T2DM is to lower plasma glucose. There is little data on how glycaemic response to oral agents varies with body mass index (BMI). Therefore, we assessed current prescribing practice and effect of BMI on glycaemic response to sulphonylureas and metformin in a large population T2DM cohort. METHODS: BMI was determined in 3856 T2DM patients on sulphonylurea or metformin monotherapy in 2001-2002. Patients were identified from the Diabetes Audit and Research in Tayside, Scotland (DARTS) database. In a linear regression, the effect of BMI and other confounders on drug response was assessed in 2064 treatment-naïve patients commencing sulphonylureas or metformin between 1994 and 2002. RESULTS: In 2001-2002, metformin was more likely to be used in obese than non-obese patients: 13% normal weight, 33.6% overweight and 62.1% obese patients were treated with metformin. Glycaemic response to sulphonylureas was not influenced by BMI (P = 0.81). Metformin was more effective in lowering glucose in those with a lower BMI (r = -0.02, P = 0.02), although the clinical impact of this was small. The HbA(1c) reduction in non-obese patients was similar to that in obese patients (1.46% vs. 1.34%, P = 0.11). CONCLUSIONS: Glycaemic response to metformin in non-obese and obese patients is similar, suggesting that an individual's BMI should not influence the choice of oral agent. Given the non-glycaemia-related benefits of metformin, it should be used in more non-obese patients than is current practice in Tayside, Scotland. [ABSTRACT FROM AUTHOR]

AIMS: Diabetic subjects with mutations in the gene encoding hepatocyte nuclear factor (HNF)-1alpha (MODY3) are prone to develop hypoglycaemia at low doses of glibenclamide, interpreted as sulphonylurea hypersensitivity. The present study was undertaken to compare the plasma insulin responses to glucose and tolbutamide in HNF-1alpha mutation carriers with those of healthy control subjects. METHODS: Seven mutation carriers; three normoglycaemic, two with impaired glucose tolerance, and two with newly detected diabetes, underwent an oral glucose tolerance test and a tolbutamide-modified intravenous glucose tolerance test with measurements of plasma insulin. Twenty-two healthy subjects served as controls. RESULTS: The plasma insulin response to intravenous glucose was reduced in the HNF-1alpha mutation carriers compared to the control subjects, with an area under the curve (median (interquartile range)) of 812 min pmol/l (421, 1647) and 1933 min pmol/l (1521, 2908), respectively (P = 0.03). In striking contrast, the plasma insulin response to tolbutamide was preserved, with an area under the curve of 2109 min pmol/l (1126, 3172) and 2250 min pmol/l (1614, 3276) in the mutation carriers and control subjects, respectively. CONCLUSIONS: HNF-1alpha mutation carriers are characterized by preserved tolbutamide-induced insulin secretion. Compared to healthy subjects, our MODY3 individuals did not show any increased serum insulin response to tolbutamide, suggesting that HNF-1alpha mutation carriers are not characterized by sulphonylurea hypersensitivity. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes shows evidence of underlying heterogeneity. No studies have assessed whether different causes for diabetes change the response to oral hypoglycaemic therapy. In a few cases, patients with diabetes caused by mutations in the hepatocyte nuclear factor 1alpha (HNF-1alpha) gene have been described as sensitive to the hypoglycaemic effects of sulphonylureas. We aimed to see whether the glycaemic response to the sulphonylurea gliclazide and the biguanide metformin differed in HNF-1alpha diabetes and type 2 diabetes, and to investigate the mechanism for differences in sulphonylurea sensitivity.Methods: We did a randomised crossover trial of glicazide and metformin in 36 patients, either with diabetes caused by HNF-1alpha mutations or type 2 diabetes, who were matched for body-mass index and fasting plasma glucose. The primary outcome was reduction in fasting plasma glucose. Analysis was by intention to treat. We assessed possible mechanisms for sulphonylurea sensitivity through insulin sensitivity, insulin secretory response to glucose and tolbutamide, and tolbutamide clearance.Findings: Patients with HNF-1alpha diabetes had a 5.2-fold greater response to gliclazide than to metformin (fasting plasma glucose reduction 4.7 vs 0.9 mmol/L, p=0.0007) and 3.9-fold greater response to gliclazide than those with type 2 diabetes (p=0.002). Patients with HNF-1alpha diabetes had a strong insulin secretory response to intravenous tolbutamide despite a small response to intravenous glucose, and were more insulin sensitive than those with type 2 diabetes. Sulphonylurea metabolism was similar in both patient groups.Interpretation: The cause of hyperglycaemia changes the response to hypoglycaemic drugs; HNF-1alpha diabetes has marked sulphonylurea sensitivity. This pharmacogenetic effect is consistent with models of HNF-1alpha deficiency, which show that the beta-cell defect is upstream of the sulphonylurea receptor. Definition of the genetic basis of hyperglycaemia has implications for patient management. [ABSTRACT FROM AUTHOR]

Key words: cortical blindness; hypertensive encephalopathy; nephrotic syndrome; tibolone [ABSTRACT FROM PUBLISHER]

Type 2 diabetes (T2D) is a complex condition characterised by the interaction between insulin resistance and beta cell dysfunction. C-peptide, a key biomarker of endogenous insulin secretion, has a role in diagnosing type 1 diabetes (T1D). However, its utility in T2D has not been extensively studied. This review provides an overview of the progression of C-peptide levels over time in T2D and discuss its interpretation in clinical settings. We reviewed current evidence on the relationship between C-peptide levels and response to antidiabetic drugs, as well as the utility of C-peptide testing in T2D treatment strategies. We also reviewed available evidence for C-peptide in predicting future outcomes in T2D. In this review, we hoped to clarify the value of C-peptide testing in understanding and managing T2D and to highlight areas where further research is needed., (© 2025 The Author(s). Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Background: Multiple long-term conditions (MLTCs; commonly referred to as multimorbidity) are highly prevalent among people admitted to hospital and are therefore of critical importance to hospital-based healthcare systems. To date, most research on MLTCs has been conducted in primary care or the general population with comparatively little work undertaken in the hospital setting., Purpose: To describe the rationale and content of ADMISSION: a four-year UK Research and Innovation and National Institute of Health and Care Research funded interdisciplinary programme that seeks, in partnership with public contributors, to transform care for people living with MLTCs admitted to hospital., Research Design: Based across five UK academic centres, ADMISSION combines expertise in clinical medicine, epidemiology, informatics, computing, biostatistics, social science, genetics and care pathway mapping to examine patterns of conditions, mechanisms, consequences and pathways of care for people with MLTCs admitted to hospital., Data Collection: The programme uses routinely collected electronic health record data from large UK teaching hospitals, population-based cohort data from UK Biobank and routinely collected blood samples from The Scottish Health Research Register and Biobank (SHARE). These approaches are complemented by focused qualitative work exploring the perspectives of healthcare professionals and the lived experience of people with MLTCs admitted to hospital., Conclusion: ADMISSION will provide the necessary foundations to develop novel ways to prevent and treat MLTCs and their consequences in people admitted to hospital and to improve care systems and the quality of care for this underserved group., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: ES reports funding support from HDRUK, MRC, Wellcome Trust, NIHR, Alpha 1 Foundation, EPSRC and British Lung Foundation. SG reports funding support from HDRUK, MRC and NIHR. MDW reports consulting fees from Rejuvenate Biomed. In addition to the support all authors acknowledge from the ADMISSION research collaborative, MDW, JGB, SJH, MO, CP, SJR, SMR, , RC and AAS also acknowledge support from the National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre based at Newcastle upon Tyne Hospitals NHS Foundation Trust, Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust and Newcastle University. SHa, MSi and DC are supported by the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre. JMSW is funded by a NIHR Research Professorship (NIHR301614). RC also receives support as part of a generous donation made by the McArdle family to Newcastle University for research that will benefit the lives of older people in the UK. MDW, SP and JS acknowledge support from the NIHR Newcastle HealthTech Research Centre. SJR holds an NIHR Clinical Lectureship, funded by Health Education England (HEE) and NIHR. The other authors have no conflicts of interest to declare., (© The Author(s) 2025.)

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10 -10 ; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10 -14 ). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4-15 additional correct interventions and 37-135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested., Competing Interests: Competing interests: T.S. and C.D. are employees and shareholders of Novo Nordisk. M.F.G. has received financial and nonfinancial (in kind) support from Boehringer Ingelheim Pharma GmbH, JDRF International, Eli Lilly, AbbVie, Sanofi-Aventis, Astellas, Novo Nordisk A/S and Bayer AG, within European Union grant H2020-JTI-lMl2-2015-05 (grant agreement no. 115974 - BEAt-DKD). She has also received financial and in kind support from Novo Nordisk, Pfizer, Follicum, Coegin Pharma, Abcentra, Probi and Johnson & Johnson, within a project funded by the Swedish Foundation for Strategic Research on precision medicine in diabetes (LUDC-IRC, grant no. 15-0067). M.F.G. has received personal consultancy fees from Lilly and Tribune Therapeutics AB. M.P.-N. is an employee of Boehringer Ingelheim. Outside the submitted work, P.S.W. has received consulting fees from Astra Zeneca, research funding from Bayer AG, research funding, consulting and lecturing fees from Bayer Health Care, lecturing fees from Bristol Myers Squibb, research funding and consulting fees from Boehringer Ingelheim, research funding and consulting fees from Daiichi Sankyo Europe, consulting fees and nonfinancial support from Diasorin, nonfinancial research support from I.E.M., research funding and consulting fees from Novartis Pharma, lecturing fees from Pfizer Pharma, nonfinancial grants from Philips Medical Systems, and research funding and consulting fees from Sanofi-Aventis. C.L.R. reports grants from the Irish Research Council, Science Foundation Ireland, Anabio and the Health Research Board. He serves on advisory boards and speakers panels of Novo Nordisk, Roche, Herbalife, GI Dynamics, Eli Lilly, Johnson & Johnson, Glia, Irish Life Health, Boehringer Ingelheim, Currax, Zealand Pharma, Keyron, Astra Zeneca and Rhythm Pharma. C.L.R. is a member of the Irish Society for Nutrition and Metabolism outside the area of work commented on here. C.L.R. provides obesity clinical care in the My Best Weight clinic and Beyond BMI clinic and is a shareholder in these clinics. He was the chief medical officer and director of the Medical Device Division of Keyron in 2021. Both of these are unremunerated positions. C.L.R. was a previous investor in Keyron, which develops endoscopically implantable medical devices intended to mimic the surgical procedures of sleeve gastrectomy and gastric bypass. No patients have been included in any of Keyron’s studies and they are not listed on the stock market. C.L.R. was gifted stock holdings in September 2021 and divested all stock holdings in Keyron in September 2021. He continues to provide scientific advice to Keyron for no remuneration. Outside the submitted work, E.R.P. has received honoraria from Novo Nordisk, Lilly and Illumina. Within the past five years, P.W.F. has received consulting honoraria from Eli Lilly Inc., Novo Nordisk Foundation, Novo Nordisk A/S, UBS and Zoe Ltd, has been an employee of the Novo Nordisk Foundation, and has been on advisory boards for the Danish Diabetes and Endocrine Academy, Novo Nordisk A/S, Hamad Medical Corporation and Zoe Ltd. P.W.F. has also received investigator-initiated grants (paid to institution) from numerous pharmaceutical companies as part of the Innovative Medicines Initiative of the European Union. The other authors declare no competing interests., (© 2024. The Author(s).)

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score., Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry. The cohort was divided into training (70%), validation (10%) and testing (20%) cohorts. Clinical 10-year CVD risk was calculated using the pooled cohort equation (PCE) risk score. A polygenic risk score (PRS) for coronary heart disease was also obtained. Retinal images were analysed using an EfficientNet-B2 network to predict 10-year CVD risk. The primary outcome was time to first major adverse CV event (MACE) including CV death, myocardial infarction or stroke., Results: 1241 individuals were included in the test cohort (mean PCE 10-year CVD risk 35%). There was a strong correlation between retinal predicted CVD risk and the PCE risk score (r = 0.66) but not the polygenic risk score (r = 0.05). There were 288 MACE events. Higher retina-predicted risk was significantly associated with increased 10-year risk of MACE (HR 1.05 per 1% increase; 95% CI 1.04-1.06, p < 0.001) and remained so after adjustment for the PCE and polygenic risk score (HR 1.03; 95% CI 1.02-1.04, p < 0.001). The retinal risk score had similar performance to the PCE (both AUC 0.697) and when combined with the PCE and polygenic risk score had significantly improved performance compared to the PCE alone (AUC 0.728). An increase in retinal-predicted risk within 3 years was associated with subsequent increased MACE likelihood., Conclusions: A deep-learning AI model could accurately predict MACE from routine retinal screening photographs with a comparable performance to traditional clinical risk assessment in a diabetic cohort. Combining the AI-derived retinal risk prediction with a coronary heart disease polygenic risk score improved risk prediction. AI retinal assessment might allow a one-stop CVD risk assessment at routine retinal screening., Competing Interests: Declarations. Ethics approval and consent to participate: Data analysis was conducted within the Health Informatics Centre Trusted Research Environment under its overarching ethics approval for approved researchers to conduct research within their secure environment (East of Scotland Research Ethics Committee reference: 18/ES/0126). As the study data are de-identified, consent from individual patients was not required. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)

Objective: The recent availability of cardiovascular risk-reducing type 2 diabetes (T2D) therapies makes it imperative to optimally identify individuals who could derive benefit. Current clinical risk prediction may misclassify individuals as low risk and could be improved. Our aim was to determine the incremental prognostic value of a coronary heart disease (CHD) genome-wide polygenic risk score (PRS) to a clinical risk score in prediction of major adverse cardiovascular events (MACE) in patients with T2D., Research Design and Methods: We evaluated 10,556 individuals with T2D aged 40-79 years without a prior cardiovascular hospitalization. We calculated 10-year clinical cardiovascular risk at the date of recruitment using the Pooled Cohort Equation (PCE Risk) and constructed a CHD PRS. The primary outcome was time to first MACE incidence, and we assessed the additional incremental predictive value of the CHD PRS to the PCE risk., Results: At 10 years, there were 1,477 MACE. After adjustment for clinical risk, the CHD PRS was significantly associated with MACE (hazard ratio [HR] 1.69 per SD increase, 95% CI 1.60-1.79). Individuals with PCE Risk <7.5% but in the top quintile of CHD PRS had a significantly increased likelihood of MACE (HR 10.69, 95% CI 5.07-22.55) compared with those in the lowest. The addition of the PRS to the clinical risk score led to significant improvements in risk prediction, particularly in those at low clinical risk., Conclusions: The addition of a CHD PRS to clinical assessment improved MACE prediction in T2D individuals without prior cardiovascular disease, particularly in those at low clinical risk., (© 2024 by the American Diabetes Association.)

Aims/hypothesis: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes., Methods: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively., Results: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA 1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes., Conclusions/interpretation: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions., Competing Interests: Acknowledgements: We extend our gratitude to the IMI-DIRECT study participants who willingly participated in phenotyping as well as to the clinical and technical staff across European study centres for their contributions to participant recruitment and clinical assessment. This publication’s development has been supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement 115317 (DIRECT), with resources derived from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contributions. Special thanks go to the study centre team, L. M. 't Hart, F. Rutters, J. Vangipurapu and T. H. Hansen, for providing internal review on this manuscript. Data availability: Access to the molecular and clinical raw data, as well as the processed data, is restricted. This is in accordance with the informed consent provided by study participants, the various national ethical approvals obtained for the study, and compliance with the European General Data Protection Regulation (GDPR). Individual-level clinical and molecular data cannot be transferred from the centralised IMI-DIRECT repository. Requests for access will receive guidance on accessing data through the DIRECT secure analysis platform after submitting an appropriate application. The IMI-DIRECT data access policy and additional information about the IMI-DIRECT research consortium’s initiatives and activities can be found at https://directdiabetes.org . Code used for MR in the study is included as ESM. Funding: Open Access funding enabled and organized by Projekt DEAL. We would like to thank Helmholtz Munich, German Diabetes Center (DZD) for their support in current research and China Research Council (CRC) funding for a PhD student hosted by Helmholtz Munich. Authors’ relationships and activities: The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: SS, QD, MH, JA and HG conceptualised the analysis plan. RWK, PWF, MW, IF, GG, IP, HR, MD, MM, OP, JS, KT, FDM, SB, AV, AM, TM, TK, JA, EP and HG were involved in conception and design of the DIRECT study. SS, QD, MH, JA, GK, AA, CP, RB, JFT, AJ and AT were involved in the data acquisition, pre-processing and interpretation of data. SS organised inclusion of outlined sections and, along with QD, wrote the original draft of the manuscript. All authors contributed to drafting the article or critically revising it for significant intellectual content and have provided approval to the final version to be published. SS and HG are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis., (© 2024. The Author(s).)

Background: This study estimated to what extent the number of measurements of cardiometabolic risk factors (e.g., blood pressure, cholesterol, glycated haemoglobin) were impacted by the COVID-19 pandemic and whether these have recovered to expected levels., Methods and Findings: A cohort of individuals aged ≥18 years in England with records in the primary care-COVID-19 General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) were identified. Their records of 12 risk factor measurements were extracted between November 2018 and March 2024. Number of measurements per 1,000 individuals were calculated by age group, sex, ethnicity, and area deprivation quintile. The observed number of measurements were compared to a composite expectation band, derived as the union of the 95% confidence intervals of 2 estimates: (1) a projected trend based on data prior to the COVID-19 pandemic; and (2) an assumed stable trend from before pandemic. Point estimates were calculated as the mid-point of the expectation band. A cohort of 49,303,410 individuals aged ≥18 years were included. There was sharp drop in all measurements in March 2020 to February 2022, but overall recovered to the expected levels during March 2022 to February 2023 except for blood pressure, which had prolonged recovery. In March 2023 to March 2024, blood pressure measurements were below expectation by 16% (-19 per 1,000) overall, in people aged 18 to 39 (-23%; -18 per 1,000), 60 to 79 (-17%; -27 per 1,000), and ≥80 (-31%; -57 per 1,000). There was suggestion that recovery in blood pressure measurements was socioeconomically patterned. The second most deprived quintile had the highest deviation (-20%; -23 per 1,000) from expectation compared to least deprived quintile (-13%; -15 per 1,000)., Conclusions: There was a substantial reduction in routine measurements of cardiometabolic risk factors following the COVID-19 pandemic, with variable recovery. The implications for missed diagnoses, worse prognosis, and health inequality are a concern., Competing Interests: NS has consulted for and/or received speaker honoraria from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and received grant support paid to his University from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. JV was National Clinical Director for Diabetes and Obesity at NHS England from 2013 to September 2023. JH received research grants from Amgen, British Heart Foundation, Health and Care Research Wales, and speaker honorarium from Amgen. CD is the Treasurer of European Council for Cardiovascular Research, Association of Physicians of Great Britain and Ireland, Council Member of the European Society of Hypertension, and the Vice President of Scottish Heart & Arterial disease Risk Prevention (SHARP). All other authors declared no potential conflicts of interests., (Copyright: © 2024 Ho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rationale & Objective: Despite a lack of supporting evidence, current guidance recommends against the use of metformin in people with advanced kidney impairment. This observational study compared the outcomes of patients with type 2 diabetes who continued versus stopped metformin after developing stage 4 chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR]<30mL/min/1.73m 2 )., Study Design: Nationwide observational cohort study., Setting & Participants: All adults with type 2 diabetes and incident stage 4 CKD in Scotland who were treated with metformin between January 2010 and April 2019., Exposure: Stopping versus continuing metformin within 6 months following incident stage 4 CKD., Outcome: Primary outcome was all-cause mortality. Secondary outcomes included major adverse cardiovascular events (MACE)., Analytical Approach: Target trial emulation with clone-censor-weight design and marginal structural models fit for sensitivity analyses., Results: In a population of 371,742 Scottish residents with a diagnosis of type 2 diabetes before April 30, 2019, 4,278 were identified as prevalent metformin users with incident CKD stage 4. Within 6 months of developing CKD stage IV, 1,713 (40.1%) individuals discontinued metformin. Compared with continuing metformin, stopping metformin was associated with a lower 3-year survival (63.7% [95% CI, 60.9-66.6] vs 70.5% [95% CI, 68.0-73.0]; HR, 1.26 [95% CI, 1.10-1.44]), and the incidence of MACE was similar between both strategies (HR, 1.05 [95% CI, 0.88-1.26]). Marginal structural models confirmed the higher risk of all-cause mortality and similar risk of MACE in patients who stopped versus continued metformin (all-cause mortality: HR, 1.34 [95% CI, 1.08-1.67]; MACE: HR, 1.04 [95% CI, 0.81-1.33])., Limitations: Residual confounding., Conclusions: The continued use of metformin may be appropriate when eGFR falls below 30mL/min/1.73m 2 . Randomized controlled trials are needed to confirm these findings., Plain-Language Summary: Current guidance recommends against the use of metformin in people with advanced kidney impairment despite a lack of evidence. It is therefore currently unclear how the decision to stop versus continue metformin in patients who reach stage 4 CKD impacts their risk of mortality and cardiovascular events. This study showed that stopping metformin after reaching stage 4 CKD was associated with reduced survival that did not appear to be mediated by an increase in adverse cardiovascular outcomes. These findings may support the continued use of metformin in patients with advanced kidney impairment, but further research is needed to confirm these observations., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

We investigated whether characterization of full-length GAD (f-GADA) antibody (GADA) responses could identify early insulin requirement in adult-onset diabetes. In 179 f-GADA-positive participants diagnosed with type 2 diabetes, we assessed associations of truncated GADA (t-GADA) positivity, f-GADA IgG subclasses, and f-GADA affinity with early insulin requirement (<5 years), type 1 diabetes genetic risk score (T1D GRS), and C-peptide. t-GADA positivity was lower in f-GADA-positive without early insulin in comparison with f-GADA-positive type 2 diabetes requiring insulin within 5 years, and T1D (75% vs. 91% and 95% respectively, P < 0.0001). t-GADA positivity (in those f-GADA positive) identified a group with a higher T1D genetic susceptibility (mean T1D GRS 0.248 vs. 0.225, P = 0.003), lower C-peptide (1,156 pmol/L vs. 4,289 pmol/L, P = 1 × 10-7), and increased IA-2 antigen positivity (23% vs. 6%, P = 0.03). In survival analysis, t-GADA positivity was associated with early insulin requirement compared with those only positive for f-GADA, independently from age of diagnosis, f-GADA titer, and duration of diabetes (adjusted hazard ratio 5.7 [95% CI 1.4, 23.5], P = 0.017). The testing of t-GADA in f-GADA-positive individuals with type 2 diabetes identifies those who have genetic and clinical characteristics comparable to T1D and stratifies those at higher risk of early insulin requirement., (© 2024 by the American Diabetes Association.)

Aims/hypothesis: Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change., Methods: We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA 1c change., Results: A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m 2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA 1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA 1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years., Conclusions/interpretation: Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA 1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity., (© 2024. The Author(s).)

Aims/hypothesis: Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis., Methods: Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument., Results: Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA 1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3])., Conclusions/interpretation: Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes., (© 2024. The Author(s).)

Context: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood., Objective: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D., Methods: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D., Results: Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes., Conclusion: These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

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 < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability., Conclusions: In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes., (© 2024. The Author(s).)

Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA 1c , HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed., Results: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%., Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Information on BMI and risk of developing hypertension in type 1 diabetes (T1D) is scarce, and it comes mostly from cross-sectional analyses. This study underscores a risk of developing hypertension in T1D individuals with high BMI, and this risk appears to be higher than in those with type 2 diabetes., Competing Interests: Declaration of competing interest The authors declare no potential conflict of interest relevant to this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value., Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c , HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic., Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c ) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance., Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification., Data Availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch ., (© 2024. The Author(s).)

Aims/hypothesis: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA)., Methods: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA 1c ) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events., Results: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA 1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA 1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications., Conclusions/interpretation: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries., (© 2024. The Author(s).)

Objective: The impact of CYP2C19 genotype on clopidogrel outcomes is one of the most well established pharmacogenetic interactions, supported by robust evidence and recommended by the Food and Drug Administration and clinical pharmacogenetics implementation consortium. However, there is a scarcity of large-scale real-world data on the extent of this pharmacogenetic effect, and clinical testing for the CYP2C19 genotype remains infrequent. This study utilizes the UK Biobank dataset, including 10 365 patients treated with clopidogrel, to offer the largest observational analysis of these pharmacogenetic effects to date., Methods: Incorporating time-varying drug exposure and repeated clinical outcome, we adopted semiparametric frailty models to detect and quantify exposure-based effects of CYP2C19 (*2,*17) variants and nongenetic factors on the incidence risks of composite outcomes of death or recurrent hospitalizations due to major adverse cardiovascular events (MACE) or hemorrhage in the entire cohort of clopidogrel-treated patients., Results: Out of the 10 365 clopidogrel-treated patients, 40% (4115) experienced 10 625 MACE events during an average follow-up of 9.23 years. Individuals who received clopidogrel (coverage >25%) with a CYP2C19*2 loss-of-function allele had a 9.4% higher incidence of MACE [incidence rate ratios (IRR), 1.094; 1.044-1.146], but a 15% lower incidence of hemorrhage (IRR, 0.849; 0.712-0.996). These effects were stronger with high clopidogrel exposure. Conversely, the gain-of-function CYP2C19*17 variant was associated with a 5.3% lower incidence of MACE (IRR, 0.947; 0.903-0.983). Notably, there was no evidence of *2 or *17 effects when clopidogrel exposure was low, confirming the presence of a drug-gene interaction., Conclusion: The impact of CYP2C19 on clinical outcomes in clopidogrel-treated patients is substantial, highlighting the importance of incorporating genotype-based prescribing into clinical practice, regardless of the reason for clopidogrel use or the duration of treatment. Moreover, the methodology introduced in this study can be applied to further real-world investigations of known drug-gene and drug-drug interactions and the discovery of novel interactions., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Type 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., Methods: Circulating 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., Results: From 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., Conclusions: Using 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., Competing Interests: GR has received grant funding from, and is a consultant for, Sun Pharmaceuticals Inc. KS is CEO of Lipotype. KS and CK are shareholders of Lipotype. MG is an employee of Lipotype. MH is an employee of Janssen Research & Development. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Li, Dragan, Tran, Fung, Kuznetsov, Hansen, Beulens, Hart, Slieker, Donnelly, Gerl, Klose, Mehl, Simons, Elders, Pearson, Rutter and Ibberson.)

Background: Heterogeneity in type 2 diabetes can be represented by a tree-like graph structure by use of reversed graph-embedded dimensionality reduction. We aimed to examine whether this approach can be used to stratify key pathophysiological components and diabetes-related complications during longitudinal follow-up of individuals with recent-onset type 2 diabetes., Methods: For this cohort analysis, 927 participants aged 18-69 years from the German Diabetes Study (GDS) with recent-onset type 2 diabetes were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualised for age and sex. Insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, insulin secretion was assessed by intravenous glucose tolerance test, hepatic lipid content was assessed by 1 H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 were assessed by ELISA, and peripheral and autonomic neuropathy were assessed by functional and clinical measures. Participants were followed up for up to 16 years. We also investigated heart failure and all-cause mortality in 794 individuals with type 2 diabetes undergoing invasive coronary diagnostics from the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort., Findings: There were gradients of clamp-measured insulin sensitivity (both dimensions: p<0·0001) and insulin secretion (p dim1 <0·0001, p dim2 =0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, p dim1 <0·0001, p dim2 =0·037), pro-inflammatory biomarkers (IL-6: n=348, p dim1 <0·0001, p dim2 =0·013; IL-18: n=350, p dim1 <0·0001, p dim2 =0·38), and elevated cardiovascular risk (n events =143, p dim1 =0·14, p dim2 <0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (n events =85, p dim1 =0·032, p dim2 =0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (n events =184, p dim1 =0·012, p dim2 =0·044) and cardiac autonomic neuropathy (n events =118, p dim1 =0·0094, p dim2 =0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (n events =488, p dim1 =0·12, p dim2 =0·0032). Significant gradients differentiated individuals having heart failure with preserved ejection fraction from those who had heart failure with reduced ejection fraction., Interpretation: These data define the pathophysiological underpinnings of the tree structure, which has the potential to stratify diabetes-related complications on the basis of routinely available variables and thereby expand the toolbox of precision diabetes diagnosis., Funding: German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, European Community, German Research Foundation, and Schmutzler Stiftung., (Copyright © 2024 Elsevier Ltd. All rights reserved.)