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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.

Aims/hypothesisHuman complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes.MethodsThe study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m(2) and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p 1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 × 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10(-10)).Conclusions/interpretationWe applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.

Objective: Recently, results from a meta-analysis of genome-wide association studies have yielded a number of novel type 2 diabetes loci. However, conflicting results have been published regarding their effects on insulin secretion and insulin sensitivity. In this study we used hyperglycemic clamps with three different stimuli to test associations between these novel loci and various measures of beta-cell function.Research Design and Methods: For this study, 336 participants, 180 normal glucose tolerant and 156 impaired glucose tolerant, underwent a 2-h hyperglycemic clamp. In a subset we also assessed the response to glucagon-like peptide (GLP)-1 and arginine during an extended clamp (n = 123). All subjects were genotyped for gene variants in JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2/ADAMS30, DCD, VEGFA, BCL11A, HNF1B, WFS1, and MTNR1B.Results: Gene variants in CDC123/CAMK1D, ADAMTS9, BCL11A, and MTNR1B affected various aspects of the insulin response to glucose (all P < 6.9 x 10(-3)). The THADA gene variant was associated with lower beta-cell response to GLP-1 and arginine (both P < 1.6 x 10(-3)), suggesting lower beta-cell mass as a possible pathogenic mechanism. Remarkably, we also noted a trend toward an increased insulin response to GLP-1 in carriers of MTNR1B (P = 0.03), which may offer new therapeutic possibilities. The other seven loci were not detectably associated with beta-cell function.Conclusions: Diabetes risk alleles in CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B are associated with various specific aspects of beta-cell function. These findings point to a clear diversity in the impact that these various gene variants may have on (dys)function of pancreatic beta-cells. [ABSTRACT FROM AUTHOR]

The short-chain l-3-hydroxyacyl-CoA dehydrogenase (SCHAD) protein is involved in the penultimate step of mitochondrial fatty acid oxidation. Previously, it has been shown that mutations in the corresponding gene (HADHSC) are associated with hyperinsulinism in infancy. The presumed function of the SCHAD enzyme in glucose-stimulated insulin secretion led us to the hypothesis that common variants in HADHSC on chromosome 4q22-26 might be associated with development of type 2 diabetes. In this study, we have performed a large-scale association study in four different cohorts from the Netherlands and Denmark (n = 7,365). Direct sequencing of HADHSC cDNA and databank analysis identified four tagging single nucleotide polymorphisms (SNPs) including one missense variant (P86L). Neither the SNPs nor haplotypes investigated were associated with the disease, enzyme function, or any relevant quantitative measure (all P > 0.1). The present study provides no evidence that the specific HADHSC variants or haplotypes examined do influence susceptibility to develop type 2 diabetes. We conclude that it is unlikely that variation in HADHSC plays a major role in the pathogenesis of type 2 diabetes in the examined cohorts. [ABSTRACT FROM AUTHOR]

Objective: To evaluate metabolic effects of sex steroids in nonfasting and fasting conditions, independent from changes in body composition.Research Design and Methods: A randomized clinical trial was performed to create contrasting sex steroid levels in healthy young men: by letrozole (aromatase inhibitor) to lower estradiol (E(2)) and increase testosterone (group T, n = 10) versus letrozole plus E(2) patches to lower T and raise E(2) (group E, n = 10). Mixed meals and hyperinsulinemic-euglycemic clamps were performed before and after a 1-week treatment period.Results: Following intervention, the postprandial triglyceride response displayed a diverging response with a decline in group T and an increase in group E; the postprandial glucose-dependent insulinotropic polypeptide (GIP) response increased in group T. Insulin sensitivity increased in group T but remained unaltered in group E.Conclusions: In healthy young men, short-term changes in sex steroids affect postprandial triglyceride and GIP response and insulin sensitivity. [ABSTRACT FROM AUTHOR]

Aim: To uncover differences in small non-coding RNAs (sncRNAs) in individuals with type 2 diabetes (T2D) categorized into five clusters based on individual characteristics, which may aid in the identification of those prone to rapid progression., Materials and Methods: In the Hoorn Diabetes Care System (DCS) cohort, participants were clustered by age, body mass index (BMI), and glycated haemoglobin, C-peptide and high-density lipoprotein (HDL) cholesterol levels, yielding severe insulin-deficient diabetes, severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes, and mild diabetes with high HDL cholesterol clusters (n = 412). Utilizing plasma sncRNA-sequencing, we identified distinct cluster-specific sncRNAs. Validation was performed in a smaller DCS Hoorn dataset (n = 138). To elucidate their potential functions, we examined tissue expression, identified potential targets or (co-)regulated proteins, conducted gene set enrichment analyses on the targets through Reactome, and examined tissue expression of the (co-)regulated proteins., Results: The insulin-resistant cluster exhibited aberrant expression of 10 sncRNAs, while the high BMI cluster featured eight differentially expressed sncRNAs. Multiple (co-)regulated proteins were identified for sncRNAs associated with both clusters. Proteins associated with both clusters showed enrichment for metabolism. Proteins that specifically and only associated with the SIRD cluster showed enrichment for immune-related signalling. Furthermore, MOD cluster-specific associated proteins showed enrichment for the complement system., Conclusions: Our research showed differential sncRNA levels among type 2 diabetes clusters. This may reflect and could deepen our understanding of molecular mechanisms, in development, progression, and risk factors for each cluster., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Introduction: Micro- and macrovascular complications are common among persons with type 2 diabetes. Recently there has been growing interest to investigate the potential of circulating small non-coding RNAs (sncRNAs) as contributors to the development of diabetic complications. In this study we investigate to what extent circulating sncRNAs levels associate with prevalent diabetic kidney disease (DKD) in persons with type 2 diabetes., Methods: Plasma sncRNAs levels were determined using small RNA-seq, allowing detection of miRNAs, snoRNAs, piRNAs, tRNA fragments, and various other sncRNA classes. We tested for differentially expressed sncRNAs in persons with type 2 diabetes, with DKD (n = 69) or without DKD (n = 405). In secondary analyses, we also tested the association with eGFR, albuminuria (UACR), and the plasma proteome., Results: In total seven sncRNAs were negatively associated with prevalent DKD (all P FDR  ≤ 0.05). Including one microRNA (miR-143-5p), five snoRNAs (U8, SNORD118, SNORD24, SNORD107, SNORD87) and a piRNA (piR-019825 | DQ597218). Proteomic analyses showed that the seven sncRNAs, and especially the piRNA piR-019825, were associated with plasma levels of 24 proteins of which several have known associations with kidney function including TNF sR-I (TNFRFS1A), DAN (NBL1) and cystatin C (CST3)., Conclusion: We have identified novel small non-coding RNAs, primarily from classes other than microRNAs, that are associated with diabetic kidney disease. Our results show that the involvement of small non-coding RNAs in DKD goes beyond the already known microRNAs and also involves other classes of sncRNA, in particular snoRNAs and the piRNA piR-019825, that have never been studied before in relation to kidney function., (© 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).)

Apolipoprotein-CIII (apo-CIII) inhibits the clearance of triglycerides from circulation and is associated with an increased risk of diabetes complications. It exists in four main proteoforms: O-glycosylated variants containing either zero, one, or two sialic acids and a non-glycosylated variant. O-glycosylation may affect the metabolic functions of apo-CIII. We investigated the associations of apo-CIII glycosylation in blood plasma, measured by mass spectrometry of the intact protein, and genetic variants with micro- and macrovascular complications (retinopathy, nephropathy, neuropathy, cardiovascular disease) of type 2 diabetes in a DiaGene study ( n = 1571) and the Hoorn DCS cohort ( n = 5409). Mono-sialylated apolipoprotein-CIII (apo-CIII 1 ) was associated with a reduced risk of retinopathy (β = -7.215, 95% CI -11.137 to -3.294) whereas disialylated apolipoprotein-CIII (apo-CIII 2 ) was associated with an increased risk (β = 5.309, 95% CI 2.279 to 8.339). A variant of the GALNT2 -gene (rs4846913), previously linked to lower apo-CIII 0a , was associated with a decreased prevalence of retinopathy (OR = 0.739, 95% CI 0.575 to 0.951). Higher apo-CIII 1 levels were associated with neuropathy (β = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII 0a with macrovascular complications (β = -9.195, 95% CI -15.847 to -2.543). In conclusion, apo-CIII glycosylation was associated with the prevalence of micro- and macrovascular complications of diabetes. Moreover, a variant in the GALNT2 -gene was associated with apo-CIII glycosylation and retinopathy, suggesting a causal effect. The findings facilitate a molecular understanding of the pathophysiology of diabetes complications and warrant consideration of apo-CIII glycosylation as a potential target in the prevention of diabetes complications.

Aim: To investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal., Materials and Methods: The Hoorn Diabetes Care System cohort is a large prospective cohort consisting of individuals with type 2 diabetes from the northwest part of the Netherlands. In this cohort we assessed the association of baseline plasma levels of 172 metabolites with incident (N total  = 462/N case  = 81) and prevalent (N total  = 1247/N case  = 120) CKD using logistic regression. Additionally, replication in the UK Biobank, body mass index (BMI) mediation and causality of the association with Mendelian randomization was performed., Results: Elevated levels of total and individual branched-chain amino acids (BCAAs)-valine, leucine and isoleucine-were associated with an increased risk of incident CKD, but with reduced odds of prevalent CKD, where BMI was identified as an effect modifier. The observed inverse effects were replicated in the UK Biobank. Mendelian randomization analysis did not provide evidence for a causal relationship between BCAAs and prevalent CKD., Conclusions: Our study shows the intricate relationship between plasma BCAA levels and CKD in individuals with type 2 diabetes. While an association exists, its manifestation varies based on disease status and BMI, with no definitive evidence supporting a causal link between BCAAs and prevalent CKD., (© 2024 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

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: The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers., Methods: From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m 2 ) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts., Results: At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts., Conclusions/interpretation: Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification., (© 2024. The Author(s).)

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ( DSM-5 ) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5 -based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

People with type 2 diabetes have a tenfold higher prevalence of hypomagnesemia, which is suggested to be caused by low dietary magnesium intake, medication use, and genetics. This study aims to identify the genetic loci that influence serum magnesium concentration in 3466 people with type 2 diabetes. The GWAS models were adjusted for age, sex, eGFR, and HbA1c. Associated traits were identified using publicly available data from GTEx consortium, a human kidney eQTL atlas, and the Open GWAS database. The GWAS identified a genome-wide significant locus in TAF3 (p = 2.9 × 10 -9 ) in people with type 2 diabetes. In skeletal muscle, loci located in TAF3 demonstrate an eQTL link to ATP5F1C, a gene that is involved in the formation of Mg 2+ -ATP. Serum Mg 2+ levels were associated with MUC1/TRIM46 (p = 2.9 × 10 -7 ), SHROOM3 (p = 4.0 × 10 -7 ), and SLC22A7 (p = 1.0 × 10 -6 ) at nominal significance, which is in combination with the eQTL data suggesting that they are possible candidates for renal failure. Several genetic loci were in agreement with previous genomic studies which identified MUC1/TRIM46 (P meta  = 6.9 × 10 -29 , P Q  = 0.81) and SHROOM3 (P meta  = 2.9 × 10 -27 , P Q  = 0.04) to be associated with serum Mg 2+ in the general population. In conclusion, serum magnesium concentrations are associated with genetic variability around the regions of TAF3, MUC1/TRIM46, SHROOM3, and SLC22A7 in type 2 diabetes., (© 2024. The Author(s).)

Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII 0a ), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII 0c , apo-CIII 1 and apo-CIII 2 ). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes ( n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System ( n = 5409). Rs4846913-A, in the GALNT2 -gene, was associated with decreased apo-CIII 0a . This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII 0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172 -gene, was associated with decreased apo-CIII 2 and with hypertriglyceridemia. In line, apo-CIII 2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2 -gene plays a major role i O -glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172 / NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O -glycosylation in health and disease.

Aims/hypothesis: Inflammation is important in the development of type 2 diabetes complications. The N-glycosylation of IgG influences its role in inflammation. To date, the association of plasma IgG N-glycosylation with type 2 diabetes complications has not been extensively investigated. We hypothesised that N-glycosylation of IgG may be related to the development of complications of type 2 diabetes., Methods: In three independent type 2 diabetes cohorts, plasma IgG N-glycosylation was measured using ultra performance liquid chromatography (DiaGene n = 1815, GenodiabMar n = 640) and mass spectrometry (Hoorn Diabetes Care Study n = 1266). We investigated the associations of IgG N-glycosylation (fucosylation, galactosylation, sialylation and bisection) with incident and prevalent nephropathy, retinopathy and macrovascular disease using Cox- and logistic regression, followed by meta-analyses. The models were adjusted for age and sex and additionally for clinical risk factors., Results: IgG galactosylation was negatively associated with prevalent and incident nephropathy and macrovascular disease after adjustment for clinical risk factors. Sialylation was negatively associated with incident diabetic nephropathy after adjustment for clinical risk factors. For incident retinopathy, similar associations were found for galactosylation, adjusted for age and sex., Conclusions: We showed that IgG N-glycosylation, particularly galactosylation and to a lesser extent sialylation, is associated with a higher prevalence and future development of macro- and microvascular complications of diabetes. These findings indicate the predictive potential of IgG N-glycosylation in diabetes complications and should be analysed further in additional large cohorts to obtain the power to solidify these conclusions., (© 2023 The Authors. Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

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., (© 2023 by the American Diabetes Association.)

Aims/hypothesis: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes., Methods: Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA 1c , C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data., Results: Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10 -15 ), without evidence for reverse causality., Conclusions/interpretation: Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities., (© 2023. The Author(s).)

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries., (© 2022. The Author(s).)

Aims/hypothesis: Mitochondrial dysfunction, which can be approximated by blood mitochondrial DNA copy number (mtDNA-CN), has been implicated in the pathogenesis of type 2 diabetes mellitus. Thus far, however, insights from prospective cohort studies and Mendelian randomisation (MR) analyses on this relationship are limited. We assessed the association between blood mtDNA-CN and incident type 2 diabetes using multivariable-adjusted regression analyses, and the associations between blood mtDNA-CN and type 2 diabetes and BMI using bi-directional MR., Methods: Multivariable-adjusted Cox proportional hazard models were used to estimate the association between blood mtDNA-CN and incident type 2 diabetes in 285,967 unrelated European individuals from UK Biobank free of type 2 diabetes at baseline. Additionally, a cross-sectional analysis was performed to investigate the association between blood mtDNA-CN and BMI. We also assessed the potentially causal relationship between blood mtDNA-CN and type 2 diabetes (N=898,130 from DIAGRAM, N=215,654 from FinnGen) and BMI (N=681,275 from GIANT) using bi-directional two-sample MR., Results: During a median follow-up of 11.87 years, 15,111 participants developed type 2 diabetes. Participants with a higher level of blood mtDNA-CN are at lower risk of developing type 2 diabetes (HR 0.90 [95% CI 0.89, 0.92]). After additional adjustment for BMI and other confounders, these results attenuated moderately and remained present. The multivariable-adjusted cross-sectional analyses showed that higher blood mtDNA-CN was associated with lower BMI (-0.12 [95% CI -0.14, -0.10]) kg/m 2 . In the bi-directional MR analyses, we found no evidence for causal associations between blood mtDNA-CN and type 2 diabetes, and blood mtDNA-CN and BMI in either direction., Conclusions/interpretation: The results from the present study indicate that the observed association between low blood mtDNA-CN and higher risk of type 2 diabetes is likely not causal., (© 2022. The Author(s).)

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology., Competing Interests: Declaration of interests G.C.-P. is currently an employee of 23andMe Inc. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M. Psaty serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G. Thorleifsson, A.H., D.F.G., H. Holm, U.T., and K.S. are employees of deCODE/Amgen Inc. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer Ltd. M. McCarthy has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. M. McCarthy and A. Mahajan are employees of Genentech and holders of Roche stock. M.S. receives funding from Pfizer Inc. for a project unrelated to this work. M.E.K. is employed by SYNLAB MVZ Mannheim GmbH. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH , grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, and other from Synlab Holding Deutschland GmbH, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color Genomics; received speaking fees from Illumina and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). S. Kathiresan is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; and he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M.-K. is a part-time clinical research consultant for Metabolon, Inc. D. Saleheen has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. P.N. reports investigator-initated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a scientific advisory board member of Esperion Therapeutics, geneXwell, and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. The spouse of C.J.W. is employed by Regeneron., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Aims/hypothesis: Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants., Methods: In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms., Results: One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated., Conclusion: Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes., (© 2022. The Author(s).)

Objective: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA 1c reduction., Research Design and Methods: As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA 1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions., Results: After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA 1c reduction at a genome-wide scale ( P < 5 × 10 -8 ). The C allele at rs1234032, near GXYLT1 , was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10 -8 ), lower reduction in HbA 1c . Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, P = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide ( N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (β = 0.21, P = 2.04 × 10 -58 ). The C allele of rs10770791, in an intronic region of SLCO1B1 , was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA 1c ( P = 4.80 × 10 -8 ). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression ( P = 1.61 × 10 -7 ), which, together with functional studies in cells expressing SLCO1B1 , supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed ( P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA 1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor., Conclusions: We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs., (© 2021 by the American Diabetes Association.)

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic ( N = 12,828), metabolomic ( N = 2,945), lipidomic ( N = 2,593), and proteomic ( N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease., (© 2021 by the American Diabetes Association.)

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of the insulin-producing pancreatic β-cells. Increasing evidence suggest that the β-cells themselves contribute to their own destruction by generating neoantigens through the production of aberrant or modified proteins that escape central tolerance. We recently demonstrated that ribosomal infidelity amplified by stress could lead to the generation of neoantigens in human β-cells, emphasizing the participation of nonconventional translation events in autoimmunity, as occurring in cancer or virus-infected tissues. Using a transcriptome-wide profiling approach to map translation initiation start sites in human β-cells under standard and inflammatory conditions, we identify a completely new set of polypeptides derived from noncanonical start sites and translation initiation within long noncoding RNA. Our data underline the extreme diversity of the β-cell translatome and may reveal new functional biomarkers for β-cell distress, disease prediction and progression, and therapeutic intervention in T1D., (© 2021 by the American Diabetes Association.)

Introduction: Although associations of total plasma N- glycome (TPNG) with type 2 diabetes have been reported, little is known on the role of TPNG in type 2 diabetes complications, a major cause of type 2 diabetes-related morbidity and mortality. Here, we assessed TPNG in relation to type 2 diabetes complications in subsamples of two Dutch cohorts using mass spectrometry (n=1815 in DiaGene and n=1518 in Hoorn Diabetes Care System)., Research Design and Methods: Blood plasma samples and technical replicates were pipetted into 96-well plates in a randomized manner. Peptide:N-glycosidase F (PNGase F) was used to release N- glycans, whereafter sialic acids were derivatized for stabilization and linkage differentiation. After total area normalization, 68 individual glycan compositions were quantified in total and were used to calculate 45 derived traits which reflect structural features of glycosylation. Associations of glycan features with prevalent and incident microvascular or macrovascular complications were tested in logistic and Cox regression in both independent cohorts and the results were meta-analyzed., Results: Our results demonstrated similarities between incident and prevalent complications. The strongest association for prevalent cardiovascular disease was a high level of bisection on a group of diantennary glycans (A2FS0B; OR=1.38, p=1.34×10 -11 ), while for prevalent nephropathy the increase in 2,6-sialylation on triantennary glycans was most pronounced (A3E; OR=1.28, p=9.70×10 -6 ). Several other TPNG features, including fucosylation, galactosylation, and sialylation, firmly demonstrated associations with prevalent and incident complications of type 2 diabetes., Conclusions: These findings may provide a glance on how TPNG patterns change before complications emerge, paving the way for future studies on prediction biomarkers and potentially disease mechanisms., Competing Interests: Competing interests: MW is inventor on a patent application on sialic acid derivatization by ethyl esterification. EM is employed by Genos. VD currently works at BioTherapeutics Analytical Development, Janssen Biologics BV. No other potential conflicts of interest relevant to this article were reported., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes., Design: Systematic review and external validation., Data Sources: PubMed and Embase., Eligibility Criteria: Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes., Methods: Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic)., Results: 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons., Conclusions: This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting., Systematic Review Registration: PROSPERO CRD42020192831., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Dutch Diabetes Research foundation Fund, IMI-RHAPSODY, and ZorgInstitute Netherlands (Dutch Healthcare Institute) for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic., Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA 1c , random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster., Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression., Conclusions/interpretation: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA 1c , HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration., (© 2021. The Author(s).)

Background: Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups., Objectives: We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands., Methods: A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77-0.92 in Dutch, 0.66-0.83 in South Asian Surinamese, 0.70-0.82 in African Surinamese, 0.61-0.81 in Ghanaian, 0.69-0.86 in Turkish, and 0.69-0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one., Conclusions: Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in., Competing Interests: The author(s) has/have no competing interests to declare., (Copyright: © 2021 The Author(s).)

Introduction: Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information., Aim: To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA., Methods: The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA., Conclusion: The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

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

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D)., Research Design and Methods: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA 1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression., Results: Faster HbA 1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles ( R 2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role., Conclusions: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression., (© 2020 by the American Diabetes Association.)