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The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice. Graphical abstract

Concerns have been raised regarding a potentially increased risk of cancer associated with treatment with glucagon-like peptide-1 (GLP-1) receptor agonists. Here, we explored whether fasting and oral glucose tolerance test post-challenge glucose-dependent insulinotropic peptide (GIP) and GLP-1 levels were associated with incident first cancer. Within the cardiovascular re-examination arm of the population-based Malmö Diet Cancer study (n = 3734), 685 participants with a previous cancer diagnosis were excluded, resulting in 3049 participants (mean age 72.2 ± 5.6 years, 59.5% women), of whom 485 were diagnosed with incident first cancer (median follow-up time 9.9 years). Multivariable Cox-regression and competing risk regression (death as competing risk) were used to explore associations between incretin levels and incident first cancer. Higher levels of fasting GLP-1 (462 incident first cancer cases/2417 controls) showed lower risk of incident first cancer in competing risk regression (sub-hazard ratio 0.90; 95% confidence interval 0.82–0.99; p = 0.022). No association was seen for fasting GIP, post-challenge GIP, or post-challenge GLP-1 and incident first cancer. In this prospective study, none of the fasting and post-challenge levels of GIP and GLP-1 were associated with higher risk of incident first cancer; by contrast, higher levels of fasting GLP-1 were associated with lower risk of incident first cancer.

International audience; Background: A novel data-driven classification of type 2 diabetes has been proposed to personalise anti-diabetic treatment according to phenotype. One subgroup, severe insulin-resistant diabetes (SIRD), is characterised by mild hyperglycaemia but marked hyperinsulinaemia, and presents an increased risk of diabetic nephropathy. We hypothesised that patients with SIRD could particularly benefit from metabolic surgery.Methods: We retrospectively related the newly defined clusters with the response to metabolic surgery in participants with type 2 diabetes from independent cohorts in France (the Atlas Biologique de l'Obésite Sévère [ABOS] cohort, n=368; participants underwent Roux-en-Y gastric bypass or sleeve gastrectomy between Jan 1, 2006, and Dec 12, 2017) and Brazil (the metabolic surgery cohort of the German Hospital of San Paulo, n=121; participants underwent Roux-en-Y gastric bypass between April 1, 2008, and March 20, 2016). The study outcomes were type 2 diabetes remission and improvement of estimated glomerular filtration rate (eGFR).Findings: At baseline, 34 (9%) of 368 patients, 314 (85%) of 368 patients, and 17 (5%) of 368 patients were classified as having SIRD, mild obesity-related diabetes (MOD), and severe insulin deficient diabetes (SIDD) in the ABOS cohort, respectively, and in the São Paulo cohort, ten (8%) of 121 patients, 83 (69%) of 121 patients, and 25 (21%) of 121 patients were classified as having SIRD, MOD, and SIDD, respectively. At 1 year, type 2 diabetes remission was reported in 26 (81%) of 32 and nine (90%) of ten patients with SIRD, 167 (55%) of 306 and 42 (51%) of 83 patients with MOD, and two (13%) of 16 and nine (36%) of 25 patients with SIDD, in the ABOS and São Paulo cohorts, respectively. The mean eGFR was lower in patients with SIRD at baseline and increased postoperatively in these patients in both cohorts. In multivariable analysis, SIRD was associated with more frequent type 2 diabetes remission (odds ratio 4·3, 95% CI 1·8-11·2; p=0·0015), and an increase in eGFR (mean effect size 13·1 ml/min per 1·73 m2, 95% CI 3·6-22·7; p=0·0070).Interpretation: Patients in the SIRD subgroup had better outcomes after metabolic surgery, both in terms of type 2 diabetes remission and renal function, with no additional surgical risk. Data-driven classification might help to refine the indications for metabolic surgery.Funding: Agence Nationale de la Recherche, Investissement d'Avenir, Innovative Medecines Initiative, Fondation Cœur et Artères, and Fondation Francophone pour la Recherche sur le Diabète.

Aims/hypothesis Galectin-1 modulates inflammation and angiogenesis, and cross-sectional studies indicate that galectin-1 may be a uniting factor between obesity, type 2 diabetes and kidney function. We examined whether circulating galectin-1 can predict incidence of chronic kidney disease (CKD) and type 2 diabetes in a middle-aged population, and if Mendelian randomisation (MR) can provide evidence for causal direction of effects. Methods Participants (n = 4022; 58.6% women) in the Malmö Diet and Cancer Study–Cardiovascular Cohort enrolled between 1991 and 1994 (mean age 57.6 years) were examined. eGFR was calculated at baseline and after a mean follow-up of 16.6 ± 1.5 years. Diabetes status was ascertained through registry linkage (mean follow-up of 18.4 ± 6.1 years). The associations of baseline galectin-1 with incident CKD and type 2 diabetes were assessed with Cox regression, adjusting for established risk factors. In addition, a genome-wide association study on galectin-1 was performed to identify genetic instruments for two-sample MR analyses utilising the genetic associations obtained from the Chronic Kidney Disease Genetics (CKDGen) Consortium (41,395 cases and 439,303 controls) and the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (74,124 cases and 824,006 controls). One genome-wide significant locus in the galectin-1 gene region was identified (sentinel SNP rs7285699; p = 2.4 × 10−11). The association between galectin-1 and eGFR was also examined in individuals with newly diagnosed diabetes from the All New Diabetics In Scania (ANDIS) cohort. Results Galectin-1 was strongly associated with lower eGFR at baseline (p = 2.3 × 10−89) but not with incident CKD. However, galectin-1 was associated with increased risk of type 2 diabetes (per SD increase, HR 1.12; 95% CI 1.02, 1.24). Two-sample MR analyses could not ascertain a causal effect of galectin-1 on CKD (OR 0.92; 95% CI 0.82, 1.02) or type 2 diabetes (OR 1.05; 95% CI 0.98, 1.14) in a general population. However, in individuals with type 2 diabetes from ANDIS who belonged to the severe insulin-resistant diabetes subgroup and were at high risk of diabetic nephropathy, genetically elevated galectin-1 was significantly associated with higher eGFR (p = 5.7 × 10−3). Conclusions/interpretation Galectin-1 is strongly associated with lower kidney function in cross-sectional analyses, and two-sample MR analyses suggest a causal protective effect on kidney function among individuals with type 2 diabetes at high risk of diabetic nephropathy. Future studies are needed to explore the mechanisms by which galectin-1 affects kidney function and whether it could be a useful target among individuals with type 2 diabetes for renal improvement. Graphical abstract

Type 2 diabetes has been reproducibly clustered into five subtypes with different disease progression and risk of complications; however, etiological differences are unknown. We used genome-wide association and genetic risk score (GRS) analysis to compare the underlying genetic drivers. Individuals from the Swedish ANDIS (All New Diabetics In Scania) study were compared to individuals without diabetes; the Finnish DIREVA (Diabetes register in Vasa) and Botnia studies were used for replication. We show that subtypes differ with regard to family history of diabetes and association with GRS for diabetes-related traits. The severe insulin-resistant subtype was uniquely associated with GRS for fasting insulin but not with variants in the TCF7L2 locus or GRS reflecting insulin secretion. Further, an SNP (rs10824307) near LRMDA was uniquely associated with mild obesity-related diabetes. Therefore, we conclude that the subtypes have partially distinct genetic backgrounds indicating etiological differences. Genome-wide association and genetic risk score analyses highlight differences in genetic architecture across five subtypes of diabetes.

Aim/hypothesis Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes. Methods We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). Results Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy. Conclusions /interpretation Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies. Graphical abstract

Parent-of-origin effects (POE) and sex-specific parental effects have been reported for plasma lipid levels, and a strong relationship exists between dyslipidemia and obesity. We aim to explore whether genetic variants previously reported to have an association to lipid traits also show POE on blood lipid levels and obesity. Families from the Botnia cohort and the Hungarian Transdanubian Biobank (HTB) were genotyped for 12 SNPs, parental origin of alleles were inferred, and generalized estimating equations were modeled to assess parental-specific associations with lipid traits and obesity. POE were observed for the variants at the TMEM57, DOCK7/ANGPTL3, LPL, and APOA on lipid traits, the latter replicated in HTB. Sex-specific parental effects were also observed; variants at ANGPTL3/DOCK7 showed POE on lipid traits and obesity in daughters only, while those at LPL and TMEM57 showed POE on lipid traits in sons. Variants at LPL and DOCK7/ANGPTL3 showed POE on obesity-related traits in Botnia and HTB, and POE effects on obesity were seen to a higher degree in daughters. This highlights the need to include analysis of POEs in genetic studies of complex traits.

Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (GCG, 56%), amylin (IAPP, 52%), insulin (INS, 44%), and somatostatin (SST, 24%). Inhibition of two DEGs,UNC5DandSERPINE2, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.

BackgroundPreclinical and human genetics studies suggest that impaired glucose-dependent insulinotropic polypeptide receptor (GIPR) signalling worsens glycaemic control. The relationship between GIPR signalling and risk of cancers influenced by impaired glucose homeostasis is unclear.MethodsWe examined the association of a missense variant in GIPR, rs1800437 (E354Q), shown to impair long-term GIPR signalling and lower circulating GIP concentrations, with risk of 6 cancers influenced by impaired glucose homeostasis. Summary genetic association data on breast, colorectal, endometrial, lung, pancreatic, and renal cancer risk were obtained from GWAS consortia (235,698 cases, 333,932 controls). Replication analyses were performed in the FinnGen consortium (8,401 breast cancer cases, 99,321 controls). Colocalisation was performed to examine robustness of findings to genetic confounding. Finally, we examined the association of E354Q with potential downstream molecular mediators of GIPR signalling to identify possible mechanisms underpinning an effect on cancer risk.ResultsEach copy of E354Q was associated with a higher risk of overall breast cancer (OR:1.05,95%CI:1.03-1.06,P=4.23×10−9) and luminal A-like breast cancer (OR:1.05,95%CI:1.03-1.06,P=6.02×10−7). In replication analysis, E354Q was likewise associated with breast cancer risk (OR:1.06,95%CI:1.02-1.08,P=1.09×10−3) and in colocalisation there was a >99.9% posterior probability of circulating GIP concentrations sharing a causal variant with overall and luminal A-like breast cancer in GIPR. E354Q was not associated with risk of the 5 other cancers examined. In mechanistic analysis, this variant was associated with higher postprandial glucose concentrations but diminished insulin secretion and lower testosterone concentrations.ConclusionOur comprehensive drug target-Mendelian randomization analysis across 6 cancers suggests an adverse effect of the GIPR E354Q variant on overall and luminal A-like breast cancer risk. These findings provide genetic evidence to support further evaluation of the therapeutic potential of GIPR signalling in breast cancer prevention.

Aims/hypothesis Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. Methods We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. Results The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10−9; although not withstanding correction for multiple testing, p>9.3×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p−6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10−6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p−11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10−8] and negatively with tubulointerstitial fibrosis [p=2.0×10−9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10−16], and SNX30 expression correlated positively with eGFR [p=5.8×10−14] and negatively with fibrosis [p−16]). Conclusions/interpretation Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract

Objective: Type 2 diabetes (T2D) was recently reclassified into Severe Insulin Deficient Diabetes (SIDD), Severe Insulin Resistant Diabetes (SIRD), Mild Obesity-related Diabetes (MOD), and Mild Age-Related Diabetes (MARD), which have different risk of complications. We explored whether DNA methylation differs between these subgroups and if subgroup-unique methylation risk scores (MRSs) predict diabetic complications. Methods: Genome-wide DNA methylation was analysed in blood from newly diagnosed T2D subjects in discovery and replication cohorts. Subgroup-unique MRSs were built including top subgroup-unique DNA methylation sites. Regression models examined whether MRSs associated with subgroups and future complications. Results: We found epigenetic differences between the T2D subgroups. Subgroup-unique MRSs were significantly different in those patients allocated to each respective subgroup compared to the combined group of all other subgroups. These associations were validated in an independent replication cohort, showing that subgroup-unique MRSs associate with individual subgroups (OR 1.6-6.1 per 1SD increase, pTXNIP and ELOVL2. Methylation in blood of 18 subgroup-unique sites mirror epigenetic patterns in tissues relevant for T2D, muscle and adipose tissue. Conclusions: We identified differential epigenetic patterns between T2D subgroups, which associated with future diabetic complications. These data support a reclassification of diabetes and the need for precision medicine in T2D subgroups.

Aims/hypotheses Smoking and use of smokeless tobacco (snus) are associated with an increased risk of type 2 diabetes. We investigated whether smoking and snus use increase the risk of latent autoimmune diabetes in adults (LADA) and elucidated potential interaction with HLA high-risk genotypes. Methods Analyses were based on Swedish case–control data (collected 2010–2019) with incident cases of LADA (n=593) and type 2 diabetes (n=2038), and 3036 controls, and Norwegian prospective data (collected 1984–2019) with incident cases of LADA (n=245) and type 2 diabetes (n=3726) during 1,696,503 person-years of follow-up. Pooled RRs with 95% CIs were estimated for smoking, and ORs for snus use (case–control data only). The interaction was assessed by attributable proportion (AP) due to interaction. A two-sample Mendelian randomisation (MR) study on smoking and LADA/type 2 diabetes was conducted based on summary statistics from genome-wide association studies. Results Smoking (RRpooled 1.30 [95% CI 1.06, 1.59] for current vs never) and snus use (OR 1.97 [95% CI 1.20, 3.24] for ≥15 box-years vs never use) were associated with an increased risk of LADA. Corresponding estimates for type 2 diabetes were 1.38 (95% CI 1.28, 1.49) and 1.92 (95% CI 1.27, 2.90), respectively. There was interaction between smoking and HLA high-risk genotypes (AP 0.27 [95% CI 0.01, 0.53]) in relation to LADA. The positive association between smoking and LADA/type 2 diabetes was confirmed by the MR study. Conclusions/interpretation Our findings suggest that tobacco use increases the risk of LADA and that smoking acts synergistically with genetic susceptibility in the promotion of LADA. Data availability Analysis codes are shared through GitHub (https://github.com/jeseds/Smoking-use-of-smokeless-tobacco-HLA-genotypes-and-incidence-of-LADA). Graphical abstract

Type 2 diabetes (T2D) is defined by a single metabolite, glucose, but is increasingly recognized as a highly heterogeneous disease, including individuals with varying clinical characteristics, disease progression, drug response, and risk of complications. Identification of subtypes with differing risk profiles and disease etiologies at diagnosis could open up avenues for personalized medicine and allow clinical resources to be focused to the patients who would be most likely to develop diabetic complications, thereby both improving patient health and reducing costs for the health sector. More homogeneous populations also offer increased power in experimental, genetic, and clinical studies. Clinical parameters are easily available and reflect relevant disease pathways, including the effects of both genetic and environmental exposures. We used six clinical parameters (GAD autoantibodies, age at diabetes onset, HbA1c, BMI, and measures of insulin resistance and insulin secretion) to cluster adult-onset diabetes patients into five subtypes. These subtypes have been robustly reproduced in several populations and associated with different risks of complications, comorbidities, genetics, and response to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group had the highest risk for diabetic kidney disease (DKD) and fatty liver, emphasizing the importance of insulin resistance for DKD and hepatosteatosis in T2D. In conclusion, we believe that subclassification using these highly relevant parameters could provide a framework for personalized medicine in diabetes.

Parent-of-origin effects (POE) and sex-specific parental effects have been reported for plasma lipid levels, and a strong relationship exists between dyslipidemia and obesity. We aim to explore whether genetic variants previously reported to have an association to lipid traits also show POE on blood lipid levels and obesity. Families from the Botnia cohort and the Hungarian Transdanubian Biobank (HTB) were genotyped for 12 SNPs, parental origin of alleles were inferred, and generalized estimating equations were modeled to assess parental-specific associations with lipid traits and obesity. POE were observed for the variants at the TMEM57, DOCK7/ANGPTL3, LPL, and APOA on lipid traits, the latter replicated in HTB. Sex-specific parental effects were also observed; variants at ANGPTL3/DOCK7 showed POE on lipid traits and obesity in daughters only, while those at LPL and TMEM57 showed POE on lipid traits in sons. Variants at LPL and DOCK7/ANGPTL3 showed POE on obesity-related traits in Botnia and HTB, and POE effects on obesity were seen to a higher degree in daughters. This highlights the need to include analysis of POEs in genetic studies of complex traits.

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.

Type 2 diabetes (T2D) was recently reclassified into severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD), which have different risk of complications. We explored whether DNA methylation differs between these subgroups and whether subgroup-unique methylation risk scores (MRSs) predict diabetic complications.Genome-wide DNA methylation was analyzed in blood from subjects with newly diagnosed T2D in discovery and replication cohorts. Subgroup-unique MRSs were built, including top subgroup-unique DNA methylation sites. Regression models examined whether MRSs associated with subgroups and future complications.We found epigenetic differences between the T2D subgroups. Subgroup-unique MRSs were significantly different in those patients allocated to each respective subgroup compared with the combined group of all other subgroups. These associations were validated in an independent replication cohort, showing that subgroup-unique MRSs associate with individual subgroups (odds ratios 1.6-6.1 per 1-SD increase, P0.01). Subgroup-unique MRSs were also associated with future complications. Higher MOD-MRS was associated with lower risk of cardiovascular (hazard ratio [HR] 0.65, P = 0.001) and renal (HR 0.50, P0.001) disease, whereas higher SIRD-MRS and MARD-MRS were associated with an increased risk of these complications (HR 1.4-1.9 per 1-SD increase, P0.01). Of 95 methylation sites included in subgroup-unique MRSs, 39 were annotated to genes previously linked to diabetes-related traits, including TXNIP and ELOVL2. Methylation in the blood of 18 subgroup-unique sites mirrors epigenetic patterns in tissues relevant for T2D, muscle and adipose tissue.We identified differential epigenetic patterns between T2D subgroups that associated with future diabetic complications. These data support a reclassification of diabetes and the need for precision medicine in T2D subgroups.

Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.

BackgroundDiabetes is the leading cause of kidney disease, and heritability studies demonstrate a substantial, yet poorly understood, contribution of genetics to kidney complications in people with diabetes.MethodsWe performed genome-wide association study (GWAS) meta-analyses using ten different phenotypic definitions of diabetic kidney disease (DKD), including nearly 27,000 individuals with diabetes, and integrated the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel low frequency intronic variant (rs72831309) in the TENM2 gene encoding teneurin transmembrane protein 2 associated with a lower risk of the combined chronic kidney disease (CKD; eGFR2) and DKD (microalbuminuria or worse) phenotype (“CKD-DKD”, odds ratio 2.08, p=9.8×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A, and MFF, p−6). Integration of GWAS data with human glomerular and tubular expression data in a transcriptome-wide association study demonstrated higher tubular AKIRIN2 gene expression in DKD versus non-DKD controls (p=1.1×10−6). The lead SNPs within the DCLK1, AKIRIN2, SNX30 and three other gene regions significantly alterated the methylation at this region in kidneys (p−11). Expression of target genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes. For example, tubular TENM2 expression positively correlated with eGFR (p=2.3×10−9) and negatively with tubulointerstitial fibrosis (p=4.7×10−9), tubular DCLK1 expression positively correlated with fibrosis (p=1.6×10−12), and SNX30 level positively correlated with eGFR (p=7.6×10−13) and negatively with fibrosis (p−16).ConclusionsGWAS meta-analysis and integration with renal omics data points to novel genes contributing to pathogenesis of DKD.

Skeletal muscle fiber composition and capillary density influence physical performance and whole-body metabolic properties. ~45% of the variance in fiber type is heritable, which motivated us to perform a genome-wide association study of skeletal muscle histology from 656 Swedish men. Four independent variants were associated (p − 8) with proportion of type IIx fibers or capillary-to-fiber ratio (C:F). The strongest signal localized to the rs115660502 variant, where the G-allele corresponded with increased C:F and reduced skeletal muscle expression of the proximal gene, RAB3 GTPase Activating Non-Catalytic Protein Subunit 2 (RAB3GAP2). The G-allele was less frequent in elite short-track sprinters and more frequent in endurance athletes than in matched non-athlete (population) controls; RAB3GAP2 expression was reduced by high-intensity intermittent training. RAB3GAP2 protein was not uniformly expressed in muscle tissue but localized to the endothelium and capillaries. Experimental reduction of RAB3GAP2 in human endothelial cells led to increased tube formation in vitro, to regulation of secreted factors promoting angiogenesis and T-cell activation, to reduced intracellular levels of von Willebrand factor (VWF) and, post-implantation, to increased endothelial cell density in vivo in mice. The amount of RAB3GAP2 in skeletal muscle was positively associated with exercise-induced release of VWF in vivo in humans. By regulating the release of protein factors (VWF, CD70, TNC, TNXB, MCP1, IGFBP3, COL1A1, TFPI2 and tPA), RAB3GAP2 influences fitness adaptation after exercise by improving muscle healing and promotion of capillary formation.

Publisher Copyright: © 2022 Aims: Newly-defined subgroups of type 2 diabetes mellitus (T2DM) have been reported from real-world cohorts but not in detail from randomised clinical trials (RCTs). Methods: T2DM participants, uncontrolled on different pre-study therapies (n = 12.738; 82 % Caucasian; 44 % with diabetes duration > 10 years) from 14 RCTs, were assigned to new subgroups according to age at onset of diabetes, HbA1c, BMI, and fasting C-peptide using the nearest centroid approach. Subgroup distribution, characteristics and influencing factors were analysed. Results: In both, pooled and single RCTs, “mild-obesity related diabetes” predominated (45 %) with mean BMI of 35 kg/m2. “Severe insulin-resistant diabetes” was found least often (4.6 %) and prevalence of “mild age-related diabetes” (23.9 %) was mainly influenced by age at onset of diabetes and age cut-offs. Subgroup characteristics were widely comparable to those from real-world cohorts, but all subgroups showed higher frequencies of diabetes-related complications which were associated with longer diabetes duration. A high proportion of “severe insulin-deficient diabetes” (25.4 %) was identified with poor pre-study glycaemic control. Conclusions: Classification of RCT participants into newly-defined diabetes subgroups revealed the existence of a heterogeneous population of T2DM. For future RCTs, subgroup-based randomisation of T2DM will better define the target population and relevance of the outcomes by avoiding clinical heterogeneity.

Aim/HypothesisFive subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian young-onset type 2 diabetes patients.MethodsWe applied the European derived centroids to the Indian type 2 diabetes patients diagnosed before 45 years of age from the WellGen (n = 1612) cohort. We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts ANDIS (n= 962) and DIREVA (n=420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).ResultsBoth Indian and European young type 2 diabetes patients were predominantly classified into severely insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severely insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to figures in Europeans (Swedish: 26% vs 68%, Finnish: 24% vs 71% respectively). A higher proportion of SIDD compared to MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterized by insulin deficiency and hyperglycemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared to MOD while the latter had higher prevalence of neuropathy.Conclusions /InterpretationOur data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European patients in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in young Indian type 2 diabetes, and suggest a need to review management strategies.

Diabetes increases the risk of bacterial infections. We investigated whether common genetic variants associate with infection susceptibility in Finnish diabetic individuals. We performed genome-wide association studies and pathway analysis for bacterial infection frequency in Finnish adult diabetic individuals (FinnDiane Study; N = 5092, Diabetes Registry Vaasa; N = 4247) using national register data on antibiotic prescription purchases. Replication analyses were performed in a Swedish diabetic population (ANDIS; N = 9602) and in a Finnish non-diabetic population (FinnGen; N = 159,166). Genome-wide data indicated moderate but significant narrow-sense heritability for infection susceptibility (h2 = 16%, P = 0.02). Variants on chromosome 2 were associated with reduced infection susceptibility (rs62192851, P = 2.23 × 10–7). Homozygotic carriers of the rs62192851 effect allele (N = 44) had a 37% lower median annual antibiotic purchase rate, compared to homozygotic carriers of the reference allele (N = 4231): 0.38 [IQR 0.22–0.90] and 0.60 [0.30–1.20] respectively, P = 0.01). Variants rs6727834 and rs10188087, in linkage disequilibrium with rs62192851, replicated in the FinnGen-cohort (P IRAK1 mediated NF-κB activation through IKK complex recruitment-pathway to be a mediator of the phenotype. Common genetic variants on chromosome 2 may associate with reduced risk of bacterial infections in Finnish individuals with diabetes.

We have deployed a multi-omics approach in large cohorts of patients with existing type 2 diabetes to identify biomarkers for disease progression across three molecular classes, metabolites, lipids and proteins. A Cox regression analysis for association with time to insulin requirement in 2,973 patients in the DCS, ANDIS and GoDARTS cohorts identified homocitrulline, isoleucine and 2-aminoadipic acid, as well as the bile acids glycocholic and taurocholic acids, as predictive of more rapid deterioration. Increased levels of eight triacylglycerol species, and lowered levels of the sphingomyelin SM 42:2;2 were also predictive of disease progression. Of ∼1,300 proteins examined in two cohorts, levels of GDF-15/MIC1, IL-18RA, CRELD1, NogoR, FAS, and ENPP7 were associated with faster progression, whilst SMAC/DIABLO, COTL1, SPOCK1 and HEMK2 predicted lower progression rates. Strikingly, identified proteins and lipids were also associated with diabetes incidence and prevalence in external replication cohorts. Implicating roles in disease compensation, NogoR/RTN4R improved glucose tolerance in high fat-fed mice and tended to improved insulin signalling in liver cells whilst IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. Conversely, high NogoR levels led to islet cell apoptosis. This comprehensive, multi-disciplinary approach thus identifies novel biomarkers with potential prognostic utility, provides evidence for new disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Homeostatic control of blood glucose requires different physiological responses in the fasting and post-prandial states. We reasoned that glucose measurements under non-standardised conditions (random glucose; RG) may capture diverse glucoregulatory processes more effectively than previous genome-wide association studies (GWAS) of fasting glycaemia or after standardised glucose loads. Through GWAS meta-analysis of RG in 493,036 individuals without diabetes of diverse ethnicities we identified 128 associated loci represented by 162 distinct signals, including 14 with sex-dimorphic effects, 9 discovered through trans-ethnic analysis, and 70 novel signals for glycaemic traits. Novel RG loci were particularly enriched in expression in the ileum and colon, indicating a prominent role for the gastrointestinal tract in the control of blood glucose. Functional studies and molecular dynamics simulations of coding variants of GLP1R, a well-established type 2 diabetes treatment target, provided a genetic framework for optimal selection of GLP-1R agonist therapy. We also provided new evidence from Mendelian randomisation that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Thus, our approach based on RG GWAS provided wide-ranging insights into the biology of glucose regulation, diabetes complications and the potential for treatment stratification.

Summary Background Cluster analyses have proposed different diabetes phenotypes using age, BMI, glycaemia, homoeostasis model estimates, and islet autoantibodies. We tested whether comprehensive phenotyping validates and further characterises these clusters at diagnosis and whether relevant diabetes-related complications differ among these clusters, during 5-years of follow-up. Methods Patients with newly diagnosed type 1 or type 2 diabetes in the German Diabetes Study underwent comprehensive phenotyping and assessment of laboratory variables. Insulin sensitivity was assessed using hyperinsulinaemic-euglycaemic clamps, hepatocellular lipid content using magnetic resonance spectroscopy, hepatic fibrosis using non-invasive scores, and peripheral and autonomic neuropathy using functional and clinical criteria. Patients were reassessed after 5 years. The German Diabetes Study is registered with ClinicalTrials.gov , number NCT01055093 , and is ongoing. Findings 1105 patients were classified at baseline into five clusters, with 386 (35%) assigned to mild age-related diabetes (MARD), 323 (29%) to mild obesity-related diabetes (MOD), 247 (22%) to severe autoimmune diabetes (SAID), 121 (11%) to severe insulin-resistant diabetes (SIRD), and 28 (3%) to severe insulin-deficient diabetes (SIDD). At 5-year follow-up, 367 patients were reassessed, 128 (35%) with MARD, 106 (29%) with MOD, 88 (24%) with SAID, 35 (10%) with SIRD, and ten (3%) with SIDD. Whole-body insulin sensitivity was lowest in patients with SIRD at baseline (mean 4·3 mg/kg per min [SD 2·0]) compared with those with SAID (8·4 mg/kg per min [3·2]; p Interpretation Cluster analysis can characterise cohorts with different degrees of whole-body and adipose-tissue insulin resistance. Specific diabetes clusters show different prevalence of diabetes complications at early stages of non-alcoholic fatty liver disease and diabetic neuropathy. These findings could help improve targeted prevention and treatment and enable precision medicine for diabetes and its comorbidities. 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, Research Network SFB 1116 of the German Research Foundation, and Schmutzler Stiftung.

Purpose Sweetened beverage consumption is associated with type 2 diabetes (T2D) and LADA. We investigated to what extent this association is mediated by BMI and whether it is modified by genotypes of HLA, TCF7L2 rs7903146, or FTO rs9939609. Methods Swedish case–control data including incident cases of LADA (n = 386) and T2D (n = 1253) with matched population-based controls (n = 1545) was used. We estimated adjusted ORs of diabetes (95% CI) in relation to sweetened beverage intake (per daily 200 mL serving) and genotypes. The impact of BMI was estimated using causal mediation methodology. Associations with HOMA-IR and HOMA-B were explored through linear regression. Results Sweetened beverage intake was associated with increased risk of LADA (OR 1.15, 95% CI 1.03–1.29) and T2D (OR 1.21, 1.11–1.32). BMI was estimated to mediate 17% (LADA) and 56% (T2D) of the total risk. LADA was associated with risk variants of HLA (3.44, 2.63–4.50) and TCF7L2 (1.27, 1.00–1.61) but not FTO. Only among non-carriers of high-risk HLA genotypes was sweetened beverage intake associated with risk of LADA (OR 1.32, 1.06–1.56) and HOMA-IR (beta = 0.162, p = 0.0047). T2D was associated with TCF7L2 and FTO but not HLA, and the risk conferred by sweetened beverages appeared modified by FTO (OR 1.45, 95% CI 1.21–1.73 in non-carriers). Conclusions Our findings suggest that sweetened beverages are associated with LADA and T2D partly through mediation by excess weight, but possibly also through other mechanisms including adverse effects on insulin sensitivity. These effects seem more pronounced in individuals without genetic susceptibility.

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value

Purpose Red meat consumption is positively associated with type 1 (T1D) and type 2 (T2D) diabetes. We investigated if red meat consumption increases the risk of latent autoimmune diabetes in adults (LADA) and T2D, and potential interaction with family history of diabetes (FHD), HLA and TCF7L2 genotypes. Methods Analyses were based on Swedish case–control data comprising incident cases of LADA (n = 465) and T2D (n = 1528) with matched, population-based controls (n = 1789; n = 1553 in genetic analyses). Multivariable-adjusted ORs in relation to self-reported processed and unprocessed red meat intake were estimated by conditional logistic regression models. Attributable proportion (AP) due to interaction was used to assess departure from additivity of effects. Results Consumption of processed red meat was associated with increased risk of LADA (per one servings/day OR 1.27, 95% CI 1.07–1.52), whereas no association was observed for unprocessed red meat. For T2D, there was no association with red meat intake once BMI was taken into account. The combination of high (> 0.3 servings/day vs. less) processed red meat intake and high-risk HLA-DQB1 and -DRB1 genotypes yielded OR 8.05 (95% CI 4.86–13.34) for LADA, with indications of significant interaction (AP 0.53, 95% CI 0.32–0.73). Results were similar for the combination of FHD-T1D and processed red meat. No interaction between processed red meat intake and FHD-T2D or risk variants of TCF7L2 was seen in relation to LADA or T2D. Conclusion Consumption of processed but not unprocessed red meat may increase the risk of LADA, especially in individuals with FHD-T1D or high-risk HLA genotypes.

Aims: We investigated the risk of latent autoimmune diabetes in adults (LADA) and type 2 diabetes in relation to a healthy lifestyle, the proportion of patients attributable to an unhealthy lifestyle, and the influence of family history of diabetes (FHD) and genetic susceptibility. Methods: The population-based study included incident LADA (n = 571), type 2 diabetes (n = 1962), and matched controls (n = 2217). A healthy lifestyle was defined by BMI < 25 kg/m2, moderate-to-high physical activity, a healthy diet, no smoking, and moderate alcohol consumption. We estimated odds ratios (OR) with 95% confidence intervals (CIs) adjusted for age, sex, education, and FHD. Results: Compared to a poor/moderate lifestyle, a healthy lifestyle was associated with a reduced risk of LADA (OR 0.51, CI 0.34-0.77) and type 2 diabetes (OR 0.09, CI 0.05-0.15). A healthy lifestyle conferred a reduced risk irrespective of FHD and high-risk HLA genotypes. Having a BMI < 25 kg/m2 conferred the largest risk reduction for both LADA (OR 0.54, CI 0.43-0.66) and type 2 diabetes (OR 0.12, CI 0.10-0.15) out of the individual items. Conclusion: People with a healthy lifestyle, especially a healthy body weight, have a reduced risk of LADA including those with genetic susceptibility to diabetes. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).