RNA sequencing unravels novel L cell constituents and mechanisms of GLP-1 secretion in human gastric bypass-operated intestine.

AIMS/HYPOTHESIS: Roux-en-Y gastric bypass surgery (RYGB) frequently results in remission of type 2 diabetes as well as exaggerated secretion of glucagon-like peptide-1 (GLP-1). Here, we assessed RYGB-induced transcriptomic alterations in the small intestine and investigated how they were related to the regulation of GLP-1 production and secretion in vitro and in vivo. METHODS: Human jejunal samples taken perisurgically and 1 year post RYGB (n=13) were analysed by RNA-seq. Guided by bioinformatics analysis we targeted four genes involved in cholesterol biosynthesis, which we confirmed to be expressed in human L cells, for potential involvement in GLP-1 regulation using siRNAs in GLUTag and STC-1 cells. Gene expression analyses, GLP-1 secretion measurements, intracellular calcium imaging and RNA-seq were performed in vitro. OGTTs were performed in C57BL/6j and iScd1-/- mice and immunohistochemistry and gene expression analyses were performed ex vivo. RESULTS: Gene Ontology (GO) analysis identified cholesterol biosynthesis as being most affected by RYGB. Silencing or chemical inhibition of stearoyl-CoA desaturase 1 (SCD1), a key enzyme in the synthesis of monounsaturated fatty acids, was found to reduce Gcg expression and secretion of GLP-1 by GLUTag and STC-1 cells. Scd1 knockdown also reduced intracellular Ca2+ signalling and membrane depolarisation. Furthermore, Scd1 mRNA expression was found to be regulated by NEFAs but not glucose. RNA-seq of SCD1 inhibitor-treated GLUTag cells identified altered expression of genes implicated in ATP generation and glycolysis. Finally, gene expression and immunohistochemical analysis of the jejunum of the intestine-specific Scd1 knockout mouse model, iScd1-/-, revealed a twofold higher L cell density and a twofold increase in Gcg mRNA expression. CONCLUSIONS/INTERPRETATION: RYGB caused robust alterations in the jejunal transcriptome, with genes involved in cholesterol biosynthesis being most affected. Our data highlight SCD as an RYGB-regulated L cell constituent that regulates the production and secretion of GLP-1.

Does a prior diagnosis of infectious disease confer an increased risk of latent autoimmune diabetes in adults?

AIMS: Infections are proposed risk factors for type 1 diabetes in children. We examined whether a diagnosis of infectious disease also confers an increased risk of latent autoimmune diabetes in adults (LADA). MATERIALS AND METHODS: We used data from a population-based Swedish case-control study with incident cases of LADA (n = 597) and matched controls (n = 2386). The history of infectious disease was ascertained through national and regional patient registers. We estimated adjusted odds ratios (OR) with 95% confidence intervals for ≥1 respiratory (any/upper/lower), gastrointestinal, herpetic, other or any infectious disease episode, or separately, for 1 and ≥2 infectious disease episodes, within 0-1, 1-3, 3-5 and 5-10 years before LADA diagnosis/matching. Stratified analyses were performed on the basis of HLA risk genotypes and Glutamic acid decarboxylase antibodies (GADA) levels. RESULTS: Individuals who developed LADA did not have a higher prevalence of infectious disease 1-10 years before diabetes diagnosis. For example, OR was estimated at 0.87 (0.66, 1.14) for any versus no respiratory infectious disease within 1-3 years. Similar results were seen for LADA with high-risk HLA genotypes (OR 0.95 [0.64, 1.42]) or high GADA levels (OR 1.10 [0.79, 1.55]), ≥2 episodes (OR 0.89 [0.56, 1.40]), and in infections treated using antibiotics (OR 1.03 [0.73, 1.45]). The only significant association was observed with lower respiratory disease the year preceding LADA diagnosis (OR 1.67 [1.06, 2.64]). CONCLUSIONS: Our findings do not support the idea that exposure to infections increases the risk of LADA. A higher prevalence of respiratory infection in the year before LADA diagnosis could reflect increased susceptibility to infections due to hyperglycemia.

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial.

AIMS/HYPOTHESIS: Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). METHODS: Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. RESULTS: A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. CONCLUSIONS/INTERPRETATION: We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. TRIAL REGISTRATION: ORIGIN trial, ClinicalTrials.gov NCT00069784.

Smoking, use of smokeless tobacco, HLA genotypes and incidence of latent autoimmune diabetes in adults.

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 ).

Phenotypic and genetic classification of diabetes.

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.

The role of circulating galectin-1 in type 2 diabetes and chronic kidney disease: evidence from cross-sectional, longitudinal and Mendelian randomisation analyses.

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.

Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver.

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.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.

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 (eGFR<60 ml/min per 1.73 m2) 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<2.7×10-6). 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<1.5×10-11). 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<2.0×10-16]). 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 ).

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

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, HbA1c, 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, HbA1c, 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.

Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health.

There is currently limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). We aimed to identify genetic loci associated with OSA risk, and to test if OSA and its comorbidities share a common genetic background.We conducted the first large-scale genome-wide association study of OSA using the FinnGen study (217 955 individuals) with 16 761 OSA patients identified using nationwide health registries.We estimated 0.08 (95% CI 0.06-0.11) heritability and identified five loci associated with OSA (p<5.0×10-8): rs4837016 near GAPVD1 (GTPase activating protein and VPS9 domains 1), rs10928560 near CXCR4 (C-X-C motif chemokine receptor type 4), rs185932673 near CAMK1D (calcium/calmodulin-dependent protein kinase ID) and rs9937053 near FTO (fat mass and obesity-associated protein; a variant previously associated with body mass index (BMI)). In a BMI-adjusted analysis, an association was observed for rs10507084 near RMST/NEDD1 (rhabdomyosarcoma 2 associated transcript/NEDD1 γ-tubulin ring complex targeting factor). We found high genetic correlations between OSA and BMI (rg=0.72 (95% CI 0.62-0.83)), and with comorbidities including hypertension, type 2 diabetes, coronary heart disease, stroke, depression, hypothyroidism, asthma and inflammatory rheumatic disease (rg>0.30). The polygenic risk score for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile, and Mendelian randomisation supported a causal relationship between BMI and OSA.Our findings support the causal link between obesity and OSA, and the joint genetic basis between OSA and comorbidities.

Adult-onset diabetes in Middle Eastern immigrants to Sweden: Novel subgroups and diabetic complications-The All New Diabetes in Scania cohort diabetic complications and ethnicity.

BACKGROUND: Middle Eastern immigrants to Europe represent a high risk population for type 2 diabetes. We compared prevalence of novel subgroups and assessed risk of diabetic macro- and microvascular complications between diabetes patients of Middle Eastern and European origin. METHODS: This study included newly diagnosed diabetes patients born in Sweden (N = 10641) or Iraq (N = 286), previously included in the All New Diabetes in Scania cohort. The study was conducted between January 2008 and August 2016. Patients were followed to April 2017. Incidence rates in diabetic macro- and microvascular complications were assessed using cox-regression adjusting for the confounding effect of age at onset, sex, anthropometrics, glomerular filtration rate (eGFR) and HbA1c. FINDINGS: In Iraqi immigrants versus native Swedes, severe insulin-deficient diabetes was almost twice as common (27.9 vs. 16.2% p < 0.001) but severe insulin-resistant diabetes was less prevalent. Patients born in Iraq had higher risk of coronary events (hazard ratio [HR] 1.84, 95% CI 1.06-3.12) but considerably lower risk of chronic kidney disease (CKD) than Swedes (HR 0.19; 0.05-0.76). The lower risk in Iraqi immigrants was partially attributed to better eGFR. Genetic risk scores (GRS) showed more genetic variants associated with poor insulin secretion but lower risk of insulin resistance in the Iraqi than native Swedish group. INTERPRETATION: People with diabetes, born in the Middle East present with a more insulin-deficient phenotype and genotype than native Swedes. They have a higher risk of coronary events but lower risk of CKD. Ethnic differences should be considered in the preventive work towards diabetes and its complications.

Consumption of red meat, genetic susceptibility, and risk of LADA and type 2 diabetes.

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.

Glucose-dependent insulinotropic peptide and risk of cardiovascular events and mortality: a prospective study.

AIMS/HYPOTHESIS: Evidence that glucose-dependent insulinotropic peptide (GIP) and/or the GIP receptor (GIPR) are involved in cardiovascular biology is emerging. We hypothesised that GIP has untoward effects on cardiovascular biology, in contrast to glucagon-like peptide 1 (GLP-1), and therefore investigated the effects of GIP and GLP-1 concentrations on cardiovascular disease (CVD) and mortality risk. METHODS: GIP concentrations were successfully measured during OGTTs in two independent populations (Malmö Diet Cancer-Cardiovascular Cohort [MDC-CC] and Prevalence, Prediction and Prevention of Diabetes in Botnia [PPP-Botnia]) in a total of 8044 subjects. GLP-1 (n = 3625) was measured in MDC-CC. The incidence of CVD and mortality was assessed via national/regional registers or questionnaires. Further, a two-sample Mendelian randomisation (2SMR) analysis between the GIP pathway and outcomes (coronary artery disease [CAD] and myocardial infarction) was carried out using a GIP-associated genetic variant, rs1800437, as instrumental variable. An additional reverse 2SMR was performed with CAD as exposure variable and GIP as outcome variable, with the instrumental variables constructed from 114 known genetic risk variants for CAD. RESULTS: In meta-analyses, higher fasting levels of GIP were associated with risk of higher total mortality (HR[95% CI] = 1.22 [1.11, 1.35]; p = 4.5 × 10-5) and death from CVD (HR[95% CI] 1.30 [1.11, 1.52]; p = 0.001). In accordance, 2SMR analysis revealed that increasing GIP concentrations were associated with CAD and myocardial infarction, and an additional reverse 2SMR revealed no significant effect of CAD on GIP levels, thus confirming a possible effect solely of GIP on CAD. CONCLUSIONS/INTERPRETATION: In two prospective, community-based studies, elevated levels of GIP were associated with greater risk of all-cause and cardiovascular mortality within 5-9 years of follow-up, whereas GLP-1 levels were not associated with excess risk. Further studies are warranted to determine the cardiovascular effects of GIP per se.

Genotypes of HLA, TCF7L2, and FTO as potential modifiers of the association between sweetened beverage consumption and risk of LADA and type 2 diabetes.

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.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Serum kidney injury molecule 1 and ß2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes.

AIMS/HYPOTHESIS: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. METHODS: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min-1 [1.73 m]-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. RESULTS: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and ß2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. CONCLUSIONS/INTERPRETATION: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30-75 ml min-1 [1.73 m]-2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

A novel rare CUBN variant and three additional genes identified in Europeans with and without diabetes: results from an exome-wide association study of albuminuria.

AIMS/HYPOTHESIS: Identifying rare coding variants associated with albuminuria may open new avenues for preventing chronic kidney disease and end-stage renal disease, which are highly prevalent in individuals with diabetes. Efforts to identify genetic susceptibility variants for albuminuria have so far been limited, with the majority of studies focusing on common variants. METHODS: We performed an exome-wide association study to identify coding variants in a two-stage (discovery and replication) approach. Data from 33,985 individuals of European ancestry (15,872 with and 18,113 without diabetes) and 2605 Greenlanders were included. RESULTS: We identified a rare (minor allele frequency [MAF]: 0.8%) missense (A1690V) variant in CUBN (rs141640975, ß = 0.27, p = 1.3 × 10-11) associated with albuminuria as a continuous measure in the combined European meta-analysis. The presence of each rare allele of the variant was associated with a 6.4% increase in albuminuria. The rare CUBN variant had an effect that was three times stronger in individuals with type 2 diabetes compared with those without (pinteraction = 7.0 × 10-4, ß with diabetes = 0.69, ß without diabetes = 0.20) in the discovery meta-analysis. Gene-aggregate tests based on rare and common variants identified three additional genes associated with albuminuria (HES1, CDC73 and GRM5) after multiple testing correction (pBonferroni < 2.7 × 10-6). CONCLUSIONS/INTERPRETATION: The current study identifies a rare coding variant in the CUBN locus and other potential genes associated with albuminuria in individuals with and without diabetes. These genes have been implicated in renal and cardiovascular dysfunction. The findings provide new insights into the genetic architecture of albuminuria and highlight target genes and pathways for the prevention of diabetes-related kidney disease.

Overweight, obesity and the risk of LADA: results from a Swedish case-control study and the Norwegian HUNT Study.

AIMS/HYPOTHESIS: Excessive weight is a risk factor for type 2 diabetes, but its role in the promotion of autoimmune diabetes is not clear. We investigated the risk of latent autoimmune diabetes in adults (LADA) in relation to overweight/obesity in two large population-based studies. METHODS: Analyses were based on incident cases of LADA (n = 425) and type 2 diabetes (n = 1420), and 1704 randomly selected control participants from a Swedish case-control study and prospective data from the Norwegian HUNT Study including 147 people with LADA and 1,012,957 person-years of follow-up (1984-2008). We present adjusted ORs and HRs with 95% CI. RESULTS: In the Swedish data, obesity was associated with an increased risk of LADA (OR 2.93, 95% CI 2.17, 3.97), which was even stronger for type 2 diabetes (OR 18.88, 95% CI 14.29, 24.94). The association was stronger in LADA with low GAD antibody (GADA;

The Genetic Landscape of Renal Complications in Type 1 Diabetes.

Diabetes is the leading cause of ESRD. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (P=6.4×10-3). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole-exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (P=2.2×10-5) and the risk of type 2 diabetes (P=6.1×10-4) associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (P=1.1×10-4). Pathway analysis implicated ascorbate and aldarate metabolism (P=9.0×10-6), and pentose and glucuronate interconversions (P=3.0×10-6) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.