The diabetes susceptibility gene Clec16a regulates mitophagy.

Clec16a has been identified as a disease susceptibility gene for type 1 diabetes, multiple sclerosis, and adrenal dysfunction, but its function is unknown. Here we report that Clec16a is a membrane-associated endosomal protein that interacts with E3 ubiquitin ligase Nrdp1. Loss of Clec16a leads to an increase in the Nrdp1 target Parkin, a master regulator of mitophagy. Islets from mice with pancreas-specific deletion of Clec16a have abnormal mitochondria with reduced oxygen consumption and ATP concentration, both of which are required for normal ß cell function. Indeed, pancreatic Clec16a is required for normal glucose-stimulated insulin release. Moreover, patients harboring a diabetogenic SNP in the Clec16a gene have reduced islet Clec16a expression and reduced insulin secretion. Thus, Clec16a controls ß cell function and prevents diabetes by controlling mitophagy. This pathway could be targeted for prevention and control of diabetes and may extend to the pathogenesis of other Clec16a- and Parkin-associated diseases.

Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms.

Genome-wide association studies have revealed numerous risk loci associated with diverse diseases. However, identification of disease-causing variants within association loci remains a major challenge. Divergence in gene expression due to cis-regulatory variants in noncoding regions is central to disease susceptibility. We show that integrative computational analysis of phylogenetic conservation with a complexity assessment of co-occurring transcription factor binding sites (TFBS) can identify cis-regulatory variants and elucidate their mechanistic role in disease. Analysis of established type 2 diabetes risk loci revealed a striking clustering of distinct homeobox TFBS. We identified the PRRX1 homeobox factor as a repressor of PPARG2 expression in adipose cells and demonstrate its adverse effect on lipid metabolism and systemic insulin sensitivity, dependent on the rs4684847 risk allele that triggers PRRX1 binding. Thus, cross-species conservation analysis at the level of co-occurring TFBS provides a valuable contribution to the translation of genetic association signals to disease-related molecular mechanisms.

Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes.

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

Life-long impairment of glucose homeostasis upon prenatal exposure to psychostimulants.

Maternal drug abuse during pregnancy is a rapidly escalating societal problem. Psychostimulants, including amphetamine, cocaine, and methamphetamine, are amongst the illicit drugs most commonly consumed by pregnant women. Neuropharmacology concepts posit that psychostimulants affect monoamine signaling in the nervous system by their affinities to neurotransmitter reuptake and vesicular transporters to heighten neurotransmitter availability extracellularly. Exacerbated dopamine signaling is particularly considered as a key determinant of psychostimulant action. Much less is known about possible adverse effects of these drugs on peripheral organs, and if in utero exposure induces lifelong pathologies. Here, we addressed this question by combining human RNA-seq data with cellular and mouse models of neuroendocrine development. We show that episodic maternal exposure to psychostimulants during pregnancy coincident with the intrauterine specification of pancreatic ß cells permanently impairs their ability of insulin production, leading to glucose intolerance in adult female but not male offspring. We link psychostimulant action specifically to serotonin signaling and implicate the sex-specific epigenetic reprogramming of serotonin-related gene regulatory networks upstream from the transcription factor Pet1/Fev as determinants of reduced insulin production.

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 ).

Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial.

AIMS/HYPOTHESIS: Data analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care. METHODS: In total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2 years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA1c ≥6.5%, between subtypes. RESULTS: The five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001). CONCLUSIONS/INTERPRETATION: Cluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes. TRIAL REGISTRATION: ORIGIN trial, ClinicalTrials.gov NCT00069784.

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.

A multigenerational study on phenotypic consequences of the most common causal variant of HNF1A-MODY.

AIMS/HYPOTHESIS: Systematic studies on the phenotypic consequences of variants causal of HNF1A-MODY are rare. Our aim was to assess the phenotype of carriers of a single HNF1A variant and genetic and clinical factors affecting the clinical spectrum. METHODS: We conducted a family-based multigenerational study by comparing heterozygous carriers of the HNF1A p.(Gly292fs) variant with the non-carrier relatives irrespective of diabetes status. During more than two decades, 145 carriers and 131 non-carriers from 12 families participated in the study, and 208 underwent an OGTT at least once. We assessed the polygenic risk score for type 2 diabetes, age at onset of diabetes and measures of body composition, as well as plasma glucose, serum insulin, proinsulin, C-peptide, glucagon and NEFA response during the OGTT. RESULTS: Half of the carriers remained free of diabetes at 23 years, one-third at 33 years and 13% even at 50 years. The median age at diagnosis was 21 years (IQR 17-35). We could not identify clinical factors affecting the age at conversion; sex, BMI, insulin sensitivity or parental carrier status had no significant effect. However, for 1 SD unit increase of a polygenic risk score for type 2 diabetes, the predicted age at diagnosis decreased by 3.2 years. During the OGTT, the carriers had higher levels of plasma glucose and lower levels of serum insulin and C-peptide than the non-carriers. The carriers were also leaner than the non-carriers (by 5.0 kg, p=0.012, and by 2.1 kg/m2 units of BMI, p=2.2 × 10-4, using the first adult measurements) and, possibly as a result of insulin deficiency, demonstrated higher lipolytic activity (with medians of NEFA at fasting 621 vs 441 µmol/l, p=0.0039; at 120 min during an OGTT 117 vs 64 µmol/l, p=3.1 × 10-5). CONCLUSIONS/INTERPRETATION: The most common causal variant of HNF1A-MODY, p.(Gly292fs), presents not only with hyperglycaemia and insulin deficiency, but also with increased lipolysis and markedly lower adult BMI. Serum insulin was more discriminative than C-peptide between carriers and non-carriers. A considerable proportion of carriers develop diabetes after young adulthood. Even among individuals with a monogenic form of diabetes, polygenic risk of diabetes modifies the age at onset of diabetes.

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 ).

HLA class I genes modulate disease risk and age at onset together with DR-DQ in Chinese patients with insulin-requiring type 1 diabetes.

AIMS/HYPOTHESIS: The study aimed to investigate the effects of HLA class I genes on susceptibility to type 1 diabetes with different onset ages, in addition to the well-established effects of HLA class II genes. METHODS: A total of 361 patients with type 1 diabetes (192 patients with onset <18 years and 169 patients with onset ≥18 years) and 500 healthy control participants from China were enrolled and genotyped for the HLA-A, -B, -C, -DQA1, -DQB1 and -DRB1 genes using next-generation sequencing. RESULTS: The susceptible DR3 (ß = -0.09, p = 0.0009) and DR4-DQ8 (ß = -0.13, p = 0.0059) haplotypes were negatively associated with onset age, while the protective DR11 (ß = 0.21, p = 0.0314) and DR12 (ß = 0.27, p < 0.0001) haplotypes were positively associated with onset age. After adjustment for linkage disequilibrium with DR-DQ haplotypes, A*11:01:01 was positively associated with onset age (ß = 0.06, p = 0.0370), while the susceptible C*15:02:01 was negatively associated with onset age (ß = -0.21, p = 0.0050). The unit for ß was double square-root (fourth root) transformed years of change in onset age associated with per copy of the HLA haplotype/allele. In addition, B*46:01:01 was protective (OR 0.41, 0.46; pc [corrected for multiple comparisons] = 0.0044, 0.0040), whereas A*24:02:01 (OR 2.71, 2.25; pc = 0.0003, 0.0002) and B*54:01:01 (OR 3.96, 3.79; pc = 0.0018, 0.0004) were predisposing in both the <18 group and the ≥18 group compared with healthy control participants. In the context of DR4-DQ4, A*11:01:01 (61.29% vs 28.26%, pc = 0.0144) was increased while the predisposing A*24:02:01 (19.35% vs 47.83%, pc = 0.0403) was decreased in patients with onset ≥18 years when compared with patients with onset <18 years. CONCLUSIONS/INTERPRETATION: In addition to DR-DQ haplotypes, novel HLA class I alleles were detected to play a role in susceptibility to type 1 diabetes with different onset ages, which could improve the understanding of disease heterogeneity and has implications for the design of future studies.

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.

A TRPV1-to-secretagogin regulatory axis controls pancreatic ß-cell survival by modulating protein turnover.

Ca2+-sensor proteins are generally implicated in insulin release through SNARE interactions. Here, secretagogin, whose expression in human pancreatic islets correlates with their insulin content and the incidence of type 2 diabetes, is shown to orchestrate an unexpectedly distinct mechanism. Single-cell RNA-seq reveals retained expression of the TRP family members in ß-cells from diabetic donors. Amongst these, pharmacological probing identifies Ca2+-permeable transient receptor potential vanilloid type 1 channels (TRPV1) as potent inducers of secretagogin expression through recruitment of Sp1 transcription factors. Accordingly, agonist stimulation of TRPV1s fails to rescue insulin release from pancreatic islets of glucose intolerant secretagogin knock-out(-/-) mice. However, instead of merely impinging on the SNARE machinery, reduced insulin availability in secretagogin-/- mice is due to ß-cell loss, which is underpinned by the collapse of protein folding and deregulation of secretagogin-dependent USP9X deubiquitinase activity. Therefore, and considering the desensitization of TRPV1s in diabetic pancreata, a TRPV1-to-secretagogin regulatory axis seems critical to maintain the structural integrity and signal competence of ß-cells.

Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland.

Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assembled a comprehensive view of recent population history (≤100 generations), the timespan during which most rare-disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to that of 16,060 Swedes, Estonians, Russians, and Hungarians from geographically and linguistically adjacent countries with different population histories. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from more than 25,000 individuals, we find that although haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland typically share several-fold more of their genome in identity-by-descent segments than individuals from southwest regions. We estimate recent effective population-size changes through time across regions of Finland, and we find that there was more continuous gene flow as Finns migrated from southwest to northeast between the early- and late-settlement regions than was dichotomously described previously. Lastly, we show that haplotype sharing is locally enriched by an order of magnitude among pairs of individuals sharing rare alleles and especially among pairs sharing rare disease-causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

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.

Adult-onset type 1 diabetic patients with less severe clinical manifestation have less risk DR-DQ genotypes than childhood-onset patients.

BACKGROUND: The aim of this study was to investigate differences in clinical features and HLA genotypes between adult-onset and childhood-onset patients with type 1 diabetes in a Chinese population. MATERIALS AND METHODS: This study enrolled 716 Han Chinese patients with type 1 diabetes from Guangdong (258 childhood-onset and 458 adult-onset) to compare their clinical features. Of them 214 patients with classical type 1 diabetes (100 childhood-onset and 114 adult-onset) were selected for HLA DR and DQ genotyping by next-generation sequencing. RESULTS: Adult-onset patients were characterized by longer duration of symptoms before diagnosis, lower frequency of DKA at disease onset, less frequent autoantibody positivity, higher serum C-peptide concentrations, and better glycemic control. These findings were replicated in the restricted cohort of 214 patients with classical type 1 diabetes. Compared with childhood-onset patients, adult-onset patients had a lower frequency of the DR9 haplotype, as well as lower frequency of high-risk DR3/DR4 and DR3/DR9 genotypes, but higher frequency of DR3/DR3 genotype and DR3/X, DR4/X or DR9/X (X, non-risk) genotypes. CONCLUSIONS: Adult-onset type 1 diabetic patients with susceptible haplotypes (DR3, DR4 or DR9) were more likely to carry protective DR-DQ haplotypes than childhood-onset patients, which suggested the association between less risk DR-DQ genotypes and the less severe clinical manifestation in adult-onset patients.

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.

Relationship between insulin sensitivity and gene expression in human skeletal muscle.

BACKGROUND: Insulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood. METHODS: To explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR). RESULTS: We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A. CONCLUSIONS: The muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.

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.