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'As a community researcher, you get to make decisions in the research project’ – an autoethnographic study about a joint research processIn a participatory action research project, we emphasize experiences of collaboration between academic and community researchers by applying analytical autoethnography. The aim of the article is to describe the research process which involves both individual and collaborative processes, and to analyze challenges in relation to participation in the ongoing research process. We identified four themes: Start-up and initial challenges, Conditions and structural prerequisites for collaboration, Joint development of work methods and Power and role distribution. Our findings are presented in two separate analyses; a collaborative inductive analysis and an academic led theoretical analysis in which Arnstein’s ladder of participation and Fricker’s concept of epistemic injustice are utilized in order to scrutinize challenges related to different degrees of participation in the research process. The results demonstrate that shared hermeneutic resources are necessary for the mitigation of epistemic injustice and enablement of mutual learning processes, such as collective writing processes. The results also indicate that a full participation for community researchers in the entire research process was difficult to achieve, both in relation to structural resources such as allocated time, and in relation to perceptions of meaning- making aspects, for example, individual interests and contributionsin terms of knowledge.

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.

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.

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

Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes (T2D). In the current study we replicate and cross-validate these T2D clusters in three large cohorts using readily measured variables in the clinic.MethodsIn this cross-sectional study, 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide and HDL in three independent cohorts. 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.ResultsFive distinct T2D clusters were identified and mapped back to the original four ANDIS clusters. Using C-peptide and HDL instead of HOMA-B and HOMA-S three of the clusters mapped with high sensitivity (80.6 – 90.7%) to the previously identified Severe Insulin Deficient (SIDD), Severe insulin resistant (SIRD) and Obese (MOD) clusters. The previously described ANDIS MARD cluster could be mapped to the two milder groups in our study – one characterised by high HDL, and the other having not any extreme characteristic (MDH cluster). When these two milder groups were combined they mapped well to the previously labelled MARD cluster (sensitivity 79.4%). In the cross-validation between cohorts, particularly the SIDD and MDH cluster 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.Conclusions/interpretationClusters based on C-peptide instead of HOMA measures result in clusters that resemble those based on HOMA measures, especially for SIDD, SIRD and MOD. By adding HDL, the MARD cluster based upon HOMA measures resulted in the current clustering in 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, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts.

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.

BackgroundType 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers.MethodsGenome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires.ResultsSevere insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P−9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10×10−8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3×10−9).ConclusionsNew diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis.Research in contextEvidence before this studyIn March 2018 we suggested a novel subclassification of diabetes into five subtypes. This classification was based on clustering using clinical parameters commonly measured at diabetes diagnosis (age at diabetes onset, HbA1c, bodymass index, presence of GAD autoantibodies and HOMA2 indices for insulin resistance and secretion). These subtypes differed with respect to clinical characteristics, disease progression and risk of complications, but it remained unclear to what extent these subtypes have different underlying pathologies. In our original publication we analysed a small set of genetic risk variants for diabetes and found differential associations between subtypes, suggesting potential aetiological differences.Added value of this studyIn this study we have conducted a full genome analysis of the original ANDIS cohort, including genome-wide association studies and polygenic risk score analysis with replication in an independent cohort. We have also compared heritability and prevalence of having a family history of diabetes in the subtypes.Implications of all the available evidenceWe demonstrate that stratification into subtypes facilitates identification of genetic risk loci and that the aetiology of the subtypes is at least partially distinct. These results are especially important for the future study and treatment of individuals belonging to the severe insulin-resistant diabetes (SIRD) subtype, whose pathogenesis appears to differ substantially from that of traditional T2D.

OBJECTIVE The MHC region harbors the strongest loci for latent autoimmune diabetes in adults (LADA); however, the strength of association is likely attenuated compared with that for childhood-onset type 1 diabetes. In this study, we recapitulate independent effects in the MHC class I region in a population with type 1 diabetes and then determine whether such conditioning in LADA yields potential genetic discriminators between the two subtypes within this region. RESEARCH DESIGN AND METHODS Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects). RESULTS The strongest associations in the MHC class II region (rs3957146, β [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (β [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, β [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA. CONCLUSIONS In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

Objective Gestational diabetes mellitus (GDM) is a transient form of diabetes characterized by impaired insulin secretion and action during pregnancy. Population-based differences in prevalence exist which could be explained by phenotypic and genetic differences. The aim of this study was to examine these differences in pregnant women from Punjab, India and Scandinavia. Methods Eighty-five GDM/T2D loci in European and/or Indian populations from previous studies were assessed for association with GDM based on Swedish GDM criteria in 4018 Punjabi Indian and 507 Swedish pregnant women. Selected loci were replicated in Scandinavian cohorts, Radiel (N = 398, Finnish) and STORK/STORK-G (N = 780, Norwegian). Results Punjabi Indian women had higher GDM prevalence, lower insulin secretion and better insulin sensitivity than Swedish women. There were significant frequency differences of GDM/T2D risk alleles between both populations. rs7178572 at HMG20A, previously associated with GDM in South Indian and European women, was replicated in North Indian women. The T2D risk SNP rs11605924 in the CRY2 gene was associated with increased GDM risk in Scandinavian but decreased GDM risk in Punjabi Indian women. No other overlap was seen between GDM loci in both populations. Conclusions Gestational diabetes mellitus is more common in Indian than Swedish women, which partially can be attributed to differences in insulin secretion and action. There was marked heterogeneity in the GDM phenotypes between the populations which could only partially be explained by genetic differences.

OBJECTIVE Latent autoimmune diabetes in adults (LADA) shares clinical features with both type 1 and type 2 diabetes; however, there is ongoing debate regarding the precise definition of LADA. Understanding its genetic basis is one potential strategy to gain insight into appropriate classification of this diabetes subtype. RESEARCH DESIGN AND METHODS We performed the first genome-wide association study of LADA in case subjects of European ancestry versus population control subjects (n = 2,634 vs. 5,947) and compared against both case subjects with type 1 diabetes (n = 2,454 vs. 968) and type 2 diabetes (n = 2,779 vs. 10,396). RESULTS The leading genetic signals were principally shared with type 1 diabetes, although we observed positive genetic correlations genome-wide with both type 1 and type 2 diabetes. Additionally, we observed a novel independent signal at the known type 1 diabetes locus harboring PFKFB3, encoding a regulator of glycolysis and insulin signaling in type 2 diabetes and inflammation and autophagy in autoimmune disease, as well as an attenuation of key type 1–associated HLA haplotype frequencies in LADA, suggesting that these are factors that distinguish childhood-onset type 1 diabetes from adult autoimmune diabetes. CONCLUSIONS Our results support the need for further investigations of the genetic factors that distinguish forms of autoimmune diabetes as well as more precise classification strategies.

Mutations in the gene encoding laminin α2 chain cause congenital muscular dystrophy type 1A. In skeletal muscle, laminin α2 chain binds at least two receptor complexes: the dystrophin-glycoprotein complex and integrin α7β1. To gain insight into the molecular mechanisms underlying this disorder, we performed gene expression profiling of laminin α2 chain-deficient mouse limb muscle. One of the down-regulated genes encodes a protein called Cib2 (calcium- and integrin-binding protein 2) whose expression and function is unknown. However, the closely related Cib1 has been reported to bind integrin αIIb and may be involved in outside-in-signaling in platelets. Since Cib2 might be a novel integrin α7β1-binding protein in muscle, we have studied Cib2 expression in the developing and adult mouse. Cib2 mRNA is mainly expressed in the developing central nervous system and in developing and adult skeletal muscle. In skeletal muscle, Cib2 colocalizes with the integrin α7B subunit at the sarcolemma and at the neuromuscular and myotendinous junctions. Finally, we demonstrate that Cib2 is a calcium-binding protein that interacts with integrin α7Bβ1D. Thus, our data suggest a role for Cib2 as a cytoplasmic effector of integrin α7Bβ1D signaling in skeletal muscle.

Axenfeld-Rieger syndrome (ARS) patients with PITX2 point mutations exhibit a wide range of clinical features including mild craniofacial dysmorphism and dental anomalies. Identifying new PITX2 targets and transcriptional mechanisms are important to understand the molecular basis of these anomalies. Chromatin immunoprecipitation assays demonstrate PITX2 binding to the FoxJ1 promoter and PITX2C transgenic mouse fibroblasts and PITX2-transfected cells have increased endogenous FoxJ1 expression. FoxJ1 is expressed at embryonic day 14.5 (E14.5) in early tooth germs, then down-regulated from E15.5-E17.5 and re-expressed in the inner enamel epithelium, oral epithelium, tongue epithelium, sub-mandibular salivary gland and hair follicles during E18.5 and neonate day 1. FoxJ1 and Pitx2 exhibit overlapping expression patterns in the dental and oral epithelium. PITX2 activates the FoxJ1 promoter and, Lef-1 and beta-catenin interact with PITX2 to synergistically regulate the FoxJ1 promoter. FoxJ1 physically interacts with the PITX2 homeodomain to synergistically regulate FoxJ1, providing a positive feedback mechanism for FoxJ1 expression. Furthermore, FoxJ1, PITX2, Lef-1 and beta-catenin act in concert to activate the FoxJ1 promoter. The PITX2 T68P ARS mutant protein physically interacts with FoxJ1; however, it cannot activate the FoxJ1 promoter. These data indicate a mechanism for the activity of the ARS mutant proteins in specific cell types and provides a basis for craniofacial/ tooth anomalies observed in these patients. These data reveal novel transcriptional mechanisms of FoxJ1 and demonstrate a new role of FoxJ1 in oro-facial morphogenesis.

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10−107) and stratified analyses (all P < 3.3 × 10−30). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the

The relevance of the OGTT in predicting type 2 diabetes is unclear. We assessed the performance of 14 OGTT glucose traits in type 2 diabetes prediction.We studied 2,603 and 2,386 Europeans from the Botnia study and Malmö Prevention Project (MPP) cohorts with baseline OGTT data. Over a follow-up period of 4.94 years and 23.5 years, 155 (5.95%) and 467 (19.57%) participants, respectively, developed type 2 diabetes. The main outcome was incident type 2 diabetes.One-hour plasma glucose (1h-PG) was a fair/good predictor of incident type 2 diabetes in the Botnia study and MPP (AUC for receiver operating characteristic [AUCROC] 0.80 [0.77, 0.84] and 0.70 [0.68, 0.73]). 1h-PG alone outperformed the prediction model of multiple clinical risk factors (age, sex, BMI, family history of type 2 diabetes) in the Botnia study and MPP (AUCROC 0.75 [0.72, 0.79] and 0.67 [0.64, 0.70]). The same clinical risk factors added to 1h-PG modestly increased prediction for incident type 2 diabetes (Botnia, AUCROC 0.83 [0.80, 0.86]; MPP, AUCROC 0.74 [0.72, 0.77]). 1h-PG also outperformed HbA1c in predicting type 2 diabetes in the Botnia cohort. A 1h-PG value of 8.9 mmol/l and 8.4 mmol/l was the optimal cut-point for initial screening and selection of high-risk individuals in the Botnia study and MPP, respectively, and represented 30% and 37% of all participants in these cohorts. High-risk individuals had a substantially increased risk of incident type 2 diabetes (OR 8.0 [5.5, 11.6] and 3.8 [3.1, 4.7]) and captured 75% and 62% of all incident type 2 diabetes in the Botnia study and MPP.1h-PG is a valuable prediction tool for identifying adults at risk for future type 2 diabetes.

BACKGROUND: Laminin alpha2 chain mutations cause congenital muscular dystrophy with dysmyelination neuropathy (MDC1A). Previously, we demonstrated that laminin alpha1 chain ameliorates the disease in mice. Dystroglycan and integrins are major laminin receptors. Unlike laminin alpha2 chain, alpha1 chain binds the receptors by separate domains; laminin globular (LG) domains 4 and LG1-3, respectively. Thus, the laminin alpha1 chain is an excellent tool to distinguish between the roles of dystroglycan and integrins in the neuromuscular system. METHODOLOGY/PRINCIPAL FINDINGS: Here, we provide insights into the functions of laminin alpha1LG domains and the division of their roles in MDC1A pathogenesis and rescue. Overexpression of laminin alpha1 chain that lacks the dystroglycan binding LG4-5 domains in alpha2 chain deficient mice resulted in prolonged lifespan and improved health. Importantly, diaphragm and heart muscles were corrected, whereas limb muscles were dystrophic, indicating that different muscles have different requirements for LG4-5 domains. Furthermore, the regenerative capacity of the skeletal muscle did not depend on laminin alpha1LG4-5. However, this domain was crucial for preventing apoptosis in limb muscles, essential for myelination in peripheral nerve and important for basement membrane assembly. CONCLUSIONS/SIGNIFICANCE: These results show that laminin alpha1LG domains and consequently their receptors have disparate functions in the neuromuscular system. Understanding these interactions could contribute to design and optimization of future medical treatment for MDC1A patients.

The heterotrimeric basement membrane protein laminin-111 is essential for early mouse embryogenesis. Its beta1 and gamma1 chains are crucial for endoderm differentiation and for the formation of basement membranes, whereas alpha1 chain null mice only lack the extraembryonic Reichert's membrane. Nevertheless, mice deficient in the cell-binding alpha1 globular domains 4-5 (LG4-5) have a more severe phenotype than animals devoid of the whole alpha1 chain, as these domains are required for the formation of a polarized ectoderm. However, the influence of the alpha1LG4-5 domains on endoderm differentiation is unclear. We have used microarray analysis to compare the expression profiles of normal and alpha1LG4-5-deficient embryoid bodies and show that genes encoding secreted plasma proteins and proteins involved in endocytosis are reduced in alpha1LG4-5-deficient embryoid bodies, indicating incomplete differentiation of the visceral endoderm. Moreover, mice lacking alpha1LG4-5 display endoderm disorganization and a defective expression of the endoderm marker Dab2. We hypothesize that alpha1LG4-5 domains provide an autocrine signal necessary for the complete differentiation of a functional visceral endoderm and vital signals for the polarization of the epiblast.

Embryonic stein (ES) cells are derived from the inner cell mass of the blastocyst and can be cultured as three-dimensional embryoid bodies (EBs) in which embryonic pregastrulation stages are faithfully mimicked. Fibroblast growth factor receptors (mainly FGFR2) are involved in the first differentiation events during early mammalian embryogenesis. It has been demonstrated that the presence of FGFR2 is a prerequisite for laminin-111 and collagen type IV synthesis and subsequently basement membrane formation in EBs. To identify genes that are influenced by FGFR signalling, we performed global gene expression profiling of differentiating EBs expressing dominant negative FGFR2 (dnFGFR2), acquiring an extensive catalogue of down- and up-regulated genes. We show a strong down-regulation of endodermal and basement membrane related genes, which strengthen the view that the FGFR signalling pathway is a main stimulator of basement membrane synthesis in EBs. We further present down-regulation of genes previously not linked to FGFR signalling, and in addition an active transcription of some mesodermal related genes in differentiating dnFGFR2 EBs.

B lymphocyte development is a complex biological process critically dependent on the transcription factor early B cell factor (EBF). To deepen understanding of the roles for EBF in this process, we have used Pearson correlation analysis to evaluate microarray data from a set of mouse B lymphoid cell lines representing different stages of development. Comparing the expression pattern of EBF to that of the other genes in the data set revealed that VpreB1, mb-1, and lambda5, all known target genes, presented high correlation values to EBF. High correlations were also seen for the VpreB3 and CD19 genes and biochemical as well as functional data supported that they are target genes for EBF even though the expression of CD19 was critically dependent of Pax-5. We also obtained evidence for extensive collaborative actions of EBF and E47 even though microarray analysis of hematopoetic progenitor cells ectopically expressing these proteins suggested that they activated only a subset of pre-B cell restricted genes.