| 1. |
- Haghighi, Mona, et al.
(författare)
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A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
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| 2. |
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| 3. |
- Ahlqvist, Emma, et al.
(författare)
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Novel subgroups of adult-onset diabetes and their association with outcomes : a data-driven cluster analysis of six variables
- 2018
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Ingår i: The Lancet Diabetes and Endocrinology. - 2213-8587 .- 2213-8595. ; 6:5, s. 361-369
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundDiabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.MethodsWe did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.FindingsWe identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.InterpretationWe stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
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| 5. |
- Aydemir, O, et al.
(författare)
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Genetic Variation Within the HLA-DRA1 Gene Modulates Susceptibility to Type 1 Diabetes in HLA-DR3 Homozygotes
- 2019
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Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 68:7, s. 1523-1527
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Tidskriftsartikel (refereegranskat)abstract
- Type 1 diabetes (T1D) involves the interaction of multiple gene variants, environmental factors, and immunoregulatory dysfunction. Major T1D genetic risk loci encode HLA-DR and -DQ. Genetic heterogeneity and linkage disequilibrium in the highly polymorphic HLA region confound attempts to identify additional T1D susceptibility loci. To minimize HLA heterogeneity, T1D patients (N = 365) and control subjects (N = 668) homozygous for the HLA-DR3 high-risk haplotype were selected from multiple large T1D studies and examined to identify new T1D susceptibility loci using molecular inversion probe sequencing technology. We report that risk for T1D in HLA-DR3 homozygotes is increased significantly by a previously unreported haplotype of three single nucleotide polymorphisms (SNPs) within the first intron of HLA-DRA1. The homozygous risk haplotype has an odds ratio of 4.65 relative to the protective homozygous haplotype in our sample. Individually, these SNPs reportedly function as “expression quantitative trait loci,” modulating HLA-DR and -DQ expression. From our analysis of available data, we conclude that the tri-SNP haplotype within HLA-DRA1 may modulate class II expression, suggesting that increased T1D risk could be attributable to regulated expression of class II genes. These findings could help clarify the role of HLA in T1D susceptibility and improve diabetes risk assessment, particularly in high-risk HLA-DR3 homozygous individuals.
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| 6. |
- Battaglia, Manuela, et al.
(författare)
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Understanding and preventing type 1 diabetes through the unique working model of TrialNet
- 2017
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 60:11, s. 2139-2147
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Tidskriftsartikel (refereegranskat)abstract
- Type 1 diabetes is an autoimmune disease arising from the destruction of pancreatic insulin-producing beta cells. The disease represents a continuum, progressing sequentially at variable rates through identifiable stages prior to the onset of symptoms, through diagnosis and into the critical periods that follow, culminating in a variable depth of beta cell depletion. The ability to identify the very earliest of these presymptomatic stages has provided a setting in which prevention strategies can be trialled, as well as furnishing an unprecedented opportunity to study disease evolution, including intrinsic and extrinsic initiators and drivers. This niche opportunity is occupied by Type 1 Diabetes TrialNet, an international consortium of clinical trial centres that leads the field in intervention and prevention studies, accompanied by deep longitudinal bio-sampling. In this review, we focus on discoveries arising from this unique bioresource, comprising more than 70,000 samples, and outline the processes and science that have led to new biomarkers and mechanistic insights, as well as identifying new challenges and opportunities. We conclude that via integration of clinical trials and mechanistic studies, drawing in clinicians and scientists and developing partnership with industry, TrialNet embodies an enviable and unique working model for understanding a disease that to date has no cure and for designing new therapeutic approaches.
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| 8. |
- Beyerlein, Andreas, et al.
(författare)
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Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors : Results from the prospective TEDDY study
- 2019
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Ingår i: Journal of Medical Genetics. - : BMJ. - 0022-2593 .- 1468-6244. ; 56:9, s. 602-605
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Tidskriftsartikel (refereegranskat)abstract
- Background: Progression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown. Methods: In 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression. Results: Islet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93). Conclusions: Genetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.
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| 9. |
- Bonifacio, Ezio, et al.
(författare)
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Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes : A prospective study in children
- 2018
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Ingår i: PLoS Medicine. - : Public Library of Science (PLoS). - 1549-1676. ; 15:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes. Methods and findings: The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%–6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%–4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%–13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%–4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%–9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%–3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%–54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%–60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case–control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations. Conclusions: A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.
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| 10. |
- Bybrant, M. C., et al.
(författare)
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Tissue transglutaminase autoantibodies in children with newly diagnosed type 1 diabetes are related to human leukocyte antigen but not to islet autoantibodies: A Swedish nationwide prospective population-based cohort study
- 2018
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Ingår i: Autoimmunity. - : Informa UK Limited. - 0891-6934 .- 1607-842X. ; 51:5, s. 221-227
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: This study explored the association between tissue transglutaminase autoantibody (tTGA), high-risk human leucocyte antigen (HLA) genotypes and islet autoantibodies in children with newly diagnosed type 1 diabetes (T1D).Patients and methods: Dried blood spots and serum samples were taken at diagnosis from children <18years of age participating in Better Diabetes Diagnosis (BDD), a Swedish nationwide prospective cohort study of children newly diagnosed with T1D. We analyzed tTGA, high-risk HLA DQ2 and DQ8 (DQX is neither DQ2 nor DQ8) and islet auto-antibodies (GADA, IA-2A, IAA, and three variants of Zinc transporter; ZnT8W, ZnT8R, and ZnT8QA).Results: Out of 2705 children diagnosed with T1D, 85 (3.1%) had positive tTGA and 63 (2.3%) had borderline values. The prevalence of tTGA was higher in children with the HLA genotypes DQ2/2, DQ2/X or DQ2/8 compared to those with DQ8/8 or DQ8/X (p=.00001) and those with DQX/X (p.00001). No significant differences were found in relation to islet autoantibodies or age at diagnosis, but the presence of tTGA was more common in girls than in boys (p=.018).Conclusion: tTGA at T1D diagnosis (both positive and borderline values 5.4%) was higher in girls and in children homozygous for DQ2/2, followed by children heterozygous for DQ2. Only children with DQ2 and/or DQ8 had tTGA. HLA typing at the diagnosis of T1D can help to identify those without risk for CD.
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