scholarly journals Type 1 diabetes can present before the age of 6 months and is characterised by autoimmunity and rapid loss of beta cells

Diabetologia ◽  
2020 ◽  
Vol 63 (12) ◽  
pp. 2605-2615 ◽  
Author(s):  
Matthew B. Johnson ◽  
◽  
Kashyap A. Patel ◽  
Elisa De Franco ◽  
William Hagopian ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Secretion ◽  
Genetic Risk ◽  
Early Onset ◽  
Genetic Risk Score ◽  
Neonatal Diabetes ◽  
Pathogenic Variant ◽  
Rapid Loss ◽  
C Peptide

Abstract Aims/hypothesis Diabetes diagnosed at <6 months of age is usually monogenic. However, 10–15% of affected infants do not have a pathogenic variant in one of the 26 known neonatal diabetes genes. We characterised infants diagnosed at <6 months of age without a pathogenic variant to assess whether polygenic type 1 diabetes could arise at early ages. Methods We studied 166 infants diagnosed with type 1 diabetes at <6 months of age in whom pathogenic variants in all 26 known genes had been excluded and compared them with infants with monogenic neonatal diabetes (n = 164) or children with type 1 diabetes diagnosed at 6–24 months of age (n = 152). We assessed the type 1 diabetes genetic risk score (T1D-GRS), islet autoantibodies, C-peptide and clinical features. Results We found an excess of infants with high T1D-GRS: 38% (63/166) had a T1D-GRS >95th centile of healthy individuals, whereas 5% (8/166) would be expected if all were monogenic (p < 0.0001). Individuals with a high T1D-GRS had a similar rate of autoantibody positivity to that seen in individuals with type 1 diabetes diagnosed at 6–24 months of age (41% vs 58%, p = 0.2), and had markedly reduced C-peptide levels (median <3 pmol/l within 1 year of diagnosis), reflecting rapid loss of insulin secretion. These individuals also had reduced birthweights (median z score −0.89), which were lowest in those diagnosed with type 1 diabetes at <3 months of age (median z score −1.98). Conclusions/interpretation We provide strong evidence that type 1 diabetes can present before the age of 6 months based on individuals with this extremely early-onset diabetes subtype having the classic features of childhood type 1 diabetes: high genetic risk, autoimmunity and rapid beta cell loss. The early-onset association with reduced birthweight raises the possibility that for some individuals there was reduced insulin secretion in utero. Comprehensive genetic testing for all neonatal diabetes genes remains essential for all individuals diagnosed with diabetes at <6 months of age.

scholarly journals A type 1 diabetes genetic risk score can discriminate monogenic autoimmunity with diabetes from early-onset clustering of polygenic autoimmunity with diabetes

Diabetologia ◽  
2018 ◽  
Vol 61 (4) ◽  
pp. 862-869 ◽  
Author(s):  
Matthew B. Johnson ◽  
Kashyap A. Patel ◽  
Elisa De Franco ◽  
Jayne A. L. Houghton ◽  
Timothy J. McDonald ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Early Onset ◽  
Genetic Risk Score

1535-P: An Insulin Resistance Genetic Risk Score (IR_GRS) Is Associated with Mortality in Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1535-P
Author(s):  
RACHEL G. MILLER ◽  
TINA COSTACOU ◽  
SUNA ONENGUT-GUMUSCU ◽  
WEI-MIN CHEN ◽  
STEPHEN S. RICH ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score

243-OR: Integrating Genetic Risk Score and Islet Autoantibody Characteristics in the Predictive Model for Type 1 Diabetes in the TrialNet Study

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 243-OR
Author(s):  
LAURIC A. FERRAT ◽  
ANDREA STECK ◽  
HEMANG M. PARIKH ◽  
LU YOU ◽  
SUNA ONENGUT-GUMUSCU ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Predictive Model ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Islet Autoantibody

scholarly journals Prediction of type 1 diabetes at birth: cord blood metabolites versus genetic risk score in the MoBa cohort

2021 ◽  
Author(s):  
German Tapia ◽  
Tommi Suvitaival ◽  
Linda Ahonen ◽  
Nicolai A Lund-Blix ◽  
Pål R Njølstad ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Cord Blood ◽  
Bile Acids ◽  
Risk Score ◽  
Genetic Risk ◽  
Risk Group ◽  
Genetic Risk Score ◽  
Blood Concentrations ◽  
Polar Metabolites

Abstract Background and aim Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes. Methods Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low molecular weight metabolites (including amino acids, small organic acids and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father and Child (MoBa) cohort. We analysed the data using logistic regression (estimating odds ratios per standard deviation [aOR]), area under the receiver operating characteristic curve (AUC) and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA SNPs, and a four-category HLA risk group. Results The strongest associations for metabolites were aminoadipic acid (aOR=1.23,95%CI:0.97–1.55), indoxyl sulfate (aOR=1.15,95%CI:0.87–1.51), and tryptophan (aOR=0.84,95%CI:0.65–1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified six clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), while the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78. Conclusions In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

scholarly journals Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study

2018 ◽  
Vol 56 (9) ◽  
pp. 602-605 ◽  
Author(s):  
Andreas Beyerlein ◽  
Ezio Bonifacio ◽  
Kendra Vehik ◽  
Markus Hippich ◽  
Christiane Winkler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Islet Autoimmunity ◽  
Islet Autoantibody ◽  
Clinical Type

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 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.ResultsIslet 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).ConclusionsGenetic 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.

scholarly journals Type 1 diabetes genetic risk score discriminates between monogenic and Type 1 diabetes in children diagnosed at the age of <5 years in the Iranian population

2019 ◽  
Vol 36 (12) ◽  
pp. 1694-1702 ◽  
Author(s):  
H. Yaghootkar ◽  
F. Abbasi ◽  
N. Ghaemi ◽  
A. Rabbani ◽  
M. N. Wakeling ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Iranian Population

scholarly journals Insulin-Like Growth Factor-1 at Diagnosis and during Subsequent Years in Adolescents with Type 1 Diabetes

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Simona I. Chisalita ◽  
J. Ludvigsson
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Secretion ◽  
Insulin Treatment ◽  
Insulin Dose ◽  
Newly Diagnosed ◽  
Blood Samples ◽  
C Peptide ◽  
Endogenous Insulin ◽  
High Hba1c

Background. Type 1 diabetes (T1D) in adolescents is associated with alterations in the insulin-like factor system probably caused both by a deranged metabolism and insulinopenia in the portal vein. Objective. To study how the circulating IGF-1 is affected at diagnosis and during subsequent years in adolescents with T1D. Methods. Ten girls and ten boys with type 1 diabetes (T1D), aged 13.0 ± 1.4 (mean ± SD) years at diagnosis, took part in the study. Blood samples were drawn at diagnosis and after 3, 9, 18, and 48 months. HbA1c, total IGF-1, and C-peptide were measured. Results. At diagnosis, the patients had high HbA1c, low IGF-1, and measurable C-peptide. After the start of insulin treatment, maximal improvement in glycemic control and IGF-1 occurred within 3 months and then both tended to deteriorate, that is, HbA1c to increase and IGF-1 to decrease. C-peptide decreased with time, and after 4 years, half of the patients were C-peptide negative. At diagnosis, C-peptide correlated positively to IGF-1 (r=0.50; p<0.03). C-peptide correlated negatively with insulin dose (U/kg) after 18 and 48 months from diagnosis (r=−0.48; p<0.03 and r=−0.72; p<0.001, resp.). Conclusions. In conclusion, our results show that in newly diagnosed adolescents with type 1 diabetes and deranged metabolism, the IGF-1 level is low and rapidly improves with insulin treatment but later tends to decrease concomitantly with declining endogenous insulin secretion.

scholarly journals Methods for quick, accurate and cost-effective determination of the type 1 diabetes genetic risk score (T1D-GRS)

2020 ◽  
Vol 58 (4) ◽  
pp. e102-e104 ◽  
Author(s):  
Jonathan M. Locke ◽  
Mark J. Latten ◽  
Renu Y. Datta ◽  
Andrew R. Wood ◽  
Martin A. Crockard ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Cost Effective

scholarly journals Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Caroline A. Brorsson ◽  
Lotte B. Nielsen ◽  
Marie Louise Andersen ◽  
Simranjeet Kaur ◽  
Regine Bergholdt ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Disease Progression ◽  
Candidate Genes ◽  
Risk Score ◽  
Genetic Risk ◽  
Cell Function ◽  
Genetic Risk Score ◽  
Newly Diagnosed

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci onβ-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residualβ-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β+ IFNγ+ TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

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