Insulin secretion and sensitivity in the prediction of type 1 diabetes in children with advanced β-cell autoimmunity

Siljander, Heli; Hermann, Róbert; Hekkala, Anne; Lähde, Jyrki; Tanner, Laura M.; Keskinen, Päivi; Ilonen, Jorma; Simell, Olli; Veijola, Riitta; Knip, Mikael

doi:10.1530/eje-13-0206

Cited by 25 publications

(30 citation statements)

References 25 publications

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“…In contrast to previous studies [16,23,15] we did not find any association between BMI SD or HOMA-Index, as a measure of insulin resistance and previously included in risk models [15,23], and progression to diabetes. This may be explained by our young cohort of children, (median age 5.1 years), which may be less affected by risk factors such as insulin resistance and BMI SD.…”

Section: Discussioncontrasting

confidence: 99%

“…Previous studies have analysed predictive values of IvGTT [15,23], OGTT [13] and autoantibody levels [2,11,14,13] separately, while one study used prediction models with a number of different variables [16]. In our study, 20/49 (41%) children were initially found to have impaired glucose metabolism at baseline based on FPIR and 120-minute plasma-glucose [18].…”

Section: Discussionmentioning

confidence: 94%

“…Accurate prediction of time to diabetes is important at inclusion in prevention studies of islet autoantibody positive subjects. Several studies have described a gradually deteriorating glucose metabolism before the diagnosis of diabetes, with increasing HbA1c [12] and 120-minute plasma-glucose in an oral glucose tolerance test (OGTT) [13,14] as well as decreased first phase insulin response (FPIR) in an intravenous glucose tolerance test (IvGTT) [15]. A risk score in first degree relatives based on metabolic markers from OGTT combined with age and BMI has been proposed to identify individuals who develop diabetes within the next two years [16].…”

Section: Introductionmentioning

confidence: 99%

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Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention

Larsson

Lernmark

2014

Acta Diabetol

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Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention. Link to publication Citation for published version (APA): Larsson, H., Larsson, C., & Lernmark, Å. (2015). Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention. Acta Diabetologica, 52(3), 473-481. DOI: 10.1007/s00592-014-0680-1General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal AbstractNon-diabetic children with multiple islet autoantibodies were recruited to a secondary prevention trial.The objective was to determine the predictive value of baseline 1) HbA1c and metabolic variables derived from intravenous (IvGTT) and oral (OGTT) glucose tolerance tests, 2) insulin resistance and 3)number, type and levels of islet autoantibodies, for progression to type 1 diabetes. Children (n=50, median 5.1 (4-17.9) years) with autoantibodies to glutamate-decarboxylase (GAD65A) and at least one

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention

Larsson

Lernmark

2014

Acta Diabetol

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“…Consistent with this, M 120-150 min was significantly correlated with HOMA2-IR and AUC C-peptide. Our results are in line with observations that the ratio of HOMA-IR to firstphase insulin release during the IVGTT test better predicted type 1 diabetes than first-phase insulin release alone, particularly in individuals with already-compromised insulin release [37][38][39][40], and with the strong inverse correlation between oral disposition index and GV variables in individuals at different stages of glucose tolerance, ranging from normoglycaemia to overt type 2 diabetes [41]. Indeed, while in our study euglycaemic FDRs and healthy controls tended to differ only in AUC 5-10 min , M 120-150 min was the best discriminator between FDRs with or without (impending) dysglycaemia.…”

Section: Correlation Of Gv With Beta Cell Function and Glucose Dispossupporting

confidence: 91%

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

et al. 2015

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Aims/hypothesis We examined whether measures of glycaemic variability (GV), assessed by continuous glucose monitoring (CGM) and self-monitoring of blood glucose (SMBG), can complement or replace measures of beta cell function and insulin action in detecting the progression of preclinical disease to type 1 diabetes. Methods Twenty-two autoantibody-positive (autoAb + ) firstdegree relatives (FDRs) of patients with type 1 diabetes who were themselves at high 5-year risk (50%) for type 1 diabetes underwent CGM, a hyperglycaemic clamp test and OGTT, and were followed for up to 31 months. Clamp variables were used to estimate beta cell function (first-phase [AUC 5-10 min ] and second-phase [AUC 120-150 min ] C-peptide release) combined with insulin resistance (glucose disposal rate; M 120-150 min ). Age-matched healthy volunteers (n =20) and individuals with recent-onset type 1 diabetes (n=9) served as control groups. Results In autoAb + FDRs, M 120-150 min below the 10th percentile (P10) of controls achieved 86% diagnostic efficiency in discriminating between normoglycaemic FDRs and individuals with (impending) dysglycaemia. M 120-150 min outperformed AUC 5-10 min and AUC 120-150 min C-peptide below P10 of controls, which were only 59-68% effective. Among GV variables, CGM above the reference range was better at detecting (impending) dysglycaemia than elevated SMBG (77-82% vs 73% efficiency). Combined CGM measures were equally efficient as M 120-150 min (86%). Daytime GV variables were inversely correlated with clamp variables, and more strongly with M 120-150 min than with AUC 5-10 min or AUC 120-150 min C-peptide. Conclusions/interpretation CGM-derived GV and the glucose disposal rate, reflecting both insulin secretion and action, outperformed SMBG and first-or second-phase AUC C-peptide in identifying FDRs with (impending) dysglycaemia or diabetes. Our results indicate the feasibility of developing minimally invasive CGM-based criteria for close metabolic monitoring and as outcome measures in trials.

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“…Children with multiple islet autoantibodies and risk-conferring HLA class II genotypes have a cumulative disease risk of 50-60% over the next 5 years and a more than 80% risk when followed for 15 years after seroconversion to autoantibody positivity [3,4]. Although abnormalities in glucose metabolism and insulin secretion are present years before diagnosis, prediction of the timing of clinical disease has remained challenging [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

OGTT and random plasma glucose in the prediction of type 1 diabetes and time to diagnosis

et al. 2015

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Aims/hypothesis We assessed the utility of the OGTT and random plasma glucose concentrations in predicting the time to diagnosis of type 1 diabetes. Methods A population-derived cohort of 14,876 newborns with HLA-conferred risk of type 1 diabetes were invited to regular follow-up for islet autoantibodies. When two or more autoantibodies were detected, an OGTT was performed once a year and random plasma glucose analysed twice a year. During follow-up, 567 children developed multiple autoantibodies, 255 (45%) of whom were diagnosed with type 1 diabetes, while 312 remained non-diabetic by December 2011.Results Impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) were risk factors for type 1 diabetes (HR 3.2 [95% CI 1.5, 7.0] and 8.3 [95% CI 6.0, 11.5], respectively). When a random plasma glucose value ≥7.8 mmol/l was observed, the HR for diabetes was 6.0 (95% CI 4.3, 8.6). The median time to diagnosis after the detection of IFG was 5.2 years (interquartile range [IQR] 3.4, 6.3); after IGT, 0.7 years (IQR 0.3, 1.9); and, after a random plasma glucose ≥7.8 mmol/l, 1.0 years (IQR 0.3, 1.5). In a retrospective analysis, both OGTT-derived 2 h plasma glucose and random plasma glucose started to increase 1.5 years before diagnosis (p<0.001 and p=0.004, respectively). Conclusions/interpretation Dysglycaemia detected in an OGTT or based on random plasma glucose is a useful marker in the prediction of time to onset of type 1 diabetes in high-risk children. Random plasma glucose is a simple and low-cost measurement with comparable predictive characteristics to that of OGTT-derived 2 h glucose.

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Insulin secretion and sensitivity in the prediction of type 1 diabetes in children with advanced β-cell autoimmunity

Cited by 25 publications

References 25 publications

Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention

Baseline heterogeneity in glucose metabolism marks the risk for type 1 diabetes and complicates secondary prevention

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

OGTT and random plasma glucose in the prediction of type 1 diabetes and time to diagnosis

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