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- Carlsson, Annelie, et al.
(författare)
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Absence of Islet Autoantibodies and Modestly Raised Glucose Values at Diabetes Diagnosis Should Lead to Testing for MODY : Lessons From a 5-Year Pediatric Swedish National Cohort Study
- 2020
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Ingår i: Diabetes Care. - Arlington, VA, United States : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 43:1, s. 82-89
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE Identifying maturity-onset diabetes of the young (MODY) in pediatric populations close to diabetes diagnosis is difficult. Misdiagnosis and unnecessary insulin treatment are common. We aimed to identify the discriminatory clinical features at diabetes diagnosis of patients with glucokinase (GCK), hepatocyte nuclear factor-1A (HNF1A), and HNF4A MODY in the pediatric population.RESEARCH DESIGN AND METHODS Swedish patients (n = 3,933) aged 1–18 years, diagnosed with diabetes May 2005 to December 2010, were recruited from the national consecutive prospective cohort Better Diabetes Diagnosis. Clinical data, islet autoantibodies (GAD insulinoma antigen-2, zinc transporter 8, and insulin autoantibodies), HLA type, and C-peptide were collected at diagnosis. MODY was identified by sequencing GCK, HNF1A, and HNF4A, through either routine clinical or research testing.RESULTS The minimal prevalence of MODY was 1.2%. Discriminatory factors for MODY at diagnosis included four islet autoantibody negativity (100% vs. 11% not-known MODY; P = 2 × 10−44), HbA1c (7.0% vs. 10.7% [53 vs. 93 mmol/mol]; P = 1 × 10−20), plasma glucose (11.7 vs. 26.7 mmol/L; P = 3 × 10−19), parental diabetes (63% vs. 12%; P = 1 × 10−15), and diabetic ketoacidosis (0% vs. 15%; P = 0.001). Testing 303 autoantibody-negative patients identified 46 patients with MODY (detection rate 15%). Limiting testing to the 73 islet autoantibody-negative patients with HbA1c <7.5% (58 mmol/mol) at diagnosis identified 36 out of 46 (78%) patients with MODY (detection rate 49%). On follow-up, the 46 patients with MODY had excellent glycemic control, with an HbA1c of 6.4% (47 mmol/mol), with 42 out of 46 (91%) patients not on insulin treatment.CONCLUSIONS At diagnosis of pediatric diabetes, absence of all islet autoantibodies and modest hyperglycemia (HbA1c <7.5% [58 mmol/mol]) should result in testing for GCK, HNF1A, and HNF4A MODY. Testing all 12% patients negative for four islet autoantibodies is an effective strategy for not missing MODY but will result in a lower detection rate. Identifying MODY results in excellent long-term glycemic control without insulin.
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| 3. |
- Ping Zhao, Lue, et al.
(författare)
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Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes
- 2017
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Ingår i: Diabetes/Metabolism Research Reviews. - : WILEY. - 1520-7552 .- 1520-7560. ; 33:8
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Tidskriftsartikel (refereegranskat)abstract
- AimIt is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. MethodsUtilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. ResultsIn the training set, estimated risk scores were significantly different between patients and controls (P=8.12x10(-92)), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a biological validation by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score=3.628, Pamp;lt;0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. ConclusionThrough both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations.
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