| 1. |
- Anand, Vibha, et al.
(författare)
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Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes : Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S
- 2021
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 44, s. 2269-2276
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To combine prospective cohort studies, by including HLA harmonization, and estimate risk of islet autoimmunity and progression to clinical diabetes.RESEARCH DESIGN AND METHODS: For prospective cohorts in Finland, Germany, Sweden, and the U.S., 24,662 children at increased genetic risk for development of islet autoantibodies and type 1 diabetes have been followed. Following harmonization, the outcomes were analyzed in 16,709 infants-toddlers enrolled by age 2.5 years.RESULTS: In the infant-toddler cohort, 1,413 (8.5%) developed at least one autoantibody confirmed at two or more consecutive visits (seroconversion), 865 (5%) developed multiple autoantibodies, and 655 (4%) progressed to diabetes. The 15-year cumulative incidence of diabetes varied in children with one, two, or three autoantibodies at seroconversion: 45% (95% CI 40-52), 85% (78-90), and 92% (85-97), respectively. Among those with a single autoantibody, status 2 years after seroconversion predicted diabetes risk: 12% (10-25) if reverting to autoantibody negative, 30% (20-40) if retaining a single autoantibody, and 82% (80-95) if developing multiple autoantibodies. HLA-DR-DQ affected the risk of confirmed seroconversion and progression to diabetes in children with stable single-autoantibody status. Their 15-year diabetes incidence for higher- versus lower-risk genotypes was 40% (28-50) vs. 12% (5-38). The rate of progression to diabetes was inversely related to age at development of multiple autoantibodies, ranging from 20% per year to 6% per year in children developing multipositivity in ≤2 years or >7.4 years, respectively.CONCLUSIONS: The number of islet autoantibodies at seroconversion reliably predicts 15-year type 1 diabetes risk. In children retaining a single autoantibody, HLA-DR-DQ genotypes can further refine risk of progression.
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| 2. |
- Ghalwash, Mohamed, et al.
(författare)
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Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood : a prospective cohort study
- 2023
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Ingår i: The Lancet Child and Adolescent Health. - 2352-4642. ; 7:4, s. 261-268
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Tidskriftsartikel (refereegranskat)abstract
- Background: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10−18 years with an increased risk of developing type 1 diabetes. Methods: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. Findings: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5–20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34–0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50–0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1–1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6–9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86–95) sensitive, with a positive predictive value of 66% (60–72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89–97) but lowered the positive predictive value to 55% (49–60). Interpretation: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. Funding: JDRF International.
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| 3. |
- Ghalwash, Mohamed, et al.
(författare)
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Two-age islet-autoantibody screening for childhood type 1 diabetes : a prospective cohort study
- 2022
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Ingår i: The Lancet Diabetes and Endocrinology. - 2213-8587. ; 10:8, s. 589-596
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Tidskriftsartikel (refereegranskat)abstract
- Background: Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis and provides opportunities for disease prevention. However, only highly efficient approaches are likely to succeed in public health settings. We sought to identify efficient strategies for initial islet autoantibody screening in children younger than 15 years. Methods: We harmonised data from five prospective cohorts from Finland (DIPP), Germany (BABYDIAB), Sweden (DiPiS), and the USA (DAISY and DEW-IT) into the Type 1 Diabetes Intelligence (T1DI) cohort. 24 662 children at high risk of diabetes enrolled before age 2 years were included and followed up for islet autoantibodies and diabetes until age 15 years, or type 1 diabetes onset, whichever occurred first. Islet autoantibodies measured included those against glutamic acid decarboxylase, insulinoma antigen 2, and insulin. Main outcomes were sensitivity and positive predictive value (PPV) of detected islet autoantibodies, tested at one or two fixed ages, for diagnosis of clinical type 1 diabetes. Findings: Of the 24 662 participants enrolled in the Type 1 Diabetes Intelligence cohort, 6722 total were followed up to age 15 years or until onset of type 1 diabetes. Type 1 diabetes developed by age 15 years in 672 children, but did not develop in 6050 children. Optimal screening ages for two measurements were 2 years and 6 years, yielding sensitivity of 82% (95% CI 79–86) and PPV of 79% (95% CI 75–80) for diabetes by age 15 years. Autoantibody positivity at the beginning of each test age was highly predictive of diagnosis in the subsequent 2–5·99 year or 6–15-year age intervals. Autoantibodies usually appeared before age 6 years even in children diagnosed with diabetes much later in childhood. Interpretation: Our results show that initial screening for islet autoantibodies at two ages (2 years and 6 years) is sensitive and efficient for public health translation but might require adjustment by country on the basis of population-specific disease characteristics. Funding: Juvenile Diabetes Research Foundation.
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| 4. |
- 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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| 5. |
- Jacobsen, Laura M., et al.
(författare)
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Heterogeneity of DKA Incidence and Age-Specific Clinical Characteristics in Children Diagnosed With Type 1 Diabetes in the TEDDY Study
- 2022
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992. ; 45:3, s. 624-633
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. Because age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA). RESEARCH DESIGN AND METHODS The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0–4, 5–9, and 10–14 years; n = 142, 151, and 86, respectively) with com-parisons of autoantibody profiles, HLAs, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis compared those with oral glucose tolerance test data with TEDDY children who did not progress to diabetes. RESULTS Increasing fasting glucose (hazard ratio [HR] 1.09 [95% CI 1.04–1.14]; P = 0.0003), stimulated glucose (HR 1.50 [1.42–1.59]; P < 0.0001), fasting insulin (HR 0.89 [0.83–0.95]; P = 0.0009), and glucose-to-insulin ratio (HR 1.29 [1.16–1.43]; P < 0.0001) were associated with risk of progression to type 1 diabetes. Younger children had fewer autoantibodies with more symptoms at diagnosis. Twenty-three children (6.1%) had DKA at onset, only 1 (0.97%) of 103 with and 22 (8.0%) of 276 children without a first-degree relative (FDR) with type 1 diabetes (P = 0.008). Children with DKA were more likely to be nonadherent to study protocol (P = 0.047), with longer duration between their last TEDDY evaluation and diagnosis (median 10.2 vs. 2.0 months without DKA; P < 0.001). CONCLUSIONS DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset, adding to the heterogeneity of type 1 diabetes.
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| 6. |
- Jacobsen, Laura M., et al.
(författare)
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Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children
- 2019
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Ingår i: Pediatric Diabetes. - : Hindawi Limited. - 1399-543X .- 1399-5448. ; 20:3, s. 263-270
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The capacity to precisely predict progression to type 1 diabetes (T1D) in young children over a short time span is an unmet need. We sought to develop a risk algorithm to predict progression in children with high-risk human leukocyte antigen (HLA) genes followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Methods: Logistic regression and 4-fold cross-validation examined 38 candidate predictors of risk from clinical, immunologic, metabolic, and genetic data. TEDDY subjects with at least one persistent, confirmed autoantibody at age 3 were analyzed with progression to T1D by age 6 serving as the primary endpoint. The logistic regression prediction model was compared to two non-statistical predictors, multiple autoantibody status, and presence of insulinoma-associated-2 autoantibodies (IA-2A). Results: A total of 363 subjects had at least one autoantibody at age 3. Twenty-one percent of subjects developed T1D by age 6. Logistic regression modeling identified 5 significant predictors - IA-2A status, hemoglobin A1c, body mass index Z-score, single-nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level. The logistic model yielded a receiver operating characteristic area under the curve (AUC) of 0.80, higher than the two other predictors; however, the differences in AUC, sensitivity, and specificity were small across models. Conclusions: This study highlights the application of precision medicine techniques to predict progression to diabetes over a 3-year window in TEDDY subjects. This multifaceted model provides preliminary improvement in prediction over simpler prediction tools. Additional tools are needed to maximize the predictive value of these approaches.
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| 7. |
- Li, Ying, et al.
(författare)
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Predicting Type 1 Diabetes Onset using Novel Survival Analysis with Biomarker Ontology
- 2020
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Ingår i: AMIA ... Annual Symposium proceedings. AMIA Symposium. - 1942-597X. ; 2020, s. 727-736
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Tidskriftsartikel (refereegranskat)abstract
- Type 1 diabetes (T1D) is a chronic autoimmune disease that affects about 1 in 300 children and up to 1 in 100 adults during their life-time1. Improvements in early prediction of T1D onset may help prevent diagnosis for diabetic ketoacidosis, a serious complication often associated with a missed or delayed T1D diagnosis. In addition to genetic factors, progression to T1D is strongly associated with immunologic factors that can be measured during clinical visits. We developed a T1D-specific ontology that captures the dynamic patterns of these biomarkers and used it together with a survival model, RankSvx, proposed in our prior work2. We applied this approach to a T1D dataset harmonized from three birth cohort studies from the United States, Finland, and Sweden. Results show that the dynamic biomarker patterns captured in the proposed ontology are able to improve prediction performance (in concordance index) by 5.3%, 3.3%, 2.8%, and 1.0% over baseline for 3, 6, 9, and 12 month duration windows, respectively.
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| 8. |
- Lundgren, Markus, et al.
(författare)
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Analgesic antipyretic use among young children in the TEDDY study : No association with islet autoimmunity
- 2017
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Ingår i: BMC Pediatrics. - : Springer Science and Business Media LLC. - 1471-2431. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The use of analgesic antipyretics (ANAP) in children have long been a matter of controversy. Data on their practical use on an individual level has, however, been scarce. There are indications of possible effects on glucose homeostasis and immune function related to the use of ANAP. The aim of this study was to analyze patterns of analgesic antipyretic use across the clinical centers of The Environmental Determinants of Diabetes in the Young (TEDDY) prospective cohort study and test if ANAP use was a risk factor for islet autoimmunity. Methods: Data were collected for 8542 children in the first 2.5 years of life. Incidence was analyzed using logistic regression with country and first child status as independent variables. Holm's procedure was used to adjust for multiplicity of intercountry comparisons. Time to autoantibody seroconversion was analyzed using a Cox proportional hazards model with cumulative analgesic use as primary time dependent covariate of interest. For each categorization, a generalized estimating equation (GEE) approach was used. Results: Higher prevalence of ANAP use was found in the U.S. (95.7%) and Sweden (94.8%) compared to Finland (78.1%) and Germany (80.2%). First-born children were more commonly given acetaminophen (OR 1.26; 95% CI 1.07, 1.49; p = 0.007) but less commonly Non-Steroidal Anti-inflammatory Drugs (NSAID) (OR 0.86; 95% CI 0.78, 0.95; p = 0.002). Acetaminophen and NSAID use in the absence of fever and infection was more prevalent in the U.S. (40.4%; 26.3% of doses) compared to Sweden, Finland and Germany (p < 0.001). Acetaminophen or NSAID use before age 2.5 years did not predict development of islet autoimmunity by age 6 years (HR 1.02, 95% CI 0.99-1.09; p = 0.27). In a sub-analysis, acetaminophen use in children with fever weakly predicted development of islet autoimmunity by age 3 years (HR 1.05; 95% CI 1.01-1.09; p = 0.024). Conclusions: ANAP use in young children is not a risk factor for seroconversion by age 6 years. Use of ANAP is widespread in young children, and significantly higher in the U.S. compared to other study sites, where use is common also in absence of fever and infection.
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| 9. |
- Salami, Falastin, et al.
(författare)
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HbA1c as a time predictive biomarker for an additional islet autoantibody and type 1 diabetes in seroconverted TEDDY children
- 2022
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Ingår i: Pediatric Diabetes. - : Hindawi Limited. - 1399-543X .- 1399-5448. ; 23:8, s. 1586-1593
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND/OBJECTIVES: Increased level of glycated hemoglobin (HbA1c) is associated with type 1 diabetes onset that in turn is preceded by one to several autoantibodies against the pancreatic islet beta cell autoantigens; insulin (IA), glutamic acid decarboxylase (GAD), islet antigen-2 (IA-2) and zinc transporter 8 (ZnT8). The risk for type 1 diabetes diagnosis increases by autoantibody number. Biomarkers predicting the development of a second or a subsequent autoantibody and type 1 diabetes are needed to predict disease stages and improve secondary prevention trials. This study aimed to investigate whether HbA1c possibly predicts the progression from first to a subsequent autoantibody or type 1 diabetes in healthy children participating in the Environmental Determinants of Diabetes in the Young (TEDDY) study.METHODS: A joint model was designed to assess the association of longitudinal HbA1c levels with the development of first (insulin or GAD autoantibodies) to a second, second to third, third to fourth autoantibody or type 1 diabetes in healthy children prospectively followed from birth until 15 years of age.RESULTS: It was found that increased levels of HbA1c were associated with a higher risk of type 1 diabetes (HR 1.82, 95% CI [1.57-2.10], p<0.001) regardless of first appearing autoantibody, autoantibody number or type. A decrease in HbA1c levels was associated with the development of IA-2A as a second autoantibody following GADA (HR 0.85, 95% CI [0.75,0.97], p=0.017) and a fourth autoantibody following GADA, IAA and ZnT8A (HR 0.90, 95% CI [0.82,0.99], p=0.036). HbA1c trajectory analyses showed a significant increase of HbA1c over time (p<0.001) and that the increase is more rapid as the number of autoantibodies increased from one to three (p<0.001).CONCLUSION: In conclusion, increased HbA1c is a reliable time predictive marker for type 1 diabetes onset. The increased rate of increase of HbA1c from first to third autoantibody and the decrease in HbA1c predicting the development of IA-2A are novel findings proving the link between HbA1c and the appearance of autoantibodies. This article is protected by copyright. All rights reserved.
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| 10. |
- Smith, Laura B., et al.
(författare)
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Family adjustment to diabetes diagnosis in children : Can participation in a study on type 1 diabetes genetic risk be helpful?
- 2018
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Ingår i: Pediatric Diabetes. - : Hindawi Limited. - 1399-543X. ; 19:5, s. 1025-1033
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Tidskriftsartikel (refereegranskat)abstract
- Background: Diagnosis of type 1 diabetes often causes a negative psychological impact on families. We examined whether parents and children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study differ in their psychological adjustment to diabetes diagnosis compared to children diagnosed with diabetes in the community. Methods: TEDDY follows 8676 children at genetic risk for type 1 diabetes from birth. Fifty-four TEDDY children diagnosed with diabetes and 54 age-matched community control children diagnosed with diabetes were enrolled. Participants were aged 3 to 10years and study visits occurred at 3, 6, and 12 months postdiagnosis. Psychological measures included an adapted diabetes-specific State Anxiety Inventory, the Pediatric Quality of Life Inventory-Diabetes Module, and the Pediatric Inventory for Parents, which measures frequency and difficulty of parenting stress. Results: A generalized estimating equation analysis based on a difference score between TEDDY children and community controls found no significant differences between TEDDY parents and community controls on parent diabetes-specific anxiety (P=.30). However, TEDDY children exhibited better diabetes-specific quality of life (P=.03) and TEDDY parents reported lower frequency (P=.004) and difficulty (P=.008) of parenting stress compared to community controls. Conclusions: Children diagnosed with at-risk for type 1 diabetes who have previously enrolled in research monitoring have improved diabetes quality of life and lower parenting stress postdiagnosis compared to children diagnosed in the community. Families in follow-up studies may be more prepared if their child is diagnosed with diabetes.
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