Feature ranking based on synergy networks to identify prognostic markers in DPT-1

Interaction among different risk factors plays an important role in the development and progress of complex disease, such as diabetes. However, traditional epidemiological methods often focus on analyzing individual or a few ‘essential’ risk factors, hopefully to obtain some insights into the etiology of complex disease. In this paper, we propose a systematic framework for risk factor analysis based on a synergy network, which enables better identification of potential risk factors that may serve as prognostic markers for complex disease. A spectral approximate algorithm is derived to solve this network optimization problem, which leads to a new network-based feature ranking method that improves the traditional feature ranking by taking into account the pairwise synergistic interactions among risk factors in addition to their individual predictive power. We first evaluate the performance of our method based on simulated datasets, and then, we use our method to study immunologic and metabolic indices based on the Diabetes Prevention Trial-Type 1 (DPT-1) study that may provide prognostic and diagnostic information regarding the development of type 1 diabetes. The performance comparison based on both simulated and DPT-1 datasets demonstrates that our network-based ranking method provides prognostic markers with higher predictive power than traditional analysis based on individual factors

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study

M. Knip on TEDDY Study Grp -työryhmän jäsen.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.Peer reviewe

Temporal changes in gastrointestinal fungi and the risk of autoimmunity during early childhood: the TEDDY study

Fungal infections are a major health problem that often begin in the gastrointestinal tract. Gut microbe interactions in early childhood are critical for proper immune responses, yet there is little known about the development of the fungal population from infancy into childhood. Here, as part of the TEDDY (The Environmental Determinants of Diabetes in the Young) study, we examine stool samples of 888 children from 3 to 48 months and find considerable differences between fungi and bacteria. The metagenomic relative abundance of fungi was extremely low but increased while weaning from milk and formula. Overall fungal diversity remained constant over time, in contrast with the increase in bacterial diversity. Fungal profiles had high temporal variation, but there was less variation from month-to-month in an individual than among different children of the same age. Fungal composition varied with geography, diet, and the use of probiotics. Multiple Candida spp. were at higher relative abundance in children than adults, while Malassezia and certain food-associated fungi were lower in children. There were only subtle fungal differences associated with the subset of children that developed islet autoimmunity or type 1 diabetes. Having proper fungal exposures may be crucial for children to establish appropriate responses to fungi and limit the risk of infection: the data here suggests those gastrointestinal exposures are limited and variable.</p

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BackgroundAround 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 findingsThe 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 auto-antibodies 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 14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of 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.ConclusionsA 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

Differences in recruitment and early retention among ethnic minority participants in a large pediatric cohort: The TEDDY Study

Objective: The TEDDY Study is an international, multi-center prospective study designed to identify the environmental triggers of type 1 diabetes (T1D) in genetically at-risk children. This report investigates ethnic minority (EM) differences in patterns of enrollment and retention in the US centers. Methods: As of June 2009, 267,739 newborns had been screened at birth for high risk T1D genotypes. Data collected at the time of screening, enrollment and at the baseline visit were used. Descriptive and multiple-logistic regression analyses assessed differences between EM groups regarding exclusion, enrollment and early withdrawal. Results: Of the 10,975 eligible subjects, 6,912 (67%) were invited to participate. EM subjects were more likely to be excluded because of an inability to contact. Of those invited 3,265 (47%) enrolled by the age of 4.5 months. Adjusted analyses showed that except for those classified as other EM, the odds of enrolling were similar across groups. EM subjects had elevated early withdrawal rates. Adjusted models demonstrated that this was significantly more likely among Hispanic subjects. Conclusion: Understanding patterns associated with EM participation in research extends our ability to make more accurate inferences and permits assessment of strategies that promote inclusion of EM to better address health disparities. (C) 2012 Elsevier Inc. All rights reserved

A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns

Objective: To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction. Research Design and Methods: RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset. Results: We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D. Conclusion: Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study

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

Age at first introduction to complementary foods is associated with sociodemographic factors in children with increased genetic risk of developing type 1 diabetes.

Infant's age at introduction to certain complementary foods (CF) has in previous studies been associated with islet autoimmunity, which is an early marker for type 1 diabetes (T1D). Various maternal sociodemographic factors have been found to be associated with early introduction to CF. The aims of this study were to describe early infant feeding and identify sociodemographic factors associated with early introduction to CF in a multinational cohort of infants with an increased genetic risk for T1D. The Environmental Determinants of Diabetes in the Young study is a prospective longitudinal birth cohort study. Infants (N = 6404) screened for T1D high risk human leucocyte antigen-DQ genotypes (DR3/4, DR4/4, DR4/8, DR3/3, DR4/4, DR4/1, DR4/13, DR4/9 and DR3/9) were followed for 2 years at six clinical research centres: three in the United States (Colorado, Georgia/Florida, Washington) and three in Europe (Sweden, Finland, Germany). Age at first introduction to any food was reported at clinical visits every third month from the age of 3 months. Maternal sociodemographic data were self-reported through questionnaires. Age at first introduction to CF was primarily associated with country of residence. Root vegetables and fruits were usually the first CF introduced in Finland and Sweden and cereals were usually the first CF introduced in the United States. Between 15% and 20% of the infants were introduced to solid foods before the age of 4 months. Young maternal age (<25 years), low educational level (<12 years) and smoking during pregnancy were significant predictors of early introduction to CF in this cohort. Infants with a relative with T1D were more likely to be introduced to CF later

Enrollment experiences in a pediatric longitudinal observational study: The Environmental Determinants of Diabetes in the Young (TEDDY) study.

OBJECTIVE: Our objective was to identify characteristics of infants and their families who were enrolled, refused to enroll, or were excluded from The Environmental Determinants of Diabetes in the Young (TEDDY) study. METHOD: 16,435 infants screened at birth and identified as at increased genetic risk for type 1 diabetes (T1DM) were placed into one of three categories: enrolled, excluded, or refused to enroll. Enrollment, exclusion and refusal rates were compared across countries and between infants from the general population (GP) and infants with a first degree T1DM relative (FDR). A multivariate logistic model was used to identify factors associated with TEDDY enrollment. RESULTS: TEDDY enrollment, exclusion, and refusal rates differed by country and by GP/FDR status but reasons for refusal to enroll were similar across countries and GP/FDR populations. Sweden had the highest enrollment rate, US had the highest exclusion rate, and Finland had the highest refusal rate. FDR infants were more likely to enroll than GP infants. Inability to re-contact the family was the most common reason for exclusion. Primary reasons for refusal to enroll included protocol factors (e.g. blood draws) or family factors (e.g., too busy). Study enrollment was associated with FDR status, European country of origin, older maternal age, a singleton birth, and having another child in TEDDY. CONCLUSIONS: Findings highlight the importance of country specific estimates for enrollment targets in longitudinal pediatric studies and suggest that enrollment estimates should be lowered when the study involves the general population, painful procedures, or makes multiple demands on families