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.</p

Trends in High-Risk HLA Susceptibility Genes Among Colorado Youth With Type 1 Diabetes

OBJECTIVE—Type 1 diabetes is associated with a wide spectrum of susceptibility and protective genotypes within the HLA class II system. It has been reported that adults diagnosed with youth-onset type 1 diabetes more recently have been found to have fewer classical high-risk HLA class II genotypes than those diagnosed several decades ago. We hypothesized that such temporal trends in the distribution of HLA-DR, DQ genotypes would be evident, and perhaps even stronger, among 5- to 17-year-old Hispanic and non-Hispanic white (NHW) youth diagnosed with type 1 diabetes in Colorado between 1978 and 2004

A bone grease processing station at the Mitchell Prehistoric Indian Village: archaeological evidence for the exploitation of bone fats

© Association for Environmental Archaeology 2015. Author's accepted manuscript version deposited in accordance with SHERPA RoMEO guidelines. The definitive version is available at http://www.maneyonline.com/doi/abs/10.1179/1749631414Y.0000000035.Recent excavations at the Mitchell Prehistoric Indian Village, an Initial Middle Missouri site in Mitchell, South Dakota have revealed a large, clay-lined feature filled with fractured and fragmented bison bones. Fracture and fragmentation analysis, along with taphonomic evidence, suggests that the bones preserved within the feature represent evidence of prehistoric bone marrow and bone grease exploitation. Further, the character of the feature suggests that it served as a bone grease processing station. Bone fat exploitation is an activity that is frequently cited as a causal explanation for the nature of many fractured and fragmented bone assemblages in prehistory, and zooarchaeological assemblages have frequently been studied as evidence of bone fat exploitation. The Mitchell example provides some of the first direct, in-situ archaeological evidence of a bone grease processing feature, and this interpretation is sustained by substantial analytical evidence suggesting bone fat exploitation. This new evidence provides a clearer concept of the nature of bone fat exploitation in prehistory as well as an indication of the scale and degree to which bone grease exploitation occurred at the Mitchell site. Finally, this research demonstrates the importance of careful zooarchaeological and taphonomic analysis for the interpretation of both artifactual remains as well as archaeological features

Association of Intrauterine Exposure to Maternal Diabetes and Obesity With Type 2 Diabetes in Youth: The SEARCH Case-Control Study

OBJECTIVE—Limited data exist on the association between in utero exposure to maternal diabetes and obesity and type 2 diabetes in diverse youth. These associations were explored in African-American, Hispanic, and non-Hispanic white youth participating in the SEARCH Case-Control Study

A combined risk score enhances prediction of type 1 diabetes among susceptible children

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordType 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis10-12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.National Institutes of Health/National Center for Advancing Translational Sciences Clinical and Translational ScienceDiabetes Research CenterDiabetes UKWellcome TrustJDR