title: A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
abstract: OBJECTIVE
  To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Lin
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America.
authors.forename: Xiaoning
authors.initials: X
authors.lastname: Qian
authors.affiliation: Department of Pediatrics, College of Medicine, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Krischer
authors.affiliation: Department of Pediatrics, College of Medicine, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Kendra
authors.initials: K
authors.lastname: Vehik
authors.affiliation: Department of Pediatrics, College of Medicine, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Hye-Seung
authors.initials: HS
authors.lastname: Lee
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, Florida, United States of America.
authors.forename: Shuai
authors.initials: S
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis* / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D011379:Prognosis; D011446:Prospective Studies; D018570:Risk Assessment
chemical_list: D015415:Biomarkers