Autor: |
Ying Lin, Xiaoning Qian, Jeffrey Krischer, Kendra Vehik, Hye-Seung Lee, Shuai Huang |
Jazyk: |
angličtina |
Rok vydání: |
2014 |
Předmět: |
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Zdroj: |
PLoS ONE, Vol 9, Iss 6, p e91095 (2014) |
Druh dokumentu: |
article |
ISSN: |
1932-6203 |
DOI: |
10.1371/journal.pone.0091095 |
Popis: |
ObjectiveTo identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.Research design and methodsRuleFit 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.ResultsWe 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.ConclusionOur 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. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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