20 research outputs found
Additional file 1: Table S1. of Perceived inadequate care and excessive overprotection during childhood are associated with greater risk of sleep disturbance in adulthood: the Hisayama Study
Characteristics for all participants according to sleep disturbance. (DOCX 18 kb
Additional file 2: Table S2. of Perceived inadequate care and excessive overprotection during childhood are associated with greater risk of sleep disturbance in adulthood: the Hisayama Study
Odds ratios for sleep disturbance according to parental bonding style. (DOCX 30 kb
Additional file 3: Table S3. of Perceived inadequate care and excessive overprotection during childhood are associated with greater risk of sleep disturbance in adulthood: the Hisayama Study
Characteristics of participants and non-participants. (DOCX 15 kb
The ROC curve for our risk prediction model.
<p>Sensitivity and specificity was maximized at a sensitivity of 0.858 and specificity of 0.623.</p
The Construction of Risk Prediction Models Using GWAS Data and Its Application to a Type 2 Diabetes Prospective Cohort
<div><p>Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.</p></div
Risk prediction models using 10-fold cross-validation on the training set.
<p>Risk prediction models using 10-fold cross-validation on the training set.</p
Cumulative disease-free survival in a prospective cohort.
<p>Models using (A) only clinical risk factors and (B) both of clinical and genetic risk factors.</p
Results of whole genome association scan for a training set.
<p>Results of whole genome association scan for a training set.</p
Outline of the risk prediction model construction and validation.
<p>Outline of the risk prediction model construction and validation.</p