Extended Data Fig. 10: Diabetes Mellitus incidence rate and predictive models.
From: Personalized lab test models to quantify disease potentials in healthy individuals
a) Incidence rate of DM in Clalit population. Shown is the number of new T2D cases per 100 K per year (y-axis) by age (x-axis) for male (light grey) and female (dark grey). Error bars indicate 95% confidence interval. b) Kaplan Meier estimates for six-year DM cumulative incidence stratified by fasting glucose lab test. Healthy patients age 50-60 at index date 1.1.2011 were classified according to most recent fasting glucose lab test value in the past year: FG [90, 95) (n = 46970), FG [95,100) (n = 28927), FG [100,105) (n = 13921), FG [105, 110) (5989), FG[110,115) (n = 2911) and FG [115,120) (n = 1504) Error bars indicate 95% confidence intervals. c) Cumulative incidence of new T2DM diagnosis computed by Kaplan-Meier method, stratified by models trained on raw lab data. 95% confidence intervals are shown in lighter colors. Similar to Fig. 4e, but trained on raw lab values and not quantile normalized values. Note that raw imputation data was computed via quantile normalized values. Model prediction in this case is robust to raw values and provides similar performance as quantile normalized values.
