If the relationship between age and risk doesn't change with age, then the differences in distribution shouldn't matter, so I assume that it does change.
To illustrate, suppose you are fitting a linear relationship between age and height, using a sample of people uniformly distributed in 0-20 years of age. If you then apply this model to a new sample of only 15-20 years of age, you'll run into problems, because the relationship changes strongly with age - and your linear model doesn't account for that.
Conversely, if people did grow linearly 3 inches every year over their entire life, then it wouldn't make a difference whether the distribution differed between the training and test sample.
Now, you could either subsample the training set to match the age distribution you see in your test set, so the model learns the relationship that is actually present in the test set. Alternatively, you could attempt to learn the changing relationship, by including more flexibility in your model. Consider transforming age using splines. Or potentially consider interaction terms.
EDIT: here is an example. I couldn't find any data quickly, so I'll just simulate something. Below are 1000 pairs of "age" (say in years) and "height" (say in meters), plus a regression line in black - plus the true curved relationship in red.
Note that the regression line is determined on the entire training sample. If you were to apply this model to a test population aged only 60-80, then you'd overestimate their height systematically, since the black line is above the red one. The same for low ages. Conversely, in the age range 15-55, the regression actually overestimates height.
And note that this wouldn't happen so badly if the true relationship were in fact linear across the entire age range - then the red line would be straight, and close to the black one. Of course, the slopes could still differ, and the discrepancy resulting from slope differences would be largest for extreme values of age.
Two possible solutions, as I wrote above, would be to capture the true curved relationship via splines, or by matching the training and test sample on the relevant predictor.
nn <- 1e3
age.train <- sample(1:80,size=nn,replace=TRUE)
height.train <- age.train^(1/5)*0.6+0.6+rnorm(nn,0,0.2)
xx <- 1:80