A causal path problem of prediction models

I am doing a study on predicting the 10-year risk of diabetes in people without diabetes using factors such as glucose, HbA1c, and triglycerides. The blood sampling time is not limited to fasting. In other words, people can choose to draw blood at any time according to their own judgment. Another main purposes is to clarify what extent adding triglycerides to the model will improve the performance of the model in the environment of arbitrary blood sampling.

There is a question here, which is the impact of blood sampling time on glucose and triglycerides. Within 8 hours after a meal, glucose and triglycerides will rise and begin to decline about 6 hours after a meal. In the fasting state after 8 hours, glucose and triglycerides tend to be stable. I built the following model using rcs and interactions.

 Surv(py, diabetes) ~ AGE + SEXC +
    rcs(HBA1C) +
    rcs(GLU) +
    rcs(TG) * rcs(Postprandial TIME) +
    rcs(GLU) :rcs(Postprandial TIME) + 
    rcs(GLU) : rcs(TG) +
    other factors......

Postprandial time affects glucose and triglycerides, so its interaction are necessary. A common practice is to divide people into fasting and non-fasting for stratified analysis. I know that stratified analysis has many problems. In particular, I feel that fasting/non-fasting status is a collider. Although there is no requirement, people with high glucose or prediabetes are more likely to draw blood on a fasting state (this may reflect the perception that fasting blood sampling is better for diabetes screening), and the 10-year diabetes incidence is higher than non-fasting status.

My assumption is that
B→diabetes incidence.

A is the time after a meal, and a long time after a meal leads to a fasting state
C is the fasting state
B is high glucose levels; people with a concern caused by high glucose levels may choose fasting not non-fasting blood sampling

Once it is limited to the group of fasting blood collection, it means that postprandial time and the concerns caused by high glucose levels ​​are linked, that is, a longer time after a meal (fasting) may mean high blood glucose levels. And high blood glucose levels ​​are also closely related to the risk of diabetes. Therefore, fasting blood sampling is associated with a higher risk of diabetes (i.e., the higher incidence of diabetes in the population of fasting blood collection). But fasting should not be associated with the incidence of diabetes.

I am not sure if my understanding is correct. It would be great if someone could help me confirm or point out the problem in my understanding. And whether the model construction I mentioned above is reasonable. Thank you very much.