After your comments I think the sample size is not a big issue. But like Peter I would be concerned about the missing data and understanding why so many samples have missing information. If your software won't let you fit some data because if missing covariates see which covariates are missing.
If it is just a couple that cause you to lose so much data drop them and then fit the model. Now many observations will enter the model because they will not have missing covariates. Maybe based on what you observed smoking doesn't really have much effect and any relationship you might see with a model that relate smoking to BMI where smoking is the only covariate may be because it is the smoker that drink that tend to be obese. I have seen many a smoker that is pretty thin.
Compare a model that includes smoking, alcohol and an interaction term between the two and also look at a model with alcohol alone. If the more detailed model is not doing much better fit to the same data then maybe it is okay to drop the smoking groups.
But before taking any of these recommendation get to understand better why covariates are missing in so many cases. Maybe you can looked at the dropped cases and compare the demographics (gender, age etc) for the fitted sample with what it is for the missing data. There could be bias due to covariate imbalance. Vance Berger's book which I have previously cited here on CV could help you with that.