You’ll have to clarify those points. For the first part, M-bias is not confounding. It’s a kind of selection bias, and it happens when there is no causal relation with the independent or dependent variables (not no correlation), specifically when you try to adjust for confounding that doesn’t exist. The collider can be a confounder, but it doesn’t have to be. From the second link, “some authors refer to this type of (M-bias) as confounding...but this extension has no practical consequences”
I don’t think you can get a good control group after the fact, because you need their outcomes at both timepoints, with a year in between. None of the options that come to mind are very good: you could ask them what they would have answered a year ago, you could start collecting data now and ask them in a year’s time, or you could throw out the temporal data and use only a single cross-section.
I used “depends” informally, so I didn’t mean to say that variables that depend on treatment and outcome are always confounders. I was answering the implication that a variable with no detectable correlation with the outcome is not likely to be a source of confounding. I assumed they were using a correlational definition of confounding, so I answered in that context.