When looking at any one variable they’re controlling for the others.
It's plausible that the former variable is "stealing" some of the statistical significance of the latter, leaving the researchers with the impression that the latter is irrelevant.
If you have two independent variables that are highly correlated, and you include both into the model, it's going to be pretty arbitrary which one ends up with statistical significance.
If we're dealing with weak effects and small data, there's very little one can do. That's why epidemiological studies like this kinda suck.
When that's what your data looks like, proper study design either involves testing that hypothesis, or staying the fuck away from making conclusions that take one of those as significant and one as non-significant.