I am running a regression of 5 independent variables:
y=intercept + beta1*X1+ beta2*X2 + beta3*X3+ beta4*X4 +beta5*X5

Someone has questionnned whether X1 truly captures what is supposed to capture since it is an index about how well a firm is managed but this index doesn't take into account the board of directors variables (BOD) and he suggested to take into consideration 4 BOD variables (such as how frequently BOD meets etc..).
My question is what about the best approach to use:
1- to run regressions of 6 independent variables where I add each time one BOD variable to show that X1 would stay significant after adding the BOD variable (i.e I will report 4 regressions where x6 each time take the values of one BOD variable)
2- to add the 4 BOD variables to the regression and make sure that I don't have a multicolinearity problem (Here I have a question whether a 9 variable regression makes sense)
3-Other suggestions?