As already said, controlling usually means including a variable in a regression (as pointed out by @EMS, this doesn't guarantee any success in achieving this, he links to this). There exist already some highly voted questions and answers on this topic, such as:
The accepted answers on these questions are all very good treatments of the question you are asking within an observational (I would say correlational) framework, more such questions can be found here.
However, you are asking your question specifically within an experimental or ANOVA framework, some more thoughts on this topic can be given.
Within an experimental framework you control for a variable by randomizing individuals (or other units of observation) on the different experimental conditions. The underlying assumption is that as a consequence the only difference between the conditions is the experimental treatment. When correctly randomizing (i.e., each individual has the same chance to be in each condition) this is a reasonable assumption. Furthermore, only randomization allows you to draw causal inferences from your observation as this is the only way to make sure that not other factors are responsible for your results.
However, it can also be necessary to control for variables within an experimental framework, namely when there is another known factor that also affects that dependent variable. To enhance statistical power and can then be a good idea to control for this variable. The usual statistical procedure used for this is analysis of covariance (ANCOVA), which basically also just adds the variable to the model.
Now comes the crux: For ANCOVA to be reasonable, it is absolutely crucial that the assignment to the groups is random and that the covariate for which it is controlled is not correlated with the grouping variable.
This is unfortunately often ignored leading to uninterpretable results. A really readable introduction to this exact issue (i.e., when to use ANCOVA or not) is given by Miller & Chapman (2001):
Despite numerous technical treatments in many venues, analysis of
covariance (ANCOVA) remains a widely misused approach to dealing with
substantive group differences on potential covariates, particularly in
psychopathology research. Published articles reach unfounded
conclusions, and some statistics texts neglect the issue. The problem
with ANCOVA in such cases is reviewed. In many cases, there is no
means of achieving the superficially appealing goal of "correcting" or
"controlling for" real group differences on a potential covariate. In
hopes of curtailing misuse of ANCOVA and promoting appropriate use, a
nontechnical discussion is provided, emphasizing a substantive
confound rarely articulated in textbooks and other general
presentations, to complement the mathematical critiques already
available. Some alternatives are discussed for contexts in which
ANCOVA is inappropriate or questionable.
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110(1), 40–48. doi:10.1037/0021-843X.110.1.40
Best Answer
If metabolic syndrome is an ordered categorical variable with six levels (0, 1, ..., 5) then a model for ordinal logistic regression would be better than purely doing $\chi^2$ which is responsive to any deviation whereas you want to know if the patient group have higher odds of having a worse metabolic syndrome profile. You can then add sex into your model as a covariate. I have no idea whether you can do this in SPSS but I imagine an online search for SPSS ordinal logistic regression will turn up something.