If you are using a linear regression of DichoOutcome (with a logit link) on Timepoint, then there is only one odds ratio, which is the odds ratio for a one-unit change in Timepoint, regardless of whether the change is from 0 to 1 or from 9 to 10, or as reported from 6 to 7. (I think 6 is used because it is the mean Timepoint value but I would not swear to that.)
The most straightforward way to get the odds ratios that you want is to use Timepoint as a classification variable in the MODEL statement (an ANOVA-like model, rather than a regression) in combination with the LSMEANS statement:
proc glimmix data=datasetname method=laplace;
class id Timepoint;
model DichoOutcome= Timepoint / dist=bin link=logit solution;
random intercept / subject = id;
random Timepoint / subject=id type=ar(1); /* You could try omitting this statement, too */
lsmeans Timepoint / diff oddsratio;
run;
But, of course, now you are no longer regressing on Timepoint, and you'll have to decide which approach is more appropriate for your research questions and your data. For example, do your data meet the linearity assumption? If so, then regression is nicely parsimonious. If not, then a linear regression is a wrong model.
The text Applied Logistic Regression by Hosmer and Lemeshow has an excellent chapter on interpretation of the fitted model, distinguishing nicely between dichotomous, polychotomous, and continuous predictors. You would find it helpful, I think. Paul Allison's text is also quite good.
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