Comparing two sets of time-series (physiological measures) I need help in finding which statistical approach I need to use. 
I have two groups: G1 (disease) and control. 
For both groups we measured their blood level every 10 minutes: 0 to 650 min.
We are trying to find the differences between G1 (disease) and control and the 
significance of the difference.
The variables in factors are Age, Diagnosis (disease or control), and time.
The dependent variable is the blood level. 
I do not know which approach I need to use to find this significance (difference).
I tried using Multiple Regression Analysis: 
P = constant + B1(age) + B2(Diagnosis) + B3(Time) 
Diagnosis 0:constant  1:Patient 
If I also want to include the Interaction (Diagnosis x Time) how would I calculate that to put into Excel for analysis? 
 A: I second @user21240's answer and @zbicyclist's comment but would like to offer a few additions:
First, the model @user21240 proposes models a linear course of the blood level over 11 hours time. We don't know what substance you are measuring, but if there could be diurnal cycles (e.g., for cortisol), you should account for these. My recommendation: transform your time variable using restricted cubic splines or natural splines.
Second, you are measuring each participant multiple times. It stands to reason that every participant's blood level will correlate strongly over time. You should really take that correlation into account in the model and use mixed models (also known as "repeated measures models").
Third, an elaboration on my second point above: the first and the second measurement of each participant will probably correlate more strongly than the first and the thirtieth measurement. In your mixed models, you can account for this effect by choosing an appropriate correlation structure for your random effects. In the statistical package R, you can use the corCAR1 correlation structure in the nlme package to model a time-dependent autoregressive (AR) of order 1 correlation structure. Unfortunately, I don't know what SPSS offers in this line, so you may want to consult the manual or help pages.
Fourth, as @zbicyclist notes, you may want to look at interactions between Age and Diagnosis... but you may also want to investigate interactions between Diagnosis and Time (and spline-transformed Time, see above), since the Diagnosis may have an impact on the time course.
Fifth, by now you have a model selection problem on your hands (spline transform Time or not, how many spline knots, log transform Age or not, which interaction to use, which correlation structure to use...). For the fixed effects, you can use a variant of AIC; this reference may help. Unfortunately, I am not aware of a formalized way to choose the correlation structure - here I would simply use what makes sense and what your software offers.
Good luck!
A: You could treat the diagnosis as a dummy variable, fit a linear model:
$Blood Level= \alpha + \beta_1 Age +\beta_2 Diagnosis + \beta_3 Time + \epsilon$ 
and look at the significance of the dummy variable, $Diagnosis$. 
