## Step 1: Define the optimization problem
The goal is to minimize the objective function: $5 \times \text{grams of fiber} + 1 \times \text{milligrams of potassium} + 1 \times \text{milligrams of magnesium}$.

## Step 2: List all variables and their attributes
Variables: 
- $x_0$: grams of fiber
- $x_1$: milligrams of potassium
- $x_2$: milligrams of magnesium

Attributes (resources/indices):
- $r_0$: kidney support index
- $r_1$: energy stability index
- $r_2$: digestive support index
- $r_3$: muscle growth index
- $r_4$: cardiovascular support index

Given attributes for each variable:
- $x_0$: $r_0 = 8.44$, $r_1 = 6.43$, $r_2 = 0.91$, $r_3 = 0.9$, $r_4 = 4.96$
- $x_1$: $r_0 = 8.9$, $r_1 = 2.94$, $r_2 = 4.62$, $r_3 = 6.06$, $r_4 = 7.58$
- $x_2$: $r_0 = 6.36$, $r_1 = 3.65$, $r_2 = 8.97$, $r_3 = 8.69$, $r_4 = 6.79$

## 3: Formulate constraints
1. $8.44x_0 + 6.36x_2 \geq 69$
2. $8.9x_1 + 6.36x_2 \geq 37$
3. $8.44x_0 + 8.9x_1 + 6.36x_2 \geq 37$
4. $2.94x_1 + 3.65x_2 \geq 56$
5. $6.43x_0 + 2.94x_1 \geq 49$
6. $6.43x_0 + 2.94x_1 + 3.65x_2 \geq 49$
7. $0.91x_0 + 4.62x_1 \geq 33$
8. $0.91x_0 + 4.62x_1 + 8.97x_2 \geq 33$
9. $0.9x_0 + 6.06x_1 \geq 50$
10. $0.9x_0 + 8.69x_2 \geq 56$
11. $6.06x_1 + 8.69x_2 \geq 62$
12. $0.9x_0 + 6.06x_1 + 8.69x_2 \geq 62$
13. $4.96x_0 + 6.79x_2 \geq 17$
14. $4.96x_0 + 7.58x_1 + 6.79x_2 \geq 17$
15. $-2x_1 + 9x_2 \geq 0$
16. $-6x_0 + 3x_1 \geq 0$
17. $2.94x_1 + 3.65x_2 \leq 229$
18. $6.43x_0 + 2.94x_1 \leq 200$
19. $7.58x_1 + 6.79x_2 \leq 97$
20. $x_0$ is an integer
21. $x_1$ is an integer

## 4: Implement in Gurobi
```python
import gurobi

def optimize():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(name="grams_of_fiber", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="milligrams_of_potassium", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="milligrams_of_magnesium")
    
    # Objective function
    model.setObjective(5 * x0 + x1 + x2, gurobi.GRB.MINIMIZE)
    
    # Constraints
    model.addConstr(8.44 * x0 + 6.36 * x2 >= 69)
    model.addConstr(8.9 * x1 + 6.36 * x2 >= 37)
    model.addConstr(8.44 * x0 + 8.9 * x1 + 6.36 * x2 >= 37)
    model.addConstr(2.94 * x1 + 3.65 * x2 >= 56)
    model.addConstr(6.43 * x0 + 2.94 * x1 >= 49)
    model.addConstr(6.43 * x0 + 2.94 * x1 + 3.65 * x2 >= 49)
    model.addConstr(0.91 * x0 + 4.62 * x1 >= 33)
    model.addConstr(0.91 * x0 + 4.62 * x1 + 8.97 * x2 >= 33)
    model.addConstr(0.9 * x0 + 6.06 * x1 >= 50)
    model.addConstr(0.9 * x0 + 8.69 * x2 >= 56)
    model.addConstr(6.06 * x1 + 8.69 * x2 >= 62)
    model.addConstr(0.9 * x0 + 6.06 * x1 + 8.69 * x2 >= 62)
    model.addConstr(4.96 * x0 + 6.79 * x2 >= 17)
    model.addConstr(4.96 * x0 + 7.58 * x1 + 6.79 * x2 >= 17)
    model.addConstr(-2 * x1 + 9 * x2 >= 0)
    model.addConstr(-6 * x0 + 3 * x1 >= 0)
    model.addConstr(2.94 * x1 + 3.65 * x2 <= 229)
    model.addConstr(6.43 * x0 + 2.94 * x1 <= 200)
    model.addConstr(7.58 * x1 + 6.79 * x2 <= 97)
    
    # Optimize
    model.optimize()
    
    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Grams of fiber: ", x0.varValue)
        print("Milligrams of potassium: ", x1.varValue)
        print("Milligrams of magnesium: ", x2.varValue)
    else:
        print("No optimal solution found")

optimize()
```