To solve the given optimization problem using Gurobi, we need to define the variables, objective function, and constraints according to the provided description. The variables are 'grams of fiber' (x0) and 'milligrams of vitamin B5' (x1). The objective is to minimize 1*x0 + 2*x1.

The constraints can be summarized as follows:
- Cognitive performance index: 4*x0 + 4*x1 >= 1, and 4*x0 + 4*x1 <= 11.
- Kidney support index: 3*x0 + 4*x1 >= 6, and 3*x0 + 4*x1 <= 26.
- Digestive support index: 1*x0 + 3*x1 >= 6, and 1*x0 + 3*x1 <= 22.
- Additional constraint: -2*x0 + 4*x1 >= 0.

Here is the Gurobi code that captures this problem:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="grams_of_fiber")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B5")

# Objective function: minimize 1*x0 + 2*x1
m.setObjective(1*x0 + 2*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(4*x0 + 4*x1 >= 1, "cognitive_performance_min")
m.addConstr(4*x0 + 4*x1 <= 11, "cognitive_performance_max")
m.addConstr(3*x0 + 4*x1 >= 6, "kidney_support_min")
m.addConstr(3*x0 + 4*x1 <= 26, "kidney_support_max")
m.addConstr(1*x0 + 3*x1 >= 6, "digestive_support_min")
m.addConstr(1*x0 + 3*x1 <= 22, "digestive_support_max")
m.addConstr(-2*x0 + 4*x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"grams_of_fiber: {x0.x}")
    print(f"milligrams_of_vitamin_B5: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```