## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin A', 'milligrams of vitamin B4', and 'milligrams of vitamin B12'. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $6.67x_0 + 8.69x_1 + 9.29x_2$.

## 3: List the constraints in symbolic notation
The constraints are:
- $3x_0 \leq 160$ (kidney support index of $x_0$)
- $14x_0 \leq 109$ (cognitive performance index of $x_0$)
- $10x_1 \leq 160$ (kidney support index of $x_1$)
- $4x_1 \leq 109$ (cognitive performance index of $x_1$)
- $1x_2 \leq 160$ (kidney support index of $x_2$)
- $13x_2 \leq 109$ (cognitive performance index of $x_2$)
- $7x_1 - 8x_2 \geq 0$
- $10x_1 + 1x_2 \leq 139$
- $3x_0 + 1x_2 \leq 127$
- $3x_0 + 10x_1 \leq 96$
- $3x_0 + 10x_1 + 1x_2 \leq 96$
- $14x_0 + 4x_1 \leq 53$
- $4x_1 + 13x_2 \leq 80$
- $14x_0 + 4x_1 + 13x_2 \leq 70$

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'milligrams of vitamin A'), 
    ('x1', 'milligrams of vitamin B4'), 
    ('x2', 'milligrams of vitamin B12')
],
'objective_function': '6.67*x0 + 8.69*x1 + 9.29*x2',
'constraints': [
    '3*x0 <= 160', 
    '14*x0 <= 109', 
    '10*x1 <= 160', 
    '4*x1 <= 109', 
    '1*x2 <= 160', 
    '13*x2 <= 109', 
    '7*x1 - 8*x2 >= 0', 
    '10*x1 + 1*x2 <= 139', 
    '3*x0 + 1*x2 <= 127', 
    '3*x0 + 10*x1 <= 96', 
    '3*x0 + 10*x1 + 1*x2 <= 96', 
    '14*x0 + 4*x1 <= 53', 
    '4*x1 + 13*x2 <= 80', 
    '14*x0 + 4*x1 + 13*x2 <= 70'
]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin A
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B4
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B12

    # Define the objective function
    model.setObjective(6.67 * x0 + 8.69 * x1 + 9.29 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x0 <= 160)  
    model.addConstr(14 * x0 <= 109)  
    model.addConstr(10 * x1 <= 160)  
    model.addConstr(4 * x1 <= 109)  
    model.addConstr(x2 <= 160)  
    model.addConstr(13 * x2 <= 109)  
    model.addConstr(7 * x1 - 8 * x2 >= 0)  
    model.addConstr(10 * x1 + x2 <= 139)  
    model.addConstr(3 * x0 + x2 <= 127)  
    model.addConstr(3 * x0 + 10 * x1 <= 96)  
    model.addConstr(3 * x0 + 10 * x1 + x2 <= 96)  
    model.addConstr(14 * x0 + 4 * x1 <= 53)  
    model.addConstr(4 * x1 + 13 * x2 <= 80)  
    model.addConstr(14 * x0 + 4 * x1 + 13 * x2 <= 70)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin A: {x0.varValue}")
        print(f"Milligrams of vitamin B4: {x1.varValue}")
        print(f"Milligrams of vitamin B12: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```