## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin D', 'grams of fat', and 'milligrams of vitamin C', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin D
- $x_1$ : grams of fat
- $x_2$ : milligrams of vitamin C

## 3: Define the objective function in symbolic notation
The objective function to minimize is $4x_0 + 8x_1 + 3x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $5.48x_0 \leq 165$ (Implicit, as $x_0$ has a cardiovascular support index of 5.48, but we need to consider the upper bound in the context of other constraints)
- $1.65x_0 \leq 128$ (Implicit, as $x_0$ has a muscle growth index of 1.65)
- $4.28x_1 \leq 165$
- $2.11x_1 \leq 128$
- $4.11x_2 \leq 165$
- $0.16x_2 \leq 128$
- $5.48x_0 + 4.28x_1 \geq 40$
- $5.48x_0 + 4.11x_2 \geq 19$
- $5.48x_0 + 4.28x_1 + 4.11x_2 \geq 19$
- $1.65x_0 + 2.11x_1 \geq 18$
- $2.11x_1 + 0.16x_2 \geq 16$
- $1.65x_0 + 2.11x_1 + 0.16x_2 \geq 16$
- $2x_0 - 6x_1 \geq 0$
- $5.48x_0 + 4.28x_1 + 4.11x_2 \leq 101$
- $1.65x_0 + 2.11x_1 \leq 101$
- $1.65x_0 + 2.11x_1 + 0.16x_2 \leq 113$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin D'), 
        ('x1', 'grams of fat'), 
        ('x2', 'milligrams of vitamin C')
    ], 
    'objective_function': '4*x0 + 8*x1 + 3*x2', 
    'constraints': [
        '5.48*x0 <= 165', 
        '1.65*x0 <= 128', 
        '4.28*x1 <= 165', 
        '2.11*x1 <= 128', 
        '4.11*x2 <= 165', 
        '0.16*x2 <= 128', 
        '5.48*x0 + 4.28*x1 >= 40', 
        '5.48*x0 + 4.11*x2 >= 19', 
        '5.48*x0 + 4.28*x1 + 4.11*x2 >= 19', 
        '1.65*x0 + 2.11*x1 >= 18', 
        '2.11*x1 + 0.16*x2 >= 16', 
        '1.65*x0 + 2.11*x1 + 0.16*x2 >= 16', 
        '2*x0 - 6*x1 >= 0', 
        '5.48*x0 + 4.28*x1 + 4.11*x2 <= 101', 
        '1.65*x0 + 2.11*x1 <= 101', 
        '1.65*x0 + 2.11*x1 + 0.16*x2 <= 113'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="milligrams_of_vitamin_D", lb=0)  # No lower bound specified, assuming 0
    x1 = model.addVar(name="grams_of_fat", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="milligrams_of_vitamin_C", lb=0)  # No lower bound specified, assuming 0

    # Objective function
    model.setObjective(4 * x0 + 8 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(5.48 * x0 <= 165)
    model.addConstr(1.65 * x0 <= 128)
    model.addConstr(4.28 * x1 <= 165)
    model.addConstr(2.11 * x1 <= 128)
    model.addConstr(4.11 * x2 <= 165)
    model.addConstr(0.16 * x2 <= 128)
    model.addConstr(5.48 * x0 + 4.28 * x1 >= 40)
    model.addConstr(5.48 * x0 + 4.11 * x2 >= 19)
    model.addConstr(5.48 * x0 + 4.28 * x1 + 4.11 * x2 >= 19)
    model.addConstr(1.65 * x0 + 2.11 * x1 >= 18)
    model.addConstr(2.11 * x1 + 0.16 * x2 >= 16)
    model.addConstr(1.65 * x0 + 2.11 * x1 + 0.16 * x2 >= 16)
    model.addConstr(2 * x0 - 6 * x1 >= 0)
    model.addConstr(5.48 * x0 + 4.28 * x1 + 4.11 * x2 <= 101)
    model.addConstr(1.65 * x0 + 2.11 * x1 <= 101)
    model.addConstr(1.65 * x0 + 2.11 * x1 + 0.16 * x2 <= 113)

    # Solve the model
    model.optimize()

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

optimize_problem()
```