To solve this optimization problem, we first need to identify and translate all given information into a symbolic representation. The variables are 'grams of fiber' (let's denote this as `x1`) and 'milligrams of vitamin E' (`x2`). 

The objective function to maximize is: `3.18 * x1 + 3.26 * x2`.

Given constraints:
- Cognitive performance index from grams of fiber is 10, so we have `10*x1`.
- Cardiovascular support index for grams of fiber is 13, giving us `13*x1`.
- Kidney support index of grams of fiber is 16, thus `16*x1`.
- For milligrams of vitamin E:
  - Cognitive performance index: `11*x2`.
  - Cardiovascular support index: `12*x2`.
  - Kidney support index: `10*x2`.

Combined indices constraints:
- Total cognitive performance index >= 13: `10*x1 + 11*x2 >= 13`.
- Total cardiovascular support index >= 18: `13*x1 + 12*x2 >= 18`.
- Total kidney support index >= 16: `16*x1 + 10*x2 >= 16`.
- Another constraint given is `5*x1 - 10*x2 >= 0`.

Upper bounds:
- Total cognitive performance index <= 61: `10*x1 + 11*x2 <= 61`.
- Total cardiovascular support index <= 50: `13*x1 + 12*x2 <= 50`.
- Total kidney support index <= 46: `16*x1 + 10*x2 <= 46`.

Integer constraint for grams of fiber (`x1` is an integer).

Here's the symbolic representation:
```json
{
    'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin E')],
    'objective_function': '3.18 * x1 + 3.26 * x2',
    'constraints': [
        '10*x1 + 11*x2 >= 13',
        '13*x1 + 12*x2 >= 18',
        '16*x1 + 10*x2 >= 16',
        '5*x1 - 10*x2 >= 0',
        '10*x1 + 11*x2 <= 61',
        '13*x1 + 12*x2 <= 50',
        '16*x1 + 10*x2 <= 46'
    ]
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="grams_of_fiber")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_E")

# Objective function: maximize 3.18*x1 + 3.26*x2
m.setObjective(3.18 * x1 + 3.26 * x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10*x1 + 11*x2 >= 13, "cognitive_performance_index")
m.addConstr(13*x1 + 12*x2 >= 18, "cardiovascular_support_index")
m.addConstr(16*x1 + 10*x2 >= 16, "kidney_support_index")
m.addConstr(5*x1 - 10*x2 >= 0, "additional_constraint")
m.addConstr(10*x1 + 11*x2 <= 61, "cognitive_upper_bound")
m.addConstr(13*x1 + 12*x2 <= 50, "cardiovascular_upper_bound")
m.addConstr(16*x1 + 10*x2 <= 46, "kidney_upper_bound")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of Fiber: {x1.x}")
    print(f"Milligrams of Vitamin E: {x2.x}")
else:
    print("No optimal solution found")
```