To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical model that can be represented in code. The objective is to minimize the function `8.54*z + 6.37*c + 8.42*v`, where `z` represents milligrams of zinc, `c` represents milligrams of calcium, and `v` represents milligrams of vitamin B4.

The constraints given can be categorized into two types: those related to the cardiovascular support index and those related to the energy stability index, along with additional linear constraints involving the variables directly.

### Constraints Translation:

1. **Cardiovascular Support Index Constraints:**
   - For zinc (`z`): `17*z`
   - For calcium (`c`): `5*c`
   - For vitamin B4 (`v`): `18*v`

2. **Energy Stability Index Constraints:**
   - For zinc (`z`): `23*z`
   - For calcium (`c`): `27*c`
   - For vitamin B4 (`v`): `1*v`

3. **Combined Indices and Other Constraints:**
   - Total combined cardiovascular support index from milligrams of calcium plus milligrams of vitamin B4 must be no less than 64: `5*c + 18*v >= 64`
   - Total combined cardiovascular support index from milligrams of zinc, and milligrams of calcium must be 58 or more: `17*z + 5*c >= 58`
   - Total combined cardiovascular support index from milligrams of zinc, milligrams of calcium, and milligrams of vitamin B4 has to be 58 at a minimum: `17*z + 5*c + 18*v >= 58`
   - Similar translations apply for the energy stability indices and other constraints.

Given these conditions, we now construct the Gurobi model in Python:

```python
from gurobipy import *

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

# Define variables
z = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_zinc")
c = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_calcium")
v = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_B4")

# Objective function
m.setObjective(8.54*z + 6.37*c + 8.42*v, GRB.MINIMIZE)

# Constraints
m.addConstr(5*c + 18*v >= 64, "cardiovascular_support_index_1")
m.addConstr(17*z + 5*c >= 58, "cardiovascular_support_index_2")
m.addConstr(17*z + 5*c + 18*v >= 58, "total_cardiovascular_support_index")

m.addConstr(27*c + 1*v >= 37, "energy_stability_index_1")
m.addConstr(23*z + 27*c >= 45, "energy_stability_index_2")
m.addConstr(23*z + 1*v >= 34, "energy_stability_index_3")
m.addConstr(23*z + 27*c + 1*v >= 34, "total_energy_stability_index")

m.addConstr(3*z - 5*c >= 0, "linear_constraint_1")
m.addConstr(4*z - 4*v >= 0, "linear_constraint_2")

m.addConstr(17*z + 18*v <= 149, "cardiovascular_support_upper_bound_1")
m.addConstr(17*z + 5*c + 18*v <= 249, "total_cardiovascular_support_upper_bound")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Zinc: {z.x}")
    print(f"Milligrams of Calcium: {c.x}")
    print(f"Milligrams of Vitamin B4: {v.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found.")
```