## Step 1: Define the variables and their attributes
We have four variables: $x_0$ (milligrams of magnesium), $x_1$ (milligrams of vitamin B12), $x_2$ (milligrams of calcium), and $x_3$ (milligrams of vitamin B1). Each variable has attributes based on the given resources/attributes.

## 2: Formulate the objective function
The objective function to minimize is $2x_0 + x_1 + 6x_2 + 6x_3$.

## 3: Define the constraints
Based on the problem description, we have several constraints:
- Immune support index constraints:
- $7x_0 + 10x_1 + 14x_2 + 3x_3 \geq 21$ (for $x_0$ and $x_1$),
- $7x_0 + 3x_3 \geq 21$,
- $10x_1 + 3x_3 \geq 31$,
- $7x_0 + 14x_2 + 3x_3 \geq 23$,
- $7x_0 + 10x_1 + 14x_2 \geq 23$,
- $7x_0 + 14x_2 + 3x_3 \geq 19$,
- $7x_0 + 10x_1 + 14x_2 \geq 19$,
- $7x_0 + 10x_1 + 14x_2 + 3x_3 \geq 19$.

- Cardiovascular support index constraints:
- $x_0 + x_2 \geq 37$,
- $6x_1 + 11x_3 \geq 64$,
- $x_0 + 6x_1 \geq 22$,
- $6x_1 + 5x_2 \geq 61$,
- $x_0 + 11x_3 \geq 33$,
- $5x_2 + 11x_3 \geq 31$,
- $x_0 + 6x_1 + 11x_3 \geq 62$,
- $x_0 + 6x_1 + 5x_2 \geq 62$,
- $6x_1 + 5x_2 + 11x_3 \geq 62$,
- $x_0 + 6x_1 + 11x_3 \geq 52$,
- $x_0 + 6x_1 + 5x_2 \geq 52$,
- $6x_1 + 5x_2 + 11x_3 \geq 52$,
- $x_0 + 6x_1 + 11x_3 \geq 66$,
- $x_0 + 6x_1 + 5x_2 \geq 66$,
- $6x_1 + 5x_2 + 11x_3 \geq 66$,
- $x_0 + 6x_1 + 5x_2 + 11x_3 \geq 66$.

- Energy stability index constraints:
- $4x_2 + 14x_3 \geq 41$,
- $2x_0 + 10x_1 \geq 20$,
- $10x_1 + 14x_3 \geq 21$,
- $2x_0 + 4x_2 + 14x_3 \geq 34$,
- $2x_0 + 10x_1 + 14x_3 \geq 34$,
- $2x_0 + 4x_2 + 14x_3 \geq 38$,
- $2x_0 + 10x_1 + 14x_3 \geq 38$,
- $2x_0 + 4x_2 + 10x_1 + 14x_3 \geq 38$,
- $4x_2 + 14x_3 \leq 86$,
- $2x_0 + 14x_3 \leq 130$,
- $2x_0 + 10x_1 \leq 116$,
- $2x_0 + 10x_1 + 4x_2 \leq 185$.

- Kidney support index constraints:
- $2x_2 + 2x_3 \geq 21$,
- $13x_1 + 2x_3 \geq 26$,
- $13x_1 + 2x_2 \geq 14$,
- $8x_0 + 2x_2 + 2x_3 \geq 31$,
- $8x_0 + 13x_1 + 2x_2 + 2x_3 \geq 31$,
- $x_0 - 10x_1 \geq 0$,
- $8x_0 + 2x_3 \leq 73$,
- $8x_0 + 13x_1 \leq 40$,
- $13x_1 + 2x_3 \leq 111$,
- $8x_0 + 2x_2 \leq 102$,
- $8x_0 + 13x_1 + 2x_3 \leq 82$.

## 4: Implement the problem in Gurobi
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="milligrams of magnesium", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x1 = model.addVar(name="milligrams of vitamin B12", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="milligrams of calcium", lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS)
    x3 = model.addVar(name="milligrams of vitamin B1", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(2*x0 + x1 + 6*x2 + 6*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    # Immune support index
    model.addConstr(7*x0 + 10*x1 + 14*x2 + 3*x3 >= 21)
    model.addConstr(7*x0 + 3*x3 >= 21)
    model.addConstr(10*x1 + 3*x3 >= 31)
    model.addConstr(7*x0 + 14*x2 + 3*x3 >= 23)
    model.addConstr(7*x0 + 10*x1 + 14*x2 >= 23)
    model.addConstr(7*x0 + 14*x2 + 3*x3 >= 19)
    model.addConstr(7*x0 + 10*x1 + 14*x2 >= 19)
    model.addConstr(7*x0 + 10*x1 + 14*x2 + 3*x3 >= 19)

    # Cardiovascular support index
    model.addConstr(x0 + 5*x2 >= 37)
    model.addConstr(6*x1 + 11*x3 >= 64)
    model.addConstr(x0 + 6*x1 >= 22)
    model.addConstr(6*x1 + 5*x2 >= 61)
    model.addConstr(x0 + 11*x3 >= 33)
    model.addConstr(5*x2 + 11*x3 >= 31)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 62)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 62)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 62)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 52)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 52)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 52)
    model.addConstr(x0 + 6*x1 + 11*x3 >= 66)
    model.addConstr(x0 + 6*x1 + 5*x2 >= 66)
    model.addConstr(6*x1 + 5*x2 + 11*x3 >= 66)
    model.addConstr(x0 + 6*x1 + 5*x2 + 11*x3 >= 66)

    # Energy stability index
    model.addConstr(4*x2 + 14*x3 >= 41)
    model.addConstr(2*x0 + 10*x1 >= 20)
    model.addConstr(10*x1 + 14*x3 >= 21)
    model.addConstr(2*x0 + 4*x2 + 14*x3 >= 34)
    model.addConstr(2*x0 + 10*x1 + 14*x3 >= 34)
    model.addConstr(2*x0 + 4*x2 + 14*x3 >= 38)
    model.addConstr(2*x0 + 10*x1 + 14*x3 >= 38)
    model.addConstr(2*x0 + 10*x1 + 4*x2 + 14*x3 >= 38)
    model.addConstr(4*x2 + 14*x3 <= 86)
    model.addConstr(2*x0 + 14*x3 <= 130)
    model.addConstr(2*x0 + 10*x1 <= 116)
    model.addConstr(2*x0 + 10*x1 + 4*x2 <= 185)

    # Kidney support index
    model.addConstr(2*x2 + 2*x3 >= 21)
    model.addConstr(13*x1 + 2*x3 >= 26)
    model.addConstr(13*x1 + 2*x2 >= 14)
    model.addConstr(8*x0 + 2*x2 + 2*x3 >= 31)
    model.addConstr(8*x0 + 13*x1 + 2*x2 + 2*x3 >= 31)
    model.addConstr(x0 - 10*x1 >= 0)
    model.addConstr(8*x0 + 2*x3 <= 73)
    model.addConstr(8*x0 + 13*x1 <= 40)
    model.addConstr(13*x1 + 2*x3 <= 111)
    model.addConstr(8*x0 + 2*x2 <= 102)
    model.addConstr(8*x0 + 13*x1 + 2*x3 <= 82)

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of magnesium: {x0.varValue}")
        print(f"Milligrams of vitamin B12: {x1.varValue}")
        print(f"Milligrams of calcium: {x2.varValue}")
        print(f"Milligrams of vitamin B1: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```