Cal/Mag 1:2
Cal/Mag 1:2 by Metabolic Maintenance is a bone and joint support supplement featuring 6 ingredients. The product has an N+ Score of 53, earning it an F grade, indicating a below-average formulation. While Magnesium at 125mg and Zinc at 5mg are provided at dosages research suggests may be beneficial, other key ingredients such as Vitamin C (10mg), Calcium (63mg), and Potassium (25mg) appear underdosed compared to typical clinical recommendations. The formula provides full label transparency, but its overall ingredient adequacy and completeness contribute to its lower score. This product may be considered by individuals looking for specific magnesium and zinc support, but it falls short in delivering comprehensive bone health nutrients at effective levels.
About This Product
Cal/Mag 1:2 by Metabolic Maintenance is a joint & bone supplement containing 6 active ingredients. It has earned an N+ Score of 53/100 (Grade F).
Notable clinical-dose ingredients include Magnesium, Zinc.
N+ Score Breakdown
Are key ingredients present at clinically effective doses based on research?
Does the product include all expected ingredients for its supplement category?
Are individual ingredient amounts clearly disclosed without proprietary blends?
Does the formula include a breadth of beneficial compounds from multiple pathways?
Strengths
- +Fully transparent label with individual ingredient amounts disclosed
- +Broad ingredient diversity exceeding category norms
- +2 ingredient(s) at clinical dose levels
Weaknesses
- −Several ingredients below clinically effective doses
- −Missing several expected ingredients for its category
Ingredient Analysis (6 ingredients)
| Ingredient | Amount | Dose Adequacy |
|---|---|---|
| Vitamin C | 10.000 mg | Under |
| Calcium | 63.000 mg | Under |
| Magnesium | 125.000 mg | Adequate |
| Zinc | 5.000 mg | Adequate |
| Boron | 500.000 mcg | N/A |
| Potassium | 25.000 mg | Under |
Clinical ranges based on NIH ODS Fact Sheets and peer-reviewed research. Status indicates whether the amount meets evidence-based thresholds.