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High Efficiency

Train BCAAs.

Metabolic profile analysis. Technical breakdown of Train BCAAs and its impact on skeletal muscle retention and daily energy expenditure.

Total Energy33kcal
Bio-Protein47g
Carbohydrates7g
Total Lipids0g

Nutritional Efficiency Report

Train BCAAs is a very low-calorie food with elite-protein content. At 33 kcal per 100g, it delivers 47g of protein — representing 570% of its total caloric load. Carbohydrates account for 85% and fats for 0% of calories.

"Efficiency Score 1.00 — this asset ranks in the top tier of the DataFood database for protein delivery per calorie. Optimal for fat loss, lean bulking, and body recomposition phases."

With 7g of carbs per 100g, it suits cyclical keto and low-carb approaches.

With only 33 kcal per 100g, Train BCAAs is a high-volume food — you can consume a large serving while keeping total caloric intake controlled. This makes it particularly effective for hunger management in deficit phases.

Protocol Compatibility

Ketosis Impact

With 7g of carbs per 100g, it suits cyclical keto and low-carb approaches.

Thermic Effect (TEF)

Protein content of 47g per 100g triggers a significant Thermic Effect of Food. The body expends approximately 20–30% of protein calories during digestion, yielding a net caloric benefit compared to equivalent fat or carbohydrate sources.

Expand Your Protocol.

Based on the metabolic profile of Train BCAAs, these related data nodes are most relevant to your nutrition architecture:

Integrate Asset.

Use the clinical weekly planner to distribute Train BCAAs across your metabolic windows.

Generate Weekly Protocol

Input physical metrics to generate a synchronized 7-day protocol.

1. Biological Metrics

2. Dietary Logic

Institutional Disclaimer: The nutritional data provided for Train BCAAs is derived from clinical databases and algorithmic estimation. This report is for metabolic infrastructure planning only and does not constitute medical advice. Consult a clinical nutritionist before implementing aggressive deficit protocols.