DataFood Engine.Monexa Data Node
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Verified Asset
High Efficiency

Vin.

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

Total Energy61kcal
Bio-Protein28g
Carbohydrates35g
Total Lipids0g

Nutritional Efficiency Report

Vin is a low-calorie food with high-protein content. At 61 kcal per 100g, it delivers 28g of protein — representing 184% of its total caloric load. Carbohydrates account for 230% 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."

At 35g carbs per 100g, it is better suited to carb-cycling or maintenance phases.

With only 61 kcal per 100g, Vin 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

At 35g carbs per 100g, it is better suited to carb-cycling or maintenance phases.

Thermic Effect (TEF)

Protein content of 28g 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 Vin, these related data nodes are most relevant to your nutrition architecture:

High Protein Foods Fat Loss Foods Database

Integrate Asset.

Use the clinical weekly planner to distribute Vin 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 Vin 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.