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Overview of Fatty Acid Metabolism01:28

Overview of Fatty Acid Metabolism

Lipids also are sources of energy that power cellular processes. Like carbohydrates, lipids are composed of carbon, hydrogen, and oxygen, but these atoms are arranged differently. Most lipids are nonpolar and hydrophobic. Major types include fats and oils, waxes, phospholipids, and steroids.
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The human body gets energy from the three macronutrients: carbohydrates, proteins, and fats. Energy is released when the chemical bonds in the organic compounds present in the food are broken down. The energy content of food is measured in kilocalories (kcal), defined as the amount of heat required to raise the temperature of one kilogram of water by one degree Celsius. This value is determined by measuring the temperature change of the water surrounding a calorimeter after the complete...
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Milk Collection in the Rat Using Capillary Tubes and Estimation of Milk Fat Content by Creamatocrit
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Energy Balance Prediction in Japanese Holstein Cows by Using Milk Fatty Acid Composition.

Akiko Nishiura1, Osamu Sasaki1, Tamako Tanigawa2

  • 1National Agriculture and Food Research Organization, Institute of Livestock and Grassland Science, Tsukuba, Ibaraki, Japan.

Animal Science Journal = Nihon Chikusan Gakkaiho
|May 21, 2026
PubMed
Summary

Accurately predicting dairy cow energy balance (EB) is crucial for health. New equations using milk fatty acid (FA) profiles improve EB prediction beyond traditional milk yield and composition data.

Keywords:
de novo fatty acidsenergy balancemilk fatty acidsprediction equationpreformed fatty acids

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Area of Science:

  • Animal Science
  • Dairy Cattle Nutrition
  • Metabolic Health

Background:

  • Negative energy balance (NEB) in early lactation negatively impacts dairy cow health and fertility.
  • Direct energy balance (EB) measurement is impractical due to intensive monitoring requirements.

Purpose of the Study:

  • Develop a practical equation for predicting EB in dairy cows.
  • Evaluate the external validity of the new EB prediction equation.

Main Methods:

  • Utilized 14,993 records from 93 Holstein cows (6-103 days in milk).
  • Applied stepwise regression (Schwarz Bayesian Criterion) selecting preformed FA, milk fat, DIM class, de novo FA, milk yield, and milk protein percentage.
  • Validated the equation using nationwide data from 18 institutions.

Main Results:

  • The developed model achieved an R² of 0.65 for EB prediction.
  • External validation yielded an R² of 0.32, outperforming a previous equation (R²=0.17).
  • Preformed FA (body fat mobilization) and de novo FA (physiological information) were key predictors.

Conclusions:

  • Incorporating FTIR-predicted milk FA composition significantly improves EB prediction accuracy and robustness.
  • The new equation offers a practical tool for metabolic monitoring and genetic evaluation in diverse dairy management settings.