The Linear-Logistic Model: A Novel Paradigm for Estimating Dietary Amino Acid Requirements

SIMPLE SUMMARY: This study discusses the importance of providing animals with adequate nutrients to promote their health and welfare. Current methods for estimating amino acid (AA) requirements only consider growth and protein retention, without considering other physiological responses. To address this, the authors used exploratory data analysis (EDA) to investigate whether growth and protein retention measurements are enough to develop dietary AA recommendations that optimize animal performance, health, and survival. The EDA revealed that AA dietary levels above those for maximum growth can lead to improvements in physiological responses related to milk yield, litter size, immune response, intestinal permeability, and plasma AA concentrations. ABSTRACT: This study aimed to determine whether current methods for estimating AA requirements for animal health and welfare are sufficient. An exploratory data analysis (EDA) was conducted, which involved a review of assumptions underlying AA requirements research, a data mining approach to identify animal responses to dietary AA levels exceeding those for maximum protein retention, and a literature review to assess the physiological relevance of the linear-logistic model developed through the data mining approach. The results showed that AA dietary levels above those for maximum growth resulted in improvements in key physiological responses, and the linear-logistic model depicted the AA level at which growth and protein retention rates were maximized, along with key metabolic functions related to milk yield, litter size, immune response, intestinal permeability, and plasma AA concentrations. The results suggest that current methods based solely on growth and protein retention measurements are insufficient for optimizing key physiological responses associated with health, survival, and reproduction. The linear-logistic model could be used to estimate AA doses that optimize these responses and, potentially, survival rates.