Prediction of neonatal metabolic maturity in pigs

Improving piglet survival is a key objective for breeders, as higher survival rates improve animal welfare and increase productivity and profitability. Piglets that are not fully developed at birth are more likely to die prematurely. Very immature piglets show a distinctive head morphology, which may serve as an indicator of neonatal severe immaturity. Unlike body weight alone, this phenotype may better capture developmental status, physiological maturity, and survival potential. Following analysis of the head morphology, the study categorized 278 newborns (99 Landrace, 87 Large White, 92 LR \(\times \) LW) according to their maturity level. Furthermore, a metabolomic analysis was also performed by proton nuclear magnetic resonance on serum samples collected on piglets in the hours following birth. The following statistics were based on 55 metabolites with non-zero variance. A subset of 14 metabolites was selected to develop a predictive model based on ExtraTrees and GLM methods. The two models accurately predicted 100% of the severe immaturity , as defined by the head phenotyping, in both the training and test samples. Some piglets morphologically classified as mature were predicted immature (31.7 % in the Test sample) at the metabolic level. The 14-metabolite signature can quantify the maturity with a qualitative score as mature or not, and two quantitative scores, a mean predicted value and a stability of the prediction, which allow the confidence of the prediction to be assessed. The predictive model was applied to an independent dataset of serum collected on different farms and from piglets of different genetic origins. As head morphology was unavailable, the model’s relevance was evaluated by considering other birth-related phenotypes, such as birth weight and body mass index. While maturity is typically defined at the biometric level, we propose a novel approach to define this complex trait at the metabolic level. The identification of a molecular signature could facilitate future experiments aimed at deciphering the genetic architecture of complex traits, such as maturity. This 14-metabolite prediction model of neonatal maturity corresponds to a minimally invasive serum sample analysis method that could be useful for genetic selection and nutritional interventions.