Human milk is recommended for feeding preterm infants. The current pilot study aims to determine whether breast-milk lipidome had any impact on the early growth-pattern of preterm infants fed their own mother’s milk. A prospective-monocentric-observational birth-cohort was established, enrolling 138 preterm infants, who received their own mother’s breast-milk throughout hospital stay. All infants were ranked according to the change in weight Z-score between birth and hospital discharge. Then, we selected infants who experienced “slower” (n = 15, −1.54 ± 0.42 Z-score) or “faster” (n = 11, −0.48 ± 0.19 Z-score) growth; as expected, although groups did not differ regarding gestational age, birth weight Z-score was lower in the “faster-growth” group (0.56 ± 0.72 vs. −1.59 ± 0.96). Liquid chromatography–mass spectrometry lipidomic signatures combined with multivariate analyses made it possible to identify breast-milk lipid species that allowed clear-cut discrimination between groups. Validation of the selected biomarkers was performed using multidimensional statistical, false-discovery-rate and ROC (Receiver Operating Characteristic) tools. Breast-milk associated with faster growth contained more medium-chain saturated fatty acid and sphingomyelin, dihomo-γ-linolenic acid (DGLA)-containing phosphethanolamine, and less oleic acid-containing triglyceride and DGLA-oxylipin. The ability of such biomarkers to predict early-growth was validated in presence of confounding clinical factors but remains to be ascertained in larger cohort studies.

Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.