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During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (−0.149), dMEO → urea (0.038), glucose → BHB (−0.55), glucose → urea (−0.194), CHOL → urea (0.175), BHB → urea (−0.049), and NEFA → urea (−0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (−0.583, P= 100), dMEO and BHB (0.349, P= 99), glucose and CHOL (0.325, P= 100), glucose and NEFA (−0.388, P= 100), and NEFA and BHB (0.759, P= 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.This study investigated the genetic and recursive relationships between energy-related metabolites (i.e., glucose, urea, BHB, NEFA, cholesterol) and the daily milk energy output (dMEO). This information should be considered when developing breeding strategies aimed at improving the animals’ response to metabolic distress.Dairy cows face significant metabolic challenges during the different phases of the production cycle. One of their primary issues revolves around meeting the elevated energy demands, encompassing essential maintenance requirements and the energy required for milk production. In this context, monitoring of energy-related blood metabolites along lactation may be useful to detect metabolic disturbances. To date, no study is available on the investigation of putative recursive relationships among energy-related blood metabolites and milk daily energy output. The use of structural equation models presents an innovative approach to enhance our understanding of these complex relationships. This approach allows us to gain a better insight into the metabolic pathways and processes involved in energy metabolism. The findings of this study uncovered recursive relationships between energy-related blood metabolites and daily milk energy output. Grasping these interactions is crucial for developing effective breeding strategies focused on selecting more resistant and resilient cows, which have the capacity to better cope with metabolic distress along lactation.