Exploring the relationship between predicted negative energy balance and its biomarkers of Holstein cows in first-parity early lactation

The negative energy balance (NEB) state in dairy cows is a critical factor affecting health, reproduction, and production, particularly during early lactation. Multiple blood and milk biomarkers change when dairy cows are in the NEB state. Direct measurement of NEB is impractical for large-scale use due to costs, necessitating reliance on indirect predictors such as milk mid-infrared (MIR) spectrometry-based predicted biomarkers. However, the genetic relationships between NEB and its potential biomarkers remain unclear. This study aimed to (1) compare measured reference NEB with MIR-predicted NEB (PNEB), a novel energy deficit score (EDS), 15 biomarkers, and 3 production traits; (2) estimate genetic parameters among these traits using a 20-trait repeatability model, quantifying the ability of the 19 other studied traits (logit-transformed EDS (LEDS), 15 biomarkers, and 3 production traits) to genetically predict logit-transformed PNEB (LPNEB); and (3) evaluate the causal effects of LPNEB on the 19 traits through a recursive model. Two datasets were used: dataset I (127 cows, 965 records) provided reference data for objective (1), and dataset II (25,287 first-parity cows, 30,634 records) enabled genetic analysis used for objectives (2) and (3). Traits were analyzed using Pearson correlations, multiple-diagonalization expectation maximization REML–based genetic parameter estimation, and recursive modeling. The studied traits had moderate to moderate-high h² ranging from 0.16 to 0.38. The genetic correlations between LPNEB and the studied traits ranged from −0.60 for LIGF-1 to 0.85 for MIR-predicted blood nonesterified fatty acids (NEFA). Analysis of genetic predictability of LPNEB traits together explained 89% of the genetic variance of LPNEB, with all 15 biomarkers alone contributing the largest fraction with 82%, LEDS alone 65%, NEFA alone 62%, and all traits except LEDS 85%, indicating that LEDS contains useful additional information. Recursive modeling further identified 8 traits, including NEFA and LEDS, as highly dependent on LPNEB, highlighting their potential as robust biomarkers. This study demonstrates the utility of MIR-predicted traits for understanding the genetic mechanisms of NEB and its potential for integration into breeding programs, while emphasizing cautious interpretation of these results due to limitations of MIR-predictions of studied traits to represent directly measured traits.