Predicting Cholesterol, Progesterone, and Days to Ovulation Using Postpartum Metabolic and Endocrine Measures

Abstract

The objective of this study was to examine relationships among metabolic and endocrine factors that may influence ovarian activity during early lactation. Holstein cows (n=19) were bled twice each week to determine plasma concentrations of insulin (INS), glucose, cholesterol, insulin-like growth factor-1 (IGF-I), and progesterone (P4). Feed intake and milk production were recorded daily while body weights and milk composition were recorded weekly. Relationships among plasma cholesterol and P4, and days to first and second postpartum ovulation were modeled with energy balance (EB), dry matter intake, milk yield and composition, plasma metabolites, and hormones using the backward elimination technique of multivariate regression analysis. Variables that contributed the most to predicting plasma cholesterol concentrations were dry matter intake×SNF using model 1 (production variables) and the square of glucose (i.e., glucose2) using model 3 (plasma hormones and metabolites). For plasma P4 concentrations, EB (model 2, production variables) and IGF-I (model 3, plasma hormones and metabolites) were the major predictors. The production variables EB and percentage of milk lactose were the greatest contributors to the models predicting days to first and second postpartum ovulations, respectively. Of the plasma hormones and metabolites evaluated, IGF-I2 was the most significant predictor of days to first postpartum ovulation, whereas glucose2 and INS were the significant predictors of days to second postpartum ovulation. Plasma IGF-I, glucose, and INS have been implicated in ovarian functions and their significant contributions to these models are consistent with possible important roles in postpartum return to ovarian competence.