Dyscalcemia is a subclinical phenotype commonly occurring in multiparous Holstein dairy cows. The condition is defined by low serum total calcium concentrations at 4 days postpartum and has been previously associated with increased risk of disease, decreased productivity, depressed intake, and reduced reproductive efficiency. Beyond the broad understanding that dyscalcemia is a useful indicator of cow-level risk, little is known about the underlying physiology of dyscalcemia or how this condition relates to other homeorhetic and homeostatic processes active during this critical postpartum timeframe. Furthermore, timely and cost-effective identification of this phenotype is largely non-existent. Therefore, the objectives of this dissertation were to: 1) Describe the physiology of dyscalcemia as it relates to milk constituent profiles predicted using Fourier-transform mid-infrared spectroscopic analysis, inflammatory phenotypes assessed through measurement of positive acute phase reactants, and feeding-related abnormalities including dry matter intake and rumination times; and 2) To investigate the potential utility of a diagnostic technique using milk constituent profiles and machine learning approaches for the identification of dyscalcemic cows. The primary objective was addressed in a tripartite approach. First, a prospective cohort study on a single farm was conducted to perform a classification analysis, supporting the hypothesis that dyscalcemia was associated with increased disease risk, and then to use this classification scheme to describe milk constituent profiles from 3 to 10 days postpartum. Second, an exploratory case-control study was conducted to establish if and by what metrics dyscalcemia was associated with early postpartum inflammation. Third, a small more intensive prospective cohort study was designed to establish the precise time sequence by which maladaptive physiological changes occur in association with dyscalcemia during the periparturient period. The secondary objective of this dissertation was evaluated using a cross-sectional design, whereby blood and milk of cows at 5 separate farms were sampled at 4 days postpartum, analyzed for calcium concentrations and milk constituent profiles, respectively, and used to train a machine learning algorithm for the prediction of dyscalcemia status. The combined findings of these objectives suggest that dyscalcemia is an important form of postpartum physiological maladaptation that can be readily diagnosed using non-invasive milk analysis techniques.