**5. Conclusions**

In this study we investigated the feasibility of using large and diverse datasets for untargeted 1H NMR serum metabolomic profiling of clinically healthy dairy cows in early lactation. In particular, we investigated the e ffects of systematic environmental factors on the serum metabolome. We used linear regression to correct spectra for (1) herd of origin; (2) parity; (3)WIM; and (4) herd, parity, andWIM simultaneously. Corrected and uncorrected spectra were then analyzed using PCA. Comparison of PCA results showed that herd of origin had a much greater impact on the serum metabolome than either parity or WIM. In order to simulate the impact of these e ffects in untargeted metabolomics, we used OPLS regression to quantify the relationship between both corrected and uncorrected NMR spectra, and the current gold-standard biomarker of energy balance in dairy cows, BHBA. Our results showed that (1) models constructed using uncorrected data from multiple farms provided reasonably robust predictions of serum BHBA concentration, (2) environmental e ffects can alter the results of biomarker discovery, and (3) that correcting spectra for environmental e ffects using linear regression may be useful when the aim of analysis is to investigate phenotypic variation free of confounding from environmental e ffects (e.g., identification of metabotypes for genetic selection).

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/5/180/s1, Table S1. 1H NMR chemical shifts (δ) and multiplicity of metabolites in bovine serum run in deuterated water (D2O).; Table S2. Results of ANOVA-simultaneous component analysis (ASCA) of uncorrected 1H NMR spectra of bovine serum.; Figure S1. Representative 700MHz 1H NMR spectrum (δ 0.4 to 9.0) of serum obtained from a Holstein–Friesian cow in early lactation.; Figure S2. Results of PCA of 707 1H NMR spectra of serum obtained from dairy cows in early lactation, corrected for weeks in milk using linear regression; (a) PC 1 vs. PC 2 scores, (b) PC 1 vs. PC 3 scores, (c) PC 2 vs. PC 3 scores, (d) PC 1 loadings, (e) PC 2 loadings, and (f) PC 3 loadings plots.; Figure S3. Results of PCA of 707 1H NMR spectra of serum obtained from dairy cows in early lactation, corrected for Parity using linear regression; (a) PC 1 vs. PC 2 scores, (b) PC 1 vs. PC 3 scores, (c) PC 2 vs. PC 3 scores, (d) PC 1 loadings, (e) PC 2 loadings, and (f) PC 3 loadings plots.; Figure S4. Results of PCA of 707 1H NMR spectra of serum obtained from dairy cows in early lactation, corrected for Herd using linear regression; (a) PC 1 vs. PC 2 scores, (b) PC 1 vs. PC 3 scores, (c) PC 2 vs. PC 3 scores, (d) PC 1 loadings, (e) PC 2 loadings, and (f) PC 3 loadings plots.; Figure S5. Average 1H NMR spectrum of bovine serum. Color-coding represents the percentage of variation in the signal at each chemical shift intensity that can be explained by (a) WIM and (b) Parity: Figure S6: Results of OPLS regressions of serum BHBA concentration against 1H NMR spectrum of bovine serum (n = 707): (a) LV1 vs. LV2 scores for uncorrected data (b) CV predicted vs. measured BHBA (c) LV1 vs. LV2 scores for corrected data (d) CV predicted vs. measured corrected BHBA ranking.

**Author Contributions:** Conceptualization, T.D.W.L., J.E.P., W.J.W., and S.J.R.; Formal analysis, T.D.W.L. and S.J.R.; Funding acquisition, J.E.P.; Investigation, T.D.W.L., A.C.E., and S.J.R.; Methodology, T.D.W.L., A.C.E., and S.J.R.; Project administration, J.E.P., W.J.W., and S.J.R.; Resources, S.J.R.; Supervision, J.E.P., W.J.W., and S.J.R.; Visualization, T.D.W.L.; Writing—original draft, T.D.W.L.; Writing—review and editing, J.E.P., A.C.E., W.J.W., and S.J.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** DairyBio: jointly funded by Dairy Australia (Melbourne, Australia), the Gardiner Foundation (Melbourne, Australia), and Agriculture Victoria (Melbourne, Australia) funded this study and T.D.W.L.'s PhD project.

**Acknowledgments:** The authors thank Simone Vassiliadis for assistance with 1H NMR protocol and metabolite identification, Di Mapleson, Brigid Ribaux and the staff at Ellinbank Dairy Research Centre (Ellinbank, Australia) for their technical expertise and assistance, Erika Oakes and the staff at Datagene (Bundoora, Australia) for their work coordinating this study, and the farmers who took part in this project.

**Conflicts of Interest:** The authors declare no conflict of interest.
