Evaluation of integrated biomarker panels for detection of subclinical physiological alterations in dairy goats: Biomarkers & subclinical physiological alterations in goats

Rezki Amalyadi

doi:10.62310/liab.v6i2.381

Authors

Rezki Amalyadi Faculty of Animal Science, University of Mataram, West Nusa Tenggara, 83115, Indonesia https://orcid.org/0009-0008-6595-3562

DOI:

https://doi.org/10.62310/liab.v6i2.381

Keywords:

Veterinary diagnostics, Subclinical disorders, Biomarkers, Dairy goats, Herd health

Abstract

Subclinical disorders in food-producing livestock remain one of the most important issues for veterinarians, since there is no clear clinical picture of the disease despite changes in physiology. The objective of this research was to assess the diagnostic significance of integrating of biochemical, inflammatory, and oxidative biomarkers to detect subclinical disorders in dairy goats under field conditions. Forty-eight lactating goats were grouped according to the results of clinical examination and blood test as healthy and subclinical. The concentrations of acylcarnitine, haptoglobin, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), malondialdehyde (MDA), and glutathione peroxidase (GPx) were determined in serum. Subclinical goats had higher concentrations of all biomarkers except GPx, which showed lower values (P<0.05). This indicates changes in metabolism, inflammation, and oxidative status at the subclinical stage of the disease. ROC curve analysis revealed moderate diagnostic ability for single biomarkers (AUC 0.79 – 0.86), however, the combination of biomarkers had high diagnostic ability (AUC = 0.93). Thus, the integrated use of biomarkers may serve as a supportive diagnostic approach for identifying subclinical physiological alterations in dairy goats. From a veterinary medicine perspective, this method may contribute to improved health monitoring and identification of animals with physiological disturbances under field conditions.

Metrics

Metrics Loading ...

References

Bales AM, Cinzori ME, Lock AL. (2024). Increasing palmitic acid and reducing stearic acid content of supplemental fatty acid blends improves production performance of mid-lactation dairy cows. Journal of Dairy Science 107(1): 278-287. https://doi.org/10.3168/jds.2023-23874

Becchi PP, Rocchetti G, Lucini L. (2025). Advancing dairy science through integrated analytical approaches based on multi-omics and machine learning. Current Opinion in Food Science 63: 101289. https://doi.org/10.1016/j.cofs.2025.101289

Caixeta LS, Omontese BO. (2021). Monitoring and improving the metabolic health of dairy cows during the transition period. Animals 11(2): 352. https://doi.org/10.3390/ani11020352

Chen L, Zhang C, Niu R, Xiong S, He J, Wang Y, Zhang P, Su F, Liu Z, Zhou L, Mao R, Hu S, Chen M, Qiu Y, Feng R. (2025). Multi‐omics biomarkers for predicting efficacy of biologic and small‐molecule therapies in adults with inflammatory bowel disease: a systematic review. United European Gastroenterology Journal 13(4): 517-530. https://doi.org/10.1002/ueg2.12720

Goldansaz SA, Guo AC, Sajed T, Steele MA, Plastow GS, Wishart DS. (2023). Livestock metabolomics and the livestock metabolome: A systematic review. PLoS One 18(2): e0177675. https://doi.org/10.1371/journal.pone.0177675

Idowu PA, Idowu AP. (2026). Omics and Precision Livestock Farming for Heat Stress Resilience: Integrating Molecular Biomarkers with Real-Time Phenotyping for Climate-Smart Livestock Systems. Frontiers in Animal Science 7: 1810017. https://doi.org/10.3389/fanim.2026.1810017

Jiang B, Tang W, Cui L, Deng X. (2023). Precision livestock farming research: A global scientometric review. Animals 13(13): 2096. https://doi.org/10.3390/ani13132096

Mace JL, Knight A. (2024). From the backyard to our beds: The spectrum of care, attitudes, relationship types, and welfare in non-commercial chicken care. Animals 14(2): 288. https://doi.org/10.3390/ani14020288

Moretti P, Paltrinieri S, Trevisi E, Probo M, Ferrari A, Minuti A, Giordano A. (2017). Reference intervals for hematological and biochemical parameters, acute phase proteins and markers of oxidation in Holstein dairy cows around 3 and 30 days after calving. Research in Veterinary Science 114: 322–331. https://doi.org/10.1016/J.RVSC.2017.06.012

Mule SN, Saad JS, Sauter IP, Fernandes LR, de Oliveira GS, Quina D, Tano FT, Brandt-Almeida D, Padron G, Stolf BS, Larsen MR, Cortez M, Palmisano G. (2024). The protein map of the protozoan parasite Leishmania (Leishmania) amazonensis, Leishmania (Viannia) braziliensis and Leishmania (Leishmania) infantum during growth phase transition and temperature stress. Journal of Proteomics 295: 105088. https://doi.org/10.1016/j.jprot.2024.105088

Ovseychik EA, Klein OI, Gessler NN, Deryabina YI, Lukashenko VS, Isakova EP. (2024). The efficacy of encapsulated phytase based on recombinant Yarrowia lipolytica on quails’ zootechnic features and phosphorus assimilation. Veterinary Sciences 11(2): 91. https://doi.org/10.3390/vetsci11020091

Rayego-Mateos S, Marquez-Exposito L, Basantes P, Tejedor-Santamaria L, Sanz AB, Nguyen TQ, Goldschmeding R, Ortiz A, Ruiz-Ortega M. (2023). CCN2 activates RIPK3, NLRP3 inflammasome, and NRF2/oxidative pathways linked to kidney inflammation. Antioxidants 12(8): 1541. https://doi.org/10.3390/antiox12081541

Schrödl W, Büchler R, Wendler S, Reinhold P, Muckova P, Reindl J, Rhode H. (2016). Acute phase proteins as promising biomarkers: Perspectives and limitations for human and veterinary medicine. Proteomics – Clinical Applications 10(11): 1077–1092. https://doi.org/10.1002/prca.201600028

Van Dixhoorn IDE, de Mol RM, Schnabel SK, van der Werf JTN, van Mourik S, Bolhuis JE, Rebel JMJ, van Reenen CG. (2023). Behavioral patterns as indicators of resilience after parturition in dairy cows. Journal of Dairy Science 106(9): 6444-6463. https://doi.org/10.3168/jds.2022-22891

Zwierzchowski G, Haxhiaj K, Wójcik R, Wishart DS, Ametaj BN. (2024). Identifying Predictive Biomarkers of Subclinical Mastitis in Dairy Cows through Urinary Metabotyping. Metabolites 14(4): 205. https://doi.org/10.3390/metabo14040205