Relationships between liver blood markers and milk parameters in cows

[Relações entre marcadores sanguíneos do fígado e parâmetros do leite em vacas]

ABSTRACT

Monitoring through hematochemical liver measurements in high-yielding cows allows assessing herd well-being and health. Therefore, the present study aims to evaluate the relationships between liver blood markers and milk parameters in cows during the 1^st and 2^nd third of lactation. A total of 124 samples from Holstein cows (62 cows/group) belonging to nine intensive dairy farms and the same diet were used. Glucose, aspartate aminotransferase, albumin, alanine aminotransferase, milk fat, solids-non-fat, milk protein and cryoscopic point showed differences between groups. Total bilirubin, conjugated bilirubin, urea, cholesterol, γ-glutamyl-transferase, milk density, somatic cell count, and pH do not show differences between the 1^st and 2^nd third of lactation. Positive relationships between total bilirubin with conjugated bilirubin and cholesterol, solids-non-fat and milk protein, milk fat and glucose, solids-non-fat and albumin, milk protein and albumin, pH and urea, and somatic cell count with alanine aminotransferase were found. Negative relationship between alanine aminotransferase and γ-glutamyl-transferase, milk fat with solids-non-fat, milk protein, and somatic cell count, and milk fat with alanine aminotransferase were identified. The results may be useful for estimating adaptations in liver function, based on the behavior of liver blood markers when milk parameters are measured daily on intensive dairy farms.

Keywords:
liver blood markers; milk parameters; Holstein cows

RESUMO

O monitoramento por meio de medidas hematoquímicas do fígado em vacas de alta produção permite avaliar o bem-estar e a saúde do rebanho. Portanto, o presente estudo tem como objetivo avaliar as relações entre marcadores sanguíneos hepáticos e parâmetros do leite em vacas durante o 1º e o 2º terços da lactação. Um total de 124 amostras de vacas Holstein (62 vacas/grupo) pertencentes a nove fazendas leiteiras intensivas e submetidas à mesma dieta foram utilizadas. Glicose, aspartato aminotransferase, albumina, alanina aminotransferase, gordura do leite, sólidos não gordurosos, proteína do leite e ponto crioscópico apresentaram diferenças entre os grupos. Bilirrubina total, bilirrubina conjugada, ureia, colesterol, γ-glutamiltransferase, densidade do leite, contagem de células somáticas e pH não apresentaram diferenças entre o 1º e o 2º terços da lactação. Foram encontradas relações positivas entre bilirrubina total com bilirrubina conjugada e colesterol, sólidos não gordurosos e proteína do leite, gordura do leite e glicose, sólidos não gordurosos e albumina, proteína do leite e albumina, pH e ureia, e contagem de células somáticas com alanina aminotransferase. Foram identificadas relações negativas entre alanina aminotransferase e γ-glutamiltransferase, gordura do leite com sólidos não gordurosos, proteína do leite e contagem de células somáticas, e gordura do leite com alanina aminotransferase. Os resultados podem ser úteis para estimar adaptações na função hepática, com base no comportamento dos marcadores sanguíneos do fígado, quando os parâmetros do leite são medidos diariamente em fazendas leiteiras intensivas.

Palavras-chave:
marcadores sanguíneos do fígado; parâmetros do leite; vacas Holstein

INTRODUCTION

The liver carries out different functions that can be classified into the following categories. Metabolic: glycogenesis, glycogenolysis, gluconeogenesis, transamination, deamination, ureogenesis, lipogenesis, β-oxidation of fatty acids, ketogenesis, and synthesis of lipoproteins and phospholipids (Wang et al., 2019; Habel and Sundrum, 2020; Lopreiato et al., 2020; McFadden, 2020; Zhang et al., 2020; Huang et al., 2022). Vascular: blood storage, and lymph synthesis (Gross and Bruckmaier, 2019a; Mezzetti et al., 2020). Secretory bilirubin, bile acids and electrolytes (Gross and Bruckmaier, 2019b; Allen, 2020; Zachut et al., 2020). Excretory: steroid hormones, calcium ion (Ca ²⁺), antibiotics, and drug metabolites (Macrae et al., 2019; Paiano et al., 2019; Haussler et al., 2022). The analysis of glucose (GLU), urea, aspartate aminotransferase (AST), albumin (ALB), alanine aminotransferase (ALT), total bilirubin (TB), conjugated bilirubin (CB), cholesterol (CHOL) and γ-glutamyl-transferase (γ-GT), forms a general assessment of liver blood markers (Paiano et al., 2020) and provides information on cow productivity (Pascottini et al., 2022). Also, milk production and quality results from internal metabolic balance (McFadden, 2020). Therefore, they can also quantify different milk parameters such as milk fat, solids-non-fat (SNF), milk density, milk protein, cryoscopic point (CRP), pH, and somatic cell count (SCC) (Stocco et al., 2020; France et al., 2022). Clinical and laboratory monitoring in dairy cows generally involves the 1^st and 2^nd third of lactation, especially for the detection of subclinical forms of ketosis and ruminal acidosis (García et al., 2020; Pinedo and Melendez, 2022). Because the risk of such pathologies during the 3^rd third of lactation is very low, blood samples are rarely monitored at this time (Gross and Bruckmaier, 2019a; Yang et al., 2019). Therefore, the present study aims to evaluate the relationships between liver blood markers and milk parameters in cows during the 1^st and 2^nd third of lactation.

MATERIALS AND METHODS

In multiparous cows, the period of peak milk production is usually between 30 to 60 d p.p., when the uterus is involuting and the ovary is returning to estrous cyclicity (Davidson and Stabenfeldt, 2014; Fails and Magee, 2018). The peak milk production generally begins to descend until 12 to 14 weeks p.p. (Nutrient…, 2001). Therefore, sampling was done according to the methodology proposed and described by Payne (1972) in the Compton metabolic profile. A total of 126 samples from Holstein cows belonging to nine intensive dairy farms and the same diet were used. The 14 clinically healthy cows/each dairy farm (confirmed by blood count) were selected. Seven cows the 1^st third of lactation [Days in milk (DIM): 42 ± 15d p.p.; Milk Production (Mean ± SD): 35.36 ± 1.49kg·d⁻¹], and seven cows the 2^nd third of lactation [DIM: 91 ± 17 d p.p.; Milk Production (Mean ± SD): 16.34 ± 1.23kg·d⁻¹].

The nine dairy farms were located in the Tizayuca-Hidalgo Agricultural and Livestock Industrial Complex, at an altitude of 2,260m above sea level, with a subhumid climate (Köppen Cfb) (Peel et al., 2007). The average temperature is 15°C, and pluvial precipitation is 620 mm/year.

In all intensive dairy farms, the cows were fed a total mixed ration (TMR). The feeding of the 1^st third of the lactation diet included: 13.25kg/d of concentrate with 17.80% CP, 14.06kg/d of corn silage, 7.14kg/d of alfalfa hay, 3.38kg/d of triticale silage, 1.32kg/d of alfalfa silage, and 1kg/d of oat straw. The feeding of the 2^nd third of the lactation diet included: 7kg/d of concentrate with 14.60% CP, 14.3kg/d of chopped alfalfa, 4kg/d of alfalfa hay, 2.5kg/d of corn flakes, and 1.5kg/d of oat straw. Additionally, a mineral supplement was offered with magnesium oxide (MgO), ferrous sulfate (FeSO ₄), calcium carbonate (CaCO ₃), manganese oxide (MnO ₂), copper sulfate (CuSO ₄), tricalcium phosphate [Ca ₃(PO ₄)₂], zinc oxide (ZnO), cobalt chloride (CoCl ₂), sodium chloride (NaCl), potassium iodide (KI) and sodium selenite (Na ₂ SeO ₃). All cows received fresh water with free access.

Blood samples were collected after the first-morning milking and before feeding, by puncture of the coccygeal vein using 8.5 mL vacuum tubes with a coagulation activator and separating gel (BD Vacutainer 367988; Becton-Dickinson Co., USA). The serum was separated by centrifugation at 3500 × g for 16 min with a portable centrifuge (Porta-Spin C828; UNICO., USA). The serum was stored in 1.5mL tubes with a cap (Tubes Safe-Lock 3810X; Eppendorf., Spain) and frozen at -20°C until analysis at the Faculty of Chemical Sciences of the University of Colima. Sampling for milk (80 mL) was collected in sterile urinalysis cups (VAR-KS-409726-125; VIRESA., Mexico), following the provisions established in the official standard (Sistema…, 2017). All samples were stored at 4°C in a portable refrigerator (Thermoelectric Cooler Car/Home M5644-710; The Coleman Company, USA) and were analyzed directly in the dairy farms.

The biochemical profile was measured with a UV-Vis double-beam spectrophotometer (Spin 120; Spinreact, Girona, Spain) and the procedures indicated in Table 1.

Table 1
Biochemical analytes, units, analytical methods, and corresponding commercial reagents

Analyte*	Unit	Method	Reagent&
Total bilirubin and conjugated (TB and CB)	μM	DMSO. Colorimetric1	1001044
Glucose (GLU)	mM	Trinder. GOD-POD2	1001190
Urea	mM	Urease-GLDH. Kinetic UV3	1001333
Aspartate aminotransferase (AST)	U/L	NADH. Kinetic UV. IFCC rec. Liquid4	MI41264
Albumin (ALB)	g/L	Colorimetric5	1001020
Alanine aminotransferase (ALT)	U/L	NADH. Kinetic UV. IFCC rec. Liquid6	41282
Cholesterol (CHOL)	mM	CHOD-POD. Liquid7	41020
γ-glutamyl transferase (γ-GT)	U/L	Carboxy substrate. Kinetic. Liquid8	41292

• *Official abbreviation of the International Union of Pure and Applied Chemistry; ¹Bilirubin-azobilirubin by diazotized sulfanilic acid; ²Glucose oxidase (GOD)-Peroxidase (POD); ³Urease- α-Ketoglutarate-Glutamate dehydrogenase (GLDH); ⁴α-ketoglutarate-glutamate and oxalacetate-malate dehydrogenase (MDH) and NADH; ⁵Bromocresol green; ⁶α-ketoglutarate-glutamate and piruvate-lactate dehydrogenase (LDH) and NADH; ⁷Cholesterol esterase (CHE)-Cholesterol oxidase (CHOD)-Peroxidase (POD); ⁸L-γ-Glutamyl-3-carboxy-4-nitroanilide-Glycylglycine; ^&Spinreact., Girona, Spain.

The precision and reliability of the techniques were controlled using lyophilized control serum (SPINTROL NORMAL 1002100; Spinreact., Girona, Spain). Milk pH was measured with a (HI981032 pH meter; HANNA instruments., Mexico). The milk fat, SNF, milk density, milk protein and CRP were determined with ultrasonography (EKOMILK ULTRA Milkana KAM98-2A; milk analyzer., Bulgaria), which uses low power ultrasound (≥100 KHz) and low intensity (≤1W/cm²). Ultrasound propagated through the milk as mechanical waves causing alternating compressions and decompressions (Soltani et al., 2019). These waves interacted with the biochemical bonds present in milk fat and milk protein, stimulating their vibration and quantification (Bhargava et al., 2021). The SCC was performed with infrared spectrophotometry (Ekomilk Scan; Ekomilk., Bulgaria). The latter dissolved the cell and nuclear membranes, allowing their contents to escape and forming a gel that increased the viscosity of the milk sample. The solution was then passed through the mixing capillary, and the flow time of the milk sample was recorded, which is proportional to the somatic cells/mL of milk. According to Piccinini et al. (2004) and Zecconi et al. (2019), the infection risk scale will be categorized as follows:

• Up to 100 × 1000 cells/mL: healthy, normal milk

• From 100 to 200 × 1000 cells/mL: suspected, upper physiological level, and

• More than 200 × 1000 cells/mL: mastitis, abnormal milk.

The statistical procedure used to calculate the 95% confidence intervals for the different liver analytes was determined by (PROC TTEST; SAS, System, v. 8.2, Cary, NC). The recommendations of the International Federation of Clinical Chemistry were followed. The first step was the detection of outliers for each analyte and values more than three standard deviations from the mean were discarded. To measure the distribution and behavior of the different values within the sample, the resulting data set was analyzed using the Kolmogorov-Smirnov test. The percentiles: P₁₀-P₉₀ and P₂₅-P₇₅ were determined by PROC UNIVARIATE NORMAL PLOT (SAS, 2001). The comparison between groups (1^st third of lactation vs. 2^nd third of lactation) was assessed by PROC ANOVA (SAS, 2001), together with the Tukey and cldiff options for a Tukey post-hoc test. The relationships between liver blood markers, between milk parameters, and between liver blood markers with milk parameters are identified by PROC CORR (SAS, 2001). Regression Analysis PROC REG (SAS, 2001), whose statistical significance is presented by Pearson's correlation coefficient and a value of (p<0.05), was used. The following model was tested:

$Y={\beta }_0+{\beta }_1\cdot X_1+e_1$

where:

$Y$ = milk fat, SNF, milk density, milk protein, pH, and SCC,

$X_1\mathrm{,...,}{X}_{\text{k}}$ = TB, CB, GLU, Urea, AST, ALB, ALT, CHOL, and γ-GT,

${\beta }_1\mathrm{,...,}{\beta }_{\text{k}}$ = partial regression coefficient for each X ₁,

${\beta }_0$ = intercept on the Y axis, and

$e_1$ = error of the estimate.

The analysis of the Outlier values was executed with a macro that uses the robust Mahalanobis distance, setting an importance to the data whose value has a probability of being (chi²) < p. Regression diagnosis for main assumptions was. Kolmogorov-Smirnov test was used to check the normality of residuals. Homoscedasticity was checked by plotting residual versus predicted values, and the Durbin-Watson test was employed to check for error uncorrelation.

ETHICAL ASPECTS

All procedures used were approved by the Bioethics and Animal Welfare Committee of the Faculty of Veterinary Medicine and Animal Husbandry of the University of Colima (Assessment record: No. 002/2023).

RESULTS

Two observations with outliers were discarded. Descriptive statistics for the traits investigated are reported in Table 2.

Table 2
Descriptive statistics of single test-day, for different liver blood markers and milk parameters in intensive dairy farms, n = 124 samples

	x ± DE			Referencea			CIb			P10-P90			P25-P75			KSc
Liver blood markers
TB, μM	6.63	±	0.70	0.17	-	8.55	6.51	-	6.76	5.71	-	7.56	6.15	-	7.11	>0.15
CB, μM	5.56	±	0.52	0.68	-	7.52	5.47	-	5.66	4.94	-	6.26	5.24	-	5.88	>0.15
GLU, mM	3.84	±	0.33	2.50	-	4.16	3.78	-	3.90	3.45	-	4.21	3.61	-	4.07	>0.15
Urea, mM	6.37	±	0.83	7.14	-	10.70	6.22	-	6.52	5.37	-	7.59	5.83	-	6.95	0.07
AST, U/L	83.34	±	21.33	78	-	132	79.55	-	87.14	56.51	-	112.1	71.67	-	97.81	0.10
ALB, g/L	33.01	±	2.39	30.30	-	35.50	32.58	-	33.43	29.87	-	36.00	31.37	-	34.68	>0.15
ALT, U/L	22.04	±	4.59	11	-	40	21.22	-	22.85	16.12	-	27.62	18.81	-	26.00	>0.15
CHOL, mM	3.52	±	1.03	2.07	-	3.50	3.34	-	3.71	2.16	-	4.85	2.96	-	4.13	0.08
γ-GT, U/L	20.97	±	3.65	6.10	-	19.40	20.32	-	21.62	16.25	-	25.88	18.37	-	23.87	>0.15
Milk parameters
Milk fat, %	3.94	±	0.69	3.70	-	4.10	3.82	-	4.06	3.00	-	4.94	3.48	-	4.35	0.09
SNF, %	8.27	±	0.51	8.10	-	8.80	8.17	-	8.36	7.62	-	9.03	7.95	-	8.55	>0.15
Milk density, g/mL	1.01	±	0.01	1.01	-	1.04	1.00	-	1.01	0.99	-	1.03	1.00	-	1.02	>0.15
Milk protein, %	3.13	±	0.20	3.10	-	3.50	3.09	-	3.13	2.86	-	3.44	2.99	-	3.25	>0.15
SCC, x 1000/mL	100.55	±	9.83	100	-	120	98.80	-	102.3	86.40	-	114.6	92.98	-	107.4	0.06
pH	6.55	±	0.41	6.40	-	6.80	6.47	-	6.62	6.05	-	7.16	6.25	-	6.86	0.06
CRP, Horvert ºH	-0.55	±	0.01	-0.50	-	-0.59	-0.55	-	-0.54	-0.57	-	-0.53	-0.56	-	-0.54	<0.01

• TB: total bilirubin, CB: conjugated bilirubin, GLU: glucose, AST: aspartate aminotransferase, ALB: albumin, ALT: alanine aminotransferase, CHOL: cholesterol, γ-GT: γ-glutamyl-transferase, SNF: solids-non-fat, SCC: somatic cell count; pH: hydrogen potential; CRP: cryoscopic point; ^a(Kaneko et al., 2008); ^b95% confidence interval; ^cKolmogorov-Smirnov normality test, p≥0.05.

The comparison between groups (1^st third of lactation vs. 2^nd third of lactation) showed differences in GLU, AST, ALB, ALT, milk fat, SNF, milk protein and CRP (Table 3).

Table 3
Tukey's studentized range test, for different liver blood markers and milk parameters in intensive dairy farms, n = 62 Holstein cows/group

	Comparison by group			Difference between means	Confidence limits		Different group means
Liver blood markers
TB, μM	1	-	2	0.04	-0.21	0.29
	2	-	1	-0.04	-0.29	0.21
CB, μM	1	-	2	0.00	-0.18	0.19
	2	-	1	-0.00	-0.19	0.18
GLU, mM	1	-	2	0.15	0.04	0.27	***
	2	-	1	-0.15	-0.27	-0.04	***
Urea, mM	1	-	2	0.02	-0.27	0.31
	2	-	1	-0.02	-0.31	0.27
AST, U/L	1	-	2	10.03	2.63	17.43	***
	2	-	1	-10.03	-17.43	-2.63	***
ALB, g/L	1	-	2	2.43	1.69	3.17	***
	2	-	1	-2.43	-3.17	-1.69	***
ALT, U/L	1	-	2	2.16	0.57	3.76	***
	2	-	1	-2.16	-3.76	-0.57	***
CHOL, mM	1	-	2	0.11	-0.25	0.48
	2	-	1	-0.11	-0.48	0.25
γ-GT, U/L	1	-	2	0.26	-1.03	1.56
	2	-	1	-0.26	-1.56	1.03
Milk parameters
Milk fat, %	1	-	2	0.33	0.09	0.56	***
	2	-	1	-0.33	-0.56	-0.09	***
SNF, %	1	-	2	0.45	0.29	0.62	***
	2	-	1	-0.45	-0.62	-0.29	***
Milk density, g/mL	1	-	2	0.00	-0.00	0.00
	2	-	1	-0.00	-0.00	0.00
Milk protein, %	1	-	2	0.22	0.16	0.28	***
	2	-	1	-0.22	-0.28	-0.16	***
SCC, x 1000/mL	1	-	2	1.44	-2.05	4.94
	2	-	1	-1.44	-4.94	2.05
pH	1	-	2	0.12	-0.02	0.26
	2	-	1	-0.12	-0.26	0.02
CRP, Horvert ºH	1	-	2	0.00	0.00	0.01	***
	2	-	1	-0.00	-0.01	-0.00	***

• TB: total bilirubin, CB: conjugated bilirubin, GLU: glucose, AST: aspartate aminotransferase, ALB: albumin, ALT: alanine aminotransferase, CHOL: cholesterol, γ-GT: γ-glutamyl-transferase, SNF: solids-non-fat, SCC: somatic cell count; pH: hydrogen potential; CRP: cryoscopic point; ¹1^st third of lactation [DIM: 42 ± 15 d p.p.; Milk Production (Mean ± SD): 35.36 ± 1.49 kg·d⁻¹], ²2^nd third of lactation [DIM: 91 ± 17 d p.p.; Milk Production (Mean ± SD): 16.34 ± 1.23 kg·d⁻¹]; Significant comparisons at the 0.05 level are indicated by ***

The interaction between liver blood markers is reported in Table 4. Positive relationships (p<0.05) between TB with CB and CHOL were calculated. A negative relationship (p<0.05) between ALT and γ-GT was identified.

Table 4
Pearson correlation coefficient between liver blood markers, n = 124 Holstein cows

	CB	GLU	Urea	AST	ALB	ALT	CHOL	γ-GT
TB	0.86*	0.27	0.38	0.17	0.09	0.10	0.94*	-0.06
	CB	0.46	0.60	0.25	-0.01	-0.03	0.66	0.04
		GLU	0.19	0.31	-0.47	-0.63	0.13	0.48
			Urea	0.17	-0.01	-0.01	0.24	0.00
				AST	-0.38	-0.16	0.13	-0.04
					ALB	0.61	0.11	-0.11
						ALT	0.15	-0.85*
							CHOL	-0.12
								γ-GT

• TB: total bilirubin, CB: conjugated bilirubin, GLU: glucose, AST: aspartate aminotransferase, ALB: albumin, ALT: alanine aminotransferase, CHOL: cholesterol, γ-GT: γ-glutamyl-transferase; *p<0.05.

According to the Kolmogorov-Smirnov normality test the CRP is not normally distributed (Table 2). Therefore, it was excluded from the relationship models. The interaction between milk parameters is reported in Table 5. A positive relationship (p<0.05) between SNF and milk protein was identified. Negative relationships (p<0.05) between milk fat with SNF, milk protein, and SCC were quantified.

Table 5
Pearson correlation coefficient between milk parameters, n = 124 Holstein cows

	SNF	Milk density	Milk protein	pH	SCC
Milk fat	-0.81*	0.05	-0.76*	-0.06	-0.82*
	SNF	-0.12	0.98*	0.11	0.71*
		Milk density	-0.12	-0.08	-0.02
			Milk protein	0.13	0.65
				pH	0.04
					SCC

• SNF: non-fat solids, pH: hydrogen potential, SCC: somatic cell count; *p<0.05.

The interaction between liver blood markers and milk parameters is reported in Table 6. Positive relationships (p<0.05) between milk fat and GLU; SNG and ALB; milk protein and ALB; pH and urea; and SCC and ALT were calculated. A negative relationship (p<0.05) between milk fat and ALT was identified.

Table 6
Pearson correlation coefficient between liver blood markers and milk parameters, n = 124 Holstein cows

	TB	CB	GLU	Urea	AST	ALB	ALT	CHOL	γ-GT
Milk fat	0.19	0.32	0.78*	0.12	0.36	-0.62	-0.82*	0.11	0.62
SNF	-0.12	-0.24	-0.66	-0.12	-0.40	0.76*	0.67	-0.06	-0.34
Milk density	0.04	-0.01	0.01	-0.05	0.06	0.08	-0.02	0.08	-0.09
Milk protein	-0.11	-0.22	-0.63	-0.11	-0.41	0.77*	0.64	-0.06	-0.29
pH	0.35	0.55	0.04	0.97*	0.07	0.17	0.14	0.22	-0.06
SCC	-0.20	-0.27	-0.65	-0.11	-0.27	0.52	0.71*	-0.16	-0.54

• TB: total bilirubin, CB: conjugated bilirubin, GLU: glucose, AST: aspartate aminotransferase, ALB: albumin, ALT: alanine aminotransferase, CHOL: cholesterol, γ-GT: γ-glutamyl-transferase, SNF: solids-non-fat, pH: hydrogen potential, SCC: somatic cell count; *p<0.05.

DISCUSSION

The concentrations of blood parameters measured in the present study (Table 2) were within the reference ranges reported for dairy cows without clinical signs of disease (Kaneko et al., 2008). As expected, there were differences in milk parameters between 1^st third of lactation and 2^nd third of lactation groups. SNF and milk protein were higher in 1^st third of the lactation. However, milk fat was lower in 1^st third of lactation (Table 3). The GLU concentration was not significantly correlated with other investigated liver blood markers. This may be due to the high compensation of the gluconeogenesis process by the regulation of glucagon and insulin (Larsen and Kristensen, 2013). In dairy cows, hepatic gluconeogenesis fulfills more than 80% of the GLU requirement (Aschenbach et al., 2010). The main substrate for gluconeogenesis in the liver is propionate, which provides more than 60% of the carbon source (Larsen and Kristensen, 2013). When the demand for GLU increases, ruminant liver can also utilize amino acids, lactate, and glycerol for gluconeogenesis, providing 11 to 16% of the carbon source (Andjelic et al., 2022). In our study, the GLU level was lower in the 1^st third of lactation compared to the 2^nd third of lactation. The high-yielding cows require large amounts of GLU as a substrate and as a source of energy for lactose synthesis (Larsen and Kristensen, 2013). Therefore, the GLU levels observed in 1^st third of lactation may be associated with an increased lactose synthesis (Azzout et al., 2007). About 80% of lactose is produced from GLU, and the remaining 20% is produced from acetate (Wang et al., 2024). Also, lactose is the major osmoregulator of mammary water uptake and, consequently, determines milk volume (Aschenbach et al., 2010). Also, GLU was positively related to milk fat (Table 6, Fig. 1). This subordinate behavior can be explained by the fact that milk fat is composed of glycerol and fatty acids (Andjelic et al., 2022). Glycerol is formed during glycolysis, while fatty acids are formed during ruminal fermentation of plant fibers (Aschenbach et al., 2010).

Figure 1
Relationship between glucose and milk fat, n = 124 Holstein cows. Milk fat (•); predicted response (-).

Hemoglobin is a protein that is present in red blood cells (Guerra et al., 2021). In its heme group, transported oxygen, to interchange in the lungs with carbon dioxide (Andjelic et al., 2022). This interchange caused the rupture of the heme group's bonds and its transformation into biliverdin, which is converted into unconjugated or indirect bilirubin (UCB) (Katica et al., 2024). The UCB is water-insoluble and enters circulation bound to ALB (Andjelic et al., 2022). In the hepatocyte, glucuronic acid is added to UCB (conjugation) to render it water-soluble (CB) (Guerra et al., 2021). The TB is the sum of CB and UCB (Kaneko et al., 2008). This dependence would explain the quantified relationship between TB and CB (Fig. 2).

Figure 2
Relationship between conjugated bilirubin and total bilirubin, n = 124 Holstein cows. Total bilirubin (•); predicted response (-).

The CB is excreted into bile or recirculated back to the bloodstream, where it is filtrated by the kidneys and excreted through urine (Katica et al., 2024). All liver lesions induce a decrease in the hepatocyte cell count, which may cause hyperbilirubinemia (Bruckmaier and Gross, 2017), a condition not observed in this study (Table 2). BT and CB a well-established blood markers that are routinely included in tests for cows with liver dysfunction (García et al., 2020). However, BT and CB are not a specific marker of liver function, so quantification of other blood markers is also necessary, e.g. AST, ALT, and γ-GT (Pinedo and Melendez, 2022). In comparison with other stages of lactation, during the 1^st third, cows experience a period of high energy requirements, associated with insufficient feed intake (Pinedo and Melendez, 2022). To compensate for this nutrient deficiency, the cow mobilizes its body's fat and protein reserves (Haussler et al., 2022). This situation causes a Negative Energy Balance (NEB) (Han et al., 2012), which alters the enzymatic activity of AST, ALT, and γ-GT (Table 3). When the liver is diseased or damaged, additional AST and ALT are released into the bloodstream (Bruckmaier and Gross, 2017). Therefore, the amount of AST and ALT in the blood is directly related to the extent of the tissue damage (Han et al., 2012). However, the enzyme values ​​of AST and ALT recorded in our study (Table 2) did not exceed the international reports for healthy cows. Therefore, the liver functions of cows were not disturbed (Han et al., 2012). It may be that hepatic gluconeogenesis is minimizing lipid metabolism disorders in BEN, such as fatty liver and ketosis (Aschenbach et al., 2010; Andjelic et al., 2022). In contrast to AST, ALT is a cytoplasmic enzyme and in cattle, is involved in the metabolism of proteins, catalyzing a reversible reaction of α-alanine synthesis (Han et al., 2012). The γ-GT is an enzyme that transfers a γ-glutamyl group present in glutamic acid, a key amino acid in the cellular metabolism of proteins (Paiano et al., 2020). An elevated serum γ-GT appears to be a sensitive specific indicator of liver damage, impaired biliary secretion, intense protein translation, or very high gluconeogenesis, making it a useful diagnostic aid (Wang et al., 2019; McFadden, 2020; Zhang et al., 2020). The enzyme values ​​of γ-GT recorded in our study (Table 2) did not exceed the international reports for healthy cows. Therefore, the liver functions of cows were not disturbed. It should also be noted that γ-GT shows greater specificity in cattle liver than AST and ALT (McFadden, 2020).

The recorded activity of the ALT parameter was negatively correlated with the activity of γ-GT (Table 4). Such a negative correlation can often be observed in cows with high milk production, especially during the increase in mitochondrial activity (Han et al., 2012). The ALB is positively related to milk protein and SNF (Figures 3 and 4). This subordination is because ALB serves as a carrier for molecules with low water content, including SNF, fat-soluble hormones, and free fatty acids. Also, the serum ALB concentration can be expressed in terms of milk protein content because it is a common component of body fluids, both in the plasma and milk of cows (Lieske et al., 2005).

Figure 3
Relationship between albumin and milk protein, n = 124 Holstein cows. Albumin (•); predicted response (-).

Figure 4
Relationship between albumin and non-fat solids, n = 124 Holstein cows. Albumin (•); predicted response (-).

In ruminants, the ammonia reaching the liver has two origins: exogenous, absorbed through the rumen’s walls, and endogenous, originating from the amino acid of the animal organism (Jin et al., 2018). Ammonia is ​​absorbed by the ruminal wall and reaches the periportal hepatocytes, which contain ureagenic enzymes for Urea synthesis. In mammals, Urea represents the molecule used for excretion of ammonia (Jin et al., 2018). Its concentration in blood is highly affected by dietary factors, especially the amount of protein ingested and the protein/energy ratio (Weiner et al., 2015). The Urea is positively related to pH (Figure 5). This subordination is due to the hydrolysis of Urea, catalyzed by the enzyme urease, which produces ammonia, which reacts with water to form ammonium hydroxide, thus raising the pH (Weiner et al., 2015).

Figure 5
Relationship between urea and pH, n = 124 Holstein cows. Urea (•); predicted response (-).

The negative relationship between milk fat and SNF (Table 5), is obvious since SNF does not include fats. The positive correlation (p<0.05) between milk protein and SNF recorded in our study is expected, as milk proteins make a significant contribution to this mass (Macrae et al., 2019). Finally, the negative relationship between SCC and milk fat (Table 5) is consistent with the results of other authors (Pedraza et al., 2000). Clinical and subclinical mastitis increase the milk SCC (Summer et al., 2015). This marker is routinely used at the herd level as an indicator of udder health (Bisutti et al., 2022). The SCC includes leukocytes and epithelial cells (Summer et al., 2015). The leucocyte populations reflect the intramammary infection severity (Bobbo et al., 2016). In healthy udders, lymphocytes and macrophages prevail (Pegolo et al., 2021). However, the fairly low SCC in milk (Table 2), indicated quite good mammary gland health status (Summer et al., 2015).

CONCLUSIONS

The present study evaluated the relationships between liver blood markers and milk parameters in cows during the 1^st and 2^nd third of lactation. Glucose, aspartate aminotransferase, albumin, alanine aminotransferase, milk fat, solids-non-fat, milk protein and cryoscopic point showed differences between groups. Total bilirubin, conjugated bilirubin, urea, cholesterol, γ-glutamyl-transferase, milk density, somatic cell count, and pH do not show differences between the 1^st and 2^nd third of lactation. Positive relationships between total bilirubin with conjugated bilirubin and cholesterol, solids-non-fat and milk protein, milk fat and glucose, solids-non-fat and albumin, milk protein and albumin, pH and urea, and somatic cell count with alanine aminotransferase were found. The negative relationship between alanine aminotransferase and γ-glutamyl-transferase, milk fat with non-fat solids, milk protein, and somatic cell count, and milk fat with alanine aminotransferase were identified. Because the liver has a variety of biochemical, several liver function tests are used in the diagnostics and management of liver diseases. Determination of liver blood markers and milk parameters can provide valuable information regarding dairy cow’s nutrition and physiological status.

ACKNOWLEDGMENTS

We are very grateful to the Tizayuca-Hidalgo Agricultural and Livestock Industrial Complex.

REFERENCES

• ALLEN, M.S. Review: control of feed intake by hepatic oxidation in ruminant animals: integration of homeostasis and homeorhesis. Animal,. v.14, p.s55-s64, 2020.
• ANDJELIC, B.; DJOKOVIĆ, R.; CINCOVIĆ, M. et al. Relationships between milk and blood biochemical parameters and metabolic status in dairy cows during lactation. Metabolites, v.12, p.1-14, 2022.
• ASCHENBACH, J.R.; KRISTENSEN, N.B.; DONKIN, S.S. et al. Gluconeogenesis in dairy cows: the secret of making sweet milk from sour dough. IUBMB Life, v.62, p.869-877, 2010.
• AZZOUT, M.D.; GAUDICHON, C.; BLOUET, C. et al. Liver glyconeogenesis: a pathway to cope with postprandial amino acid excess in high-protein fed rats? Am. J. Physiol. Regul. Integr. Comp. Physiol., v.292, p,R1400-1407, 2007.
• BHARGAVA, N.; MOR, R.S.; KUMAR, K. et al. Advances in application of ultrasound in food processing: a review. Ultrason Sonochem., v.70, p.105293, 2021.
• BISUTTI, V.; VANZIN, A.; TOSCANO, A. et al. Impact of somatic cell count combined with differential somatic cell count on milk protein fractions in Holstein cattle. J. Dairy Sci., v.105, p.6447-6459, 2022.
• BOBBO, T.; CIPOLAT-GOTET, C.; BITTANTE, G. et al. The nonlinear effect of somatic cell count on milk composition, coagulation properties, curd firmness modeling, cheese yield, and curd nutrient recovery. J. Dairy Sci., v.99, p.5104-5119, 2016.
• BRUCKMAIER, R.M.; GROSS, J.J. Lactational challenges in transition dairy cows. Anim. Prod. Sci., v.57, p.1471-1481, 2017.
• DAVIDSON, P.A.; STABENFELDT, G.H. Reproducción y lactancia. Gestación y parto. In: KLEIN, G.B.; LORENZO, G.P.L. (Eds.). Cunningham, fisiología veterinaria. Barcelona, España: Elsevier Inc, 2014. p.431-438.
• FAILS, D.A.; MAGEE, C. The ovary and estrous cycles. In: FAILS, D.A. ; MAGEE, C., (Eds.). Anatomy and physiology of farm animals. Hoboken, United States: John Wiley & Sons, 2018. p.475-487.
• FRANCE, A.E.; DUFOUR, S.; KELTON, D.F. et al. Effect of dry-off management on milking behavior, milk yield, and somatic cell count of dairy cows milked in automated milking systems. J. Dairy Sci., v.105, p.3544-3558, 2022.
• GARCÍA, C.A.C.; PRADO, R.O.F.; HORTENSIA, P.D. Fisiología del período de transición, posparto y retorno al estro en vacas lecheras: desafíos para la producción sustentable. IN: GUTIÉRREZ, N.N.S.; GUTIÉRREZ, V.M.C.; RAMÍREZ, G.M.J., (Eds.). Handbook T-2 sustentabilidad, turismo y educación. México: ECORFAN-México, S.C., 2020. p.63-86.
• GROSS, J.J.; BRUCKMAIER, R.M. Invited review: metabolic challenges and adaptation during different functional stages of the mammary gland in dairy cows: Perspectives for sustainable milk production. J. Dairy Sci., v.102, p.2828-2843, 2019a.
• GROSS, J.J.; BRUCKMAIER, R.M. Review: metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk. Animal, v.13. p.s75-s81, 2019b.
• GUERRA, R.A.R.; CRESPO, J.; MARTÍNEZ, R.M.L. et al. Measurement and clinical usefulness of bilirubin in liver disease. Adv. Lab. Med., v.2, p.352-372, 2021.
• HABEL, J.; SUNDRUM, A. Mismatch of glucose allocation between different life functions in the transition period of dairy cows. Animals, v.10, p.1-14, 2020.
• HAN, Y.; LIU, P.; HE, B. et al. Activities of aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transferase, alkaline phosphatase in plasma of postpartum Holstein cows. J. Anim. Vet. Adv., v.11, p.1270-1274, 2012.
• HAUSSLER, S.; SADRI, H.; GHAFFARI, M.H. et al. Symposium review: Adipose tissue endocrinology in the periparturient period of dairy cows. J. Dairy Sci., v.105, p.3648-3669, 2022.
• HUANG, Y.; ZHANG, C.; YANLI WANG, Y. et al. Identification and analysis of miRNAs in the normal and fatty liver from the Holstein dairy cow. Anim. Biotechnol., v.33, p.468-479, 2022.
• JIN, D.; ZHAO, S.; ZHENG, N. et al. Urea metabolism and regulation by rumen bacterial urease in ruminants - a review. Ann. Anim. Sci., v.18, p.303-318, 2018.
• KANEKO, J.J.; HARVEY, W.J.; BRUSS, L.M. Appendix 8 blood analyte reference values in large animals. In: ______. (Eds.). Clinical biochemistry of domestic animals. California, United States: Academic Press, 2008. p.882-888.
• KATICA, M.; MUKACA, A.; SALKIĆ, A. et al. Evaluation of the effect of daily cow's milk production on liver enzyme levels. Turk. J. Vet. Res. v.8, p.71-79, 2024.
• LARSEN, M.; KRISTENSEN, N.B. Precursors for liver gluconeogenesis in periparturient dairy cows. Animal, v.7, p.1640-1650, 2013.
• LIESKE, B.; JANTZ, A.; FINKE, B. An improved analytical approach for the determination of bovine serum albumin in milk. Lait, v.85, p.237-248, 2005.
• LOPREIATO, V.; FERRONATO, G.; MINUTI, A. et al. Role of nutraceuticals during the transition period of dairy cows: a review. J. Anim. Sci. Biotechnol., v.11, p 96, 2020.
• MACRAE, A.I.; BURROUGH, E.; FORREST, J. et al. Prevalence of excessive negative energy balance in commercial United Kingdom dairy herds. Vet J., v.248. p.51-57, 2019.
• MCFADDEN, J.W. Review: Lipid biology in the periparturient dairy cow: contemporary perspectives. Animal, v.14, p.s165-s175, 2020.
• MEZZETTI, M.; BIONAZ, M.; TREVISI, E. Interaction between inflammation and metabolism in periparturient dairy cows. J. Anim. Sci., v.98. p.S155-S174, 2020.
• NUTRIENT requirements of dairy cattle. 7.ed. Washington, D.C.: National Academy Press, 2001.
• PAIANO, R.B.; LAHR, F.C.; SILVA, L.S.B. et al. Haematological and biochemical profiles during the puerperium in dairy cows - short communication. Acta Vet, Hung., v.67, p.377-384, 2019.
• PAIANO, R.B.; BIRGEL, D.B.; BONILLA, J. et al. Evaluation of biochemical profile of dairy cows with metabolic diseases in tropical conditions. Reprod. Domest. Anim., v.55, p.1219-1228, 2020.
• PASCOTTINI, O.B.; LEROY, J.; OPSOMER, G. Maladaptation to the transition period and consequences on fertility of dairy cows. Reprod. Domest. Anim., v.57, Suppl.4, p.21-32, 2022.
• PAYNE, J.M. The future of presyntomatic diagnosis. Proc. R. Soc. Med., v.65. p.181-183, 1972.
• PEDRAZA, C.; MANSILLA I,M.; FAJARDO, P. et al. Cambios en la producción y composición láctea por efecto del incremento de células somáticas en leche de vacas. Agric. Tec., v.60, p.251-258, 2000
• PEEL, M.C.; FINLAYSON, B.L.; MCMAHON, T.A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., p.1633-1644, 2007.
• PEGOLO, S.; GIANNUZZI, D.; BISUTTI, V. et al. Associations between differential somatic cell count and milk yield, quality, and technological characteristics in Holstein cows. J. Dairy Sci., v.104, p.4822-4836, 2021.
• PICCININI, R.; BINDA, E.; BELOTTI, M. et al. The evaluation of non-specific immune status of heifers in field conditions during the periparturient period. Vet. Res., v.35, p.539-550, 2004.
• PINEDO, P.; MELENDEZ, P. Liver disorders associated with metabolic imbalances in dairy cows. Vet. Clin. North Am. Food Anim. Pract., v.38, p.433-446, 2022.
• SAS/STAT statistics user’s guide. Version 8.2 ed. Cary, NC: SAS Institute Inc., 2001.
• SISTEMA producto leche - alimentos - lácteos - leche y productos de leche - guía de muestreo. ed. Consejo para el fomento de la calidad de la leche y sus derivados, A.C. 2017. (NMX-F-718-COFOCALEC).
• SOLTANI, F.M.; FARAHMANDI, A.; HOSSEINPOUR, S. Recent advances in ultrasound application as a novel technique in analysis, processing and quality control of fruits, juices and dairy products industries: a review. Ultrason Sonochem., v.57, p.73-88, 2019.
• STOCCO, G.; SUMMER, A.; CIPOLAT-GOTET, C. et al. Differential somatic cell count as a novel indicator of milk quality in dairy cows. Animals, v.10, p.1-13, 2020.
• SUMMER, A.; FRANCESCHI, P.; FORMAGGIONI, P. et al. Influence of milk somatic cell content on Parmigiano-Reggiano cheese yield. J. Dairy Res., v.82, p.222-227, 2015.
• WANG, F.; SHI, H.; WANG, S. et al. Amino acid metabolism in dairy cows and their regulation in milk synthesis. Curr. Drug Metab., v.20, p.36-45, 2019.
• WANG, G.; ZHU, Y.; FENG, D. et al. Hepatic gluconeogenesis and regulatory mechanisms in lactating ruminants: a literature review. Anim. Res. One Health, v.1, p.1-10, 2024.
• WEINER, I.D.; MITCH, W.E.; SANDS,.J.M. Urea and ammonia metabolism and the control of renal nitrogen excretion. Clin. J. Am. Soc. Nephrol., v.10, p.1444-1458, 2015.
• YANG, W.; ZHANG, B.; XU, C. et al. Effects of ketosis in dairy cows on blood biochemical parameters, milk yield and composition, and digestive capacity. J. Vet. Res., v.63, p.555-560, 2019.
• ZACHUT, M.; ŠPERANDA, M.; ALMEIDA, A.M. et al. Biomarkers of fitness and welfare in dairy cattle: healthy productivity. J. Dairy Res., v.87, p.4-13, 2020.
• ZECCONI, A.; SESANA, G.; VAIRANI, D. et al. Somatic cell count as a decision tool for selective dry cow therapy in Italy. Ital. J. Anim. Sci., v.18, p.435-440, 2019.
• ZHANG, F.; NAN, X.; WANG, H. et al. Effects of propylene glycol on negative energy balance of postpartum dairy cows. Animals, v.10, p.10-21, 2020.

• DATA AVAILABILITY STATEMENT
  Data-in-article - the research data are available within the article itself.
• FUNDING
  This research was funded by the Academic Group: UCOL-CA-11 Production Systems Agriculture, by the Network of Advances in Agricultural Research in Mexico, and the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI-Mexico).

Edited by

• Editor-chefe:
  Marcelo Resende de Souza

• Editor- científico:
  Antônio de Pinho Marques Jr

Data availability

Data-in-article - the research data are available within the article itself.

Publication Dates

• Publication in this collection
  12 Jan 2026
• Date of issue
  Jul-Aug 2025

History

• Received
  26 Mar 2025
• Accepted
  18 June 2025