animals Article Changes in a Comprehensive Profile of Saliva Analytes in Fattening Pigs during a Complete Productive Cycle: A Longitudinal Study Alba Ortín-Bustillo 1 , Damián Escribano 1,2 , Marina López-Arjona 1 , María Botia 1 , Pablo Fuentes 3 , Silvia Martínez-Miró 3 , Camila P. Rubio 4 , Edgar García-Manzanilla 5,6 , Lorena Franco-Martínez 1,5 , Luis Pardo-Marín 1 , José J. Cerón 1 , Pol Llonch 4 and Fernando Tecles 1, * 1 Interdisciplinary Laboratory of Clinical Analysis of the University of Murcia (Interlab-UMU), Regional Campus of International Excellence ‘Campus Mare Nostrum’, University of Murcia, Campus de Espinardo s/n, 30100 Murcia, Spain; alba.ortinb@um.es (A.O.-B.); det20165@um.es (D.E.); marina.lopez10@um.es (M.L.-A.); maria.botiag@um.es (M.B.); lorena.franco2@um.es (L.F.-M.); lpm1@um.es (L.P.-M.); jjceron@um.es (J.J.C.) 2 Department of Animal Production, Regional Campus of International Excellence ‘Campus Mare Nostrum’, University of Murcia, Campus de Espinardo s/n, 30100 Murcia, Spain 3 Cefu S.A., 30840 Murcia, Spain; pablo.fuentes@cefusa.com (P.F.); silviamm@um.es (S.M.-M.) 4 Department of Animal and Food Science, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain; camila.peres@um.es (C.P.R.); pol.llonch@uab.cat (P.L.) 5 Pig Development Department, Teagasc Grassland Research and Innovation Centre, P61 C996 Fermoy, Ireland; edgar.garciamanzanilla@teagasc.ie 6 School of Veterinary Medicine, University College Dublin, D04 V1W8 Dublin, Ireland * Correspondence: ftecles@um.es; Tel.: +34-868-887-082 Citation: Ortín-Bustillo, A.; Escribano, D.; López-Arjona, M.; Simple Summary: The aim of this study was to evaluate whether a panel of 29 salivary biomarkers Botia, M.; Fuentes, P.; Martínez-Miró, of stress, immunity, inflammation, redox homeostasis and other physiological functions can change S.; Rubio, C.P.; García-Manzanilla, E.; in healthy fattening pigs when monitoring the different phases of their productive cycle and can be Franco-Martínez, L.; Pardo-Marín, L.; influenced by various sources of variations such as gender and performance parameters. Several et al. Changes in a Comprehensive analytes showed changes due to the productive cycle, with a majority of the analytes showing higher Profile of Saliva Analytes in Fattening Pigs during a Complete Productive values at lactation and at the beginning of nursery. Additionally, differences were seen due to sex. Cycle: A Longitudinal Study. Animals These differences can be related in some cases with performance parameters and should be taken 2022, 12, 1865. https://doi.org/ into consideration for an appropriate interpretation of the analytes. 10.3390/ani12141865 Abstract: A comprehensive panel of 29 salivary analytes was measured in fattening pigs to evaluate Academic Editors: Arianna Miglio its possible changes along their productive cycle. The identification of those changes would allow a and Angelica Stranieri better interpretation of the results according to the productive phase of the animal. Saliva samples Received: 3 July 2022 were obtained from 49 Large-White pigs (24 females, 25 males) in suckling phase, at the beginning and Accepted: 20 July 2022 the end of the nursery phase, and at the beginning and the end of the growing phase. Several analytes Published: 21 July 2022 changed according to the phase of the productive cycle, with most of the analytes showing higher Publisher’s Note: MDPI stays neutral values at lactation and at the beginning of nursery. Additionally, differences were seen due to sex. with regard to jurisdictional claims in When possible relations between performance parameters and analytes were evaluated, significant published maps and institutional affil- positive but weak relationships were found between weight at birth and salivary γ-glutamyl trans- iations. ferase, and between back-fat thickness and salivary lactate dehydrogenase. In conclusion, differences in the values of salivary analytes can be found in fattening pigs depending on the productive phase and sex of the animals. Copyright: © 2022 by the authors. Keywords: saliva; biomarker; fattening pigs; productive phase; gender Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 1. Introduction creativecommons.org/licenses/by/ Saliva has gained interest in recent years as a biological sample for analytical pur- 4.0/). poses [1]. Its collection is easy and non-invasive, does not require the use of specialized Animals 2022, 12, 1865. https://doi.org/10.3390/ani12141865 https://www.mdpi.com/journal/animals Animals 2022, 12, 1865 2 of 14 material and can be performed by non-trained staff [2,3]. This fact is relevant especially in pigs, since the need for restraint in this species makes blood collection painful and stressful [4,5]. In pigs, saliva has traditionally been used for stress assessment through the measure- ment of cortisol, a biomarker of the hypothalamic–pituitary–adrenal axis (HPA) activa- tion [4]. More recently, saliva has been also used for the measurement of analytes informing about the health status of the pigs, through additional biomarkers of stress, and biomarkers of inflammation [6], immune response [7], or redox status [8]. In addition to all this, saliva can be used for the measurement of analytes that are routinely determined in blood. This fact has led to the introduction of a term called ‘sialochemistry’, referring to the analytes that can be measured in saliva [9,10]. In veterinary medicine, sialochemistry studies have been previously performed in horses [11], cows [12] and pigs [13]. Namely in the porcine species, a sialochemistry study was made to evaluate the changes in saliva analytes during pregnancy, farrowing and lactation in sows [13]. In pigs, sialochemistry can include analytes providing diverse information. To evalu- ate the stress response, cortisol, chromogranin A (CgA), salivary α-amylase (sAA), total esterase activity (TEA), butyrylcholinesterase (BChE), lipase (Lip) and oxytocin can be mon- itored. To assess activation of the immune system, analytes such as the enzyme adenosine deaminase (ADA) and its isoenzymes can be evaluated. Inflammation can be detected by increases in positive acute phase proteins such as haptoglobin (Hp). Redox homeostasis can be assessed by measuring oxidants such as hydrogen peroxide (peroxide-activity Pox-Act, also known as H2 O2 ), reactive oxygen-derived compounds (d-ROMs) and the advanced oxidation protein products (AOPP), or antioxidants molecules such as cupric reducing antioxidant capacity (CUPRAC), ferric reducing ability of saliva (FRAS), and uric acid (UA). Finally, sialochemistry can include analytes related with the metabolic performance of the organism or with different tissues and organs, which are routinely measured in blood such as total protein (TP), urea, creatinine, glucose, lactate, alanine aminotransferase (ALT), as- partate aminotransferase (AST), alkaline phosphatase (FAL), lactate dehydrogenase (LDH), creatine kinase (CK), γ-glutamil transferase (GGT), calcium (Ca) and phosphorous (P) [14]. The aim of this study was to evaluate whether the aforementioned salivary biomarkers of stress, immunity, inflammation, redox homeostasis and analytes related with metabolism and different tissues and organs that can be included in the sialochemistry profile can change in healthy fattening pigs over their productive cycle and whether they can be influenced by various sources of variations such as gender and performance parameters. For this purpose, a longitudinal study was performed in which a group of animals was serially monitored throughout its productive cycle, from suckling to growing-finishing phase. In addition, the possible influence of other factors or sources of variation in these salivary biomarkers was studied such as: (1) gender of the animals; (2) performance parameters such as weight at birth, weight gain or back fat thickness (BF) in finishing pigs. This data will provide a complete picture of the values of analytes that can be measured in saliva of pigs in farm conditions and their possible physiological influences. 2. Materials and Methods 2.1. Animals A total of 50 Large-White pigs (Sus scrofa domesticus, 25 males and 25 females) from 5 litters (average litter size of 10.0 ± 0.7) were used in this study which was carried out at the Veterinary Teaching Farm of the University of Murcia (Guadalupe, Murcia, Spain). The birth weight of the piglets was 1.93 ± 0.59 kg (1.83 ± 0.49 for females, 2.02 ± 0.66 for males). This farm is declared free of the porcine respiratory and reproductive syndrome virus, and all animals were vaccinated against Mycoplasma hyopneumoniae (Stellamune Mycoplasma, inactivated Mycoplasma hyopneumoniae NL 1042, Pfizer Animal Health, Madrid, Spain) and Porcine circovirus type 2 (Porcilis® PCV, MSD Animal Health, Boxmeer, The Netherlands) at weaning. Animals 2022, 12, 1865 3 of 14 2.2. Experimental Procedure This experimental was made between October 2021 and April 2022. Animals included in this study were reared under intensive conventional conditions and were monitored throughout their productive cycle, according to the following scheme: (a) Suckling phase. Cross-fostering was carried out during 24 h postpartum to adjust litter size to 10 piglets per sow. All animals were individually identified by an ear tag. From 10 days of age, suckling piglets had access to a commercial pre-starter diet. Piglets were firstly sampled at 24 days of life (T1), close to weaning, which was performed at 28 days after birth. (b) Nursery phase. After weaning, piglets were moved to an environmentally controlled nursery, which was in the same farm but in a different building. Each pen contained a standard feeder and a nipple drinker to provide ad libitum access to feed and water. Piglets were fed using a two-phase feeding program over a 7-week period. Pre- starter (for the first 2 weeks, containing 2.54 Mcal/kg Net energy (NE) and 183.2 g/kg crude protein (CP)) and starter diet (for the next 5 weeks, containing 2.52/kg NE and 172.2 g/kg CP). The animals were left a week in the new conditions for acclimatization, after which a new sample was taken (T2). At the end of this phase, the animals were sampled again (T3). (c) Growing-finishing phase. Pigs were then moved to fattening pens. During this period, animals were given ad libitum access to a nutritionally balanced diet and water. Pig fed an initial growing diet (first 8 weeks, 2.45 Mcal/kg NE, 164 g/kg CP) and a finishing diet (for the next 7 weeks, 2.40 Mcal/kg NE and 149.6 g/kg CP). After one week for acclimatization, pigs were sampled again (T4). The animals stayed in this phase for 14 weeks, being sampled again at the end of the phase (T5). All diets were based on cereals and soybean meal. 2.3. Sampling Saliva was collected using Salivette tubes (Sarstedt, Aktiengesellschaft and Company, D-51588 Nümbrecht, Germany) containing a sponge (Esponja Marina, La Griega E. Koronis, Madrid, Spain) instead of a cotton swab. Pigs were allowed to chew the sponge, which was clipped to a flexible thin metal rod, for one minute (or more if needed) until thoroughly moist. Then, the sponge was placed into the Salivettes. If an animal was eating or drinking at the sampling time, this animal was sampled later to ensure clean samples (with no evidence of food debris or dirt) were obtained. Tubes were maintained and refrigerated until arrival at the laboratory (<2 h), where the Salivettes were immediately centrifuged (3000× g, 10 min, 4 ◦ C), and the supernatants collected. All samples were stored at −80 ◦ C until the end of the experimental sampling period and then analyzed. In order to avoid interferences with the analytical methods, only clean samples were used for analyses. All samples were taken between 09.30 and 10.30 h. The research protocols were approved by the Bioethical Commission of Murcia University according to the European Council Directives regarding the protection of animals used for experimental purposes (Approval number, 235/2016; Approval date, 25 April 2016). 2.4. Welfare Assessment All animals were monitored in all sampling times for the presence of pathologic conditions, in order to discard any animal suffering lameness, prolapses, gastrointestinal or respiratory diseases, or any other health issue that could interfere with the results. In addition, carcasses were evaluated at the slaughterhouse in order to discard any animals with pathologies detected after sacrifice. 2.5. Measurements of Performance Parameters Table 1 summarizes samplings and measurements throughout the study period. The following data were recorded from the animals: Animals 2022, 12, 1865 4 of 14 (a) Weight gain. Pigs were weighed at birth and at each sampling time in order to determine the mean weight gain throughout the study. (b) Back-fat thickness (BF). It was measured at the end of the growing phase by ultrasound scan using a linear probe (SF1, Wireless Backfat and Loin Depth Scanner, Sonivet, Beijing, China), at the P2 position (last rib, 65 mm from the center line of the back). Measurements were performed twice. The average of the measurements was used for further calculations, following previous protocols [15]. Table 1. Summary of the experimental protocol. Approximate Age of Sampling Number Phase Measurements/Sampling the Animals (Days) T0 Birth time 1–2 Body weight Body weight T1 Suckling phase 24 Saliva Body weight T2 Beginning of nursery phase 35 Saliva Body weight T3 End of the nursery phase 88 Saliva Beginning of the Body weight T4 99 growing phase Saliva Body weight T5 End of the growing phase 193 BF Saliva BF: back-fat thickness. 2.6. Biomarkers The following measurements were performed in the saliva samples: (a) Stress biomarkers. Cortisol was measured by an indirect competitive AlphaLISA assay developed with a commercially available monoclonal antibody against cortisol, a method that was previously validated for porcine saliva [16]. CgA was measured by an in-house method based on previously published protocols [17]. sAA was measured by a commercial spectrophotometric method (a-Amylase, OSR6182, Beckman Coulter Inc., Fullerton, CA, USA) previously validated in porcine saliva [18]. TEA was measured according to a previously validated method [19]. BChE was analyzed using a previously described protocol [20]. Lip was measured by a commercial spectrophotometric method (Lipase, Beckman Coulter Inc., Fullerton, CA, USA). For salivary oxytocin determination, an AlphaLISA method previously developed and validated for use in porcine saliva samples using a monoclonal antibody was used [16]. (b) Inflammatory biomarkers. Hp was measured by an in-house assay based on AlphaL- ISA technology [13]. (c) Immune system biomarkers. ADA was analyzed with a commercially available spectrophotometric assay (Adenosine Deaminase assay kit, Diazyme Laboratories, Poway, CA, USA), previously validated for porcine saliva [21]. Isoenzymes ADA1 and ADA2 were measured using erythro-9-(2-hydroxy-3-nonyl) adenine (EHNA) as a specific ADA1 inhibitor [7]. (d) Oxidative stress biomarkers. CUPRAC was assayed by the method of [22]. FRAS was measured by the method of [23]. UA was measured using a commercially available kit from Beckman (Beckman Coulter Inc.). AOPP, Pox-Act and d-ROMs concentrations were assessed by previously published methods [24–26]. All these assays have been validated in porcine saliva [8]. (e) Routine biochemistry analytes. ALT, AST, ALP, GGT, LDH, CK, urea, creatinine, glucose, lactate, Ca and P were measured using commercial kits from Beckman (Beckman Coulter Inc.). TP was analyzed using a commercial colorimetric kit designed to measure urinary and cerebrospinal fluid (CSF) proteins (Protein in Urine and CSF, Spinreact, Barcelona, Spain). Animals 2022, 12, 1865 5 of 14 Assays of the points “c”, “d” and “e” were performed in an automated analyzer (Olympus AU600, Olympus Diagnostica GmbH, Ennis, Ireland). Table S1 shows the different methods with their lower limit of detection. 2.7. Statistical Analysis All data were assessed for normality by the Shapiro–Wilk method. Since most of the data showed non-normal distribution, continuous variables were naturally log transformed prior to analysis. Data showing normal distribution was expressed by mean (standard devi- ation) whereas data with non-normal distribution was expressed by median (interquartile range). The changes in the different measurements over time were assessed using a Mixed Linear Model in which time and sex were considered as fixed factors, being the individual considered as a random factor. Birth weight, weight gain and BF were used as covariates. Those analytes found as being significantly influenced by covariates were further studied by linear regression analyses in order to know whether the analytical results can be used for prediction of performance parameters. When collinearity was detected, those vari- ables showing the higher significance and no correlation with others were included in the analyses. Statistical analyses were performed using the SPSS statistics package (IBM SPSS Statistics for Windows, Version 26.0. IBM Corp., Armonk, NY, USA), and the significance was set at p < 0.05. 3. Results 3.1. Longitudinal Study From the 50 pigs selected initially, 49 finished the study with no pathologic conditions, therefore they were included in the study, whereas one pig was discarded due to lameness. Data regarding the animals included in the longitudinal study are shown in Table 2. Table 2. Data from the animals included in the longitudinal study. Mean (standard deviation) are expressed in bold, whereas median (interquartile range) are expressed in italics. Number of animals 49 Females 24 Males 25 Weight at birth (kg) 1.84 (0.83) Females 1.72 (0.72) Males 2.05 (1.03) Weight gain (gr/day) 638.20 (79.72) Females 596.91 (69.06) Males 676.04 *** (70.40) BF (cm) 12.61 (3.01) Females 11.92 (3.23) Males 13.22 ** (2.73) BF: Back-fat thickness. Asterisks show statistically significant differences between sexes (**: p < 0.01; ***: p < 0.001). The results of the salivary biomarkers at each sampling period appear in Table 3. Almost all the analytes showed significant changes in their values along the different sampling times. According to the results, biomarkers can be divided in those which concentration increases or decrease throughout productive cycle of the animals. Animals 2022, 12, 1865 6 of 14 Table 3. Results obtained in a panel of 29 salivary biomarkers in 49 Large-White pigs (24 females, 25 males) at lactation (T1), beginning of the nursery (T2), end of nursery (T3), beginning of growing (T4) and end of growing (T5). Mean (standard deviation) is expressed in bold, whereas median (interquartile range) is expressed in italics. Sampling Time Significant Biomarkers T1 T2 T3 T4 T5 Fixed Factors p Value Covariates Cortisol (ng/mL) 125.6 a (102.2) 224.4 b (233.4) 60.8 c (32.6) 83.6 c (53.2) 55.6 c (58.0) Time <0.001 Female 119.0 a (108.4) 253.4 b (249.4) 66.0 c (36.3) 88.4 c (49.2) 74.6 c (51.2) Sex 0.208 Male 129.2 a (104.2) 214.4 b (150.8) 58.4 c (32.6) 78.4 ac (53.0) 49.2 c (51.8) Time × sex 0.855 CgA (µg/mL) 0.61 a (0.26) 0.39 b (0.33) 0.36 b (0.42) 0.49 ab (0.39) 0.64 a (0.32) Time <0.001 Female 0.60 ab (0.26) 0.37 b (0.39) 0.44 ab (0.42) 0.49 ab (0.57) 0.69 a (0.35) Sex 0.893 BF (p = 0.004) Male 0.62 a (0.27) 0.41 ab (0.30) 0.24 b (0.96) 0.46 ab (0.42) 0.60 a (0.29) Time × sex 0.726 sAA (IU/mL) 9.22 a (35.26) 9.22 b (10.57) 2.12 c (4.12) 0.63 d (1.24) 0.56 d (0.86) Time <0.001 Female 9.22 a (41.50) 9.22 a (10.95) 2.41 b (5.75) 0.66 c (1.61) 0.80 c (1.21) Sex 0.539 Male 9.22 a (20.43) 8.82 a (11.49) 1.89 b (3.41) 0.48 c (1.01) 0.44 c (0.58) Time × sex 0.756 TEA (IU/L) 769.5 a (401.8) 320.8 b (146.6) 179.5 cd (78.9) 136.4 d (68.7) 168.3 c (146.6) Time <0.001 Female 840.0 a (651.3) 326.9 b (143.7) 203.8 c (63.1) 117.9 d (66.6) 227.5 bc (158.9) Sex 0.028 Male 559.9 *a (292.9) 254.5 b (150.8) 156.3 *c (86.5) 146.1 c (96.5) 135.1 *c (109.8) Time × sex 0.035 BChE (IU/mL) 1.49 a (1.33) 0.88 b (0.65) 0.69 bc (0.78) 0.40 c (0.71) 0.59 b (1.32) Time <0.001 Female 1.63 a (1.24) 0.94 a (0.47) 0.80 ab (1.21) 0.40 b (0.58) 0.64 a (2.12) Sex 0.491 BF (p = 0.028) Male 1.37 a (1.60) 0.72 b (0.85) 0.55 b (0.60) 0.39 b (1.23) 0.36 b (0.92) Time × sex 0.351 Lip (IU/L) 44.0 a (70.4) 16.6 c (37.0) 30.4 b (29.6) 44.0 ab (70.6) 32.4 ab (47.2) Time <0.001 Female 44.4 a (43.2) 18.8 c (42.4) 41.6 abc (32.7) 42.0 abc (90.7) 48.2 ab (85.0) Sex 0.030 Male 42.0 a (83.6) 14.4 c (24.6) 25.6 *bc (24.8) 46.4 a (65.6) 24.0 *ab (28.2) Time × sex 0.384 Oxytocin (ng/mL) 6.16 a (7.17) 3.62 a (3.06) 1.33 b (0.72) 1.64 b (0.91) 1.74 b (0.77) Time <0.001 Female 6.71 a (6.98) 4.48 a (5.06) 1.50 b (0.93) 1.57 b (1.13) 2.07 b (1.60) Sex 0.201 Male 4.85 a (7.53) 3.24 a (2.60) 1.24 b (0.58) 1.67 b (0.95) 1.53 b (0.52) Time × sex 0.077 Hp (µg/mL) 3.36 a (2.79) 3.26 a (1.90) 1.07 b (1.17) 0.67 b (0.99) 0.54 b (0.84) Time <0.001 Female 3.44 a (3.26) 3.59 a (2.06) 1.48 b (1.28) 0.60 b (0.96) 0.85 b (0.93) Sex 0.276 Male 3.36 a (2.33) 3.27 a (1.82) 0.75 *b (0.64) 0.72 b (1.03) 0.44 b (0.40) Time × sex 0.111 Animals 2022, 12, 1865 7 of 14 Table 3. Cont. Sampling Time Significant Biomarkers T1 T2 T3 T4 T5 Fixed Factors p Value Covariates ADA1 (IU/mL) 3.90 a (3.15) 2.71 ab (1.37) 2.03 c (1.78) 1.14 d (0.97) 2.13 bc (1.62) Time <0.001 Female 3.90 a (5.60) 2.71 ab (2.04) 2.51 b (1.18) 1.17 c (0.84) 2.38 b (1.25) Sex 0.162 Male 3.87 a (2.63) 2.71 ab (1.02) 1.54 c (2.01) 1.09 c (1.12) 1.69 bc (1.57) Time × sex 0.409 ADA2 (IU/L) 11.84 a (8.95) 7.38 b (4.67) 4.77 cd (6.27) 3.70 d (3.37) 5.53 c (5.80) Time <0.001 Female 12.20 a (6.54) 7.54 ab (4.37) 5.91 bc (5.01) 3.68 c (4.19) 6.51 ab (4.39) Sex 0.636 Male 11.24 a (12.06) 7.22 a (4.68) 3.12 b (5.10) 3.72 b (3.39) 4.40 b (5.36) Time × sex 0.092 CUPRAC (µmol/L) 287.9 a (114.3) 279.2 a (205.7) 146.4 b (128.0) 222.0 ab (236.9) 195.6 ab (205.6) Time <0.001 Female 299.2 a (131.6) 293.6 a (182.0) 217.0 a (176.8) 218.0 a (260.8) 264.8 a (190.9) Sex 0.004 Birth weight (p = 0.045) Male 277.5 a (97.4) 264.0 a (231.2) 130.8 ***b (94.4) 222.8 a (230.8) 156.0 **ab (127.6) Time × sex 0.183 FRAS (µmol/L) 445.2 a (391.2) 378.0 ab (325.2) 193.2 c (189.6) 239.2 bc (306.6) 243.2 c (243.6) Time <0.001 Female 539.8 a (538.4) 398.8 ab (453.6) 273.2 b (262.1) 192.8 b (335.2) 368.8 b (286.3) Sex <0.001 Weight gain (p = 0.048) Male 426.0 *a (190.8) 334.2 a (254.2) 143.6 ***c (155.0) 256.0 ab (301.8) 202.8 **bc (136.8) Time × sex 0.144 UA (mg/dL) 0.52 a (0.36) 0.40 a (0.31) 0.20 b (0.18) 0.16 b (0.18) 0.44 a (0.32) Time <0.001 Birth weight (p = 0.004) Female 0.68 a (0.84) 0.40 b (0.36) 0.24 bc (0.23) 0.16 c (0.19) 0.46 ab (0.30) Sex 0.011 Weight gain (p = 0.041) Male 0.44 ***a (0.28) 0.40 a (0.30) 0.16 *b (0.12) 0.20 b (0.16) 0.40 a (0.30) Time × sex 0.049 AOPP (µmol/L) 214.9 a (278.9) 214.4 a (221.0) 69.0 b (92.8) 92.8 b (196.9) 102.2 b (165.0) Time <0.001 Female 244.0 a (336.8) 201.0 a (237.4) 97.7 b (108.4) 84.0 b (191.8) 167.0 ab (144.5) Sex 0.006 Weight gain (p = 0.013) Male 207.9 a (171.2) 215.7 a (187.0) 30.6 **c (66.4) 132.6 ab (222.3) 63.8 *bc (91.7) Time×sex 0.019 Pox-Act (µmol/L) 501.6 a (532.4) 309.6 a (271.9) 331.2 a (413.1) 528.7 a (781.0) 360.6 a (346.2) Time 0.018 Female 454.3 a (473.6) 251.0 a (196.1) 437.6 a (465.3) 422.4 a (577.6) 384.1 a (496.7) Sex 0.111 Weight gain (p = 0.017) Male 583.8 ab (617.1) 330.0 ab (347.0) 307.0 a (409.9) 683.2 *b (1116.8) 346.4 a (243.5) Time × sex 0.021 d-Roms (Carrateli units) 206.4 a (121.8) 203.2 ab (134.7) 298.9 bc (136.2) 287.0 c (282.2) 218.8 ab (55.2) Time <0.001 Female 240.0 a (128.4) 219.2 a (112.4) 334.2 a (195.2) 196.4 a (311.6) 218.2 a (101.9) Sex 0.962 Male 188.4 a (86.8) 201.6 a (204.4) 227.6 a (166.8) 325.6 b (186.6) 219.6 a (27.2) Time × sex 0.016 ALT (IU/L) 42.2 a (81.7) 18.2 b (25.2) 6.4 d (9.6) 8.4 cd (19.6) 14.4 bc (22.4) Time <0.001 Female 59.6 a (80.4) 19.6 b (26.0) 10.8 b (19.0) 9.6 b (21.0) 18.0 b (20.5) Sex 0.032 Weight gain (p = 0.022) Male 34.4 a (87.0) 17.2 ab (25.0) 4.8 ***c (5.6) 8.0 b (19.2) 14.4 b (26.2) Time × sex 0.020 Animals 2022, 12, 1865 8 of 14 Table 3. Cont. Sampling Time Significant Biomarkers T1 T2 T3 T4 T5 Fixed Factors p Value Covariates AST (IU/L) 454.4 a (441.6) 101.0 b (81.2) 61.2 d (61.6) 67.2 c (68.2) 73.2 bc (96.0) Time <0.001 Female 659.7 a (313.2) 107.2 b (90.8) 72.4 b (62.8) 70.4 b (44.6) 101.2 b (87.2) Sex 0.020 Birth weight (p = 0.047) Male 411.6 *a (182.2) 91.6 b (91.0) 41.2 *d (45.6) 66.8 bcd (79.4) 60.8 bc (78.6) Time × sex 0.426 ALP (IU/L) 179.2 a (214.4) 82.4 ab (91.6) 72.8 a (78.0) 65.6 a (71.2) 31.2 b (40.8) Time 0.002 Female 187.2 ab (264.0) 83.2 ab (110.4) 91.6 a (101.0) 49.6 ab (106.2) 45.2 b (60.0) Sex 0.354 Male 144.0 a (162.2) 72.8 ab (95.6) 41.6 **ab (52.0) 75.2 a (72.4) 29.6 b (18.0) Time × sex 0.003 GGT (IU/L) 27.8 a (17.5) 12.7 b (12.8) 9.8 b (12.0) 9.4 b (9.4) 9.1 b (8.1) Time <0.001 Female 28.8 a (31.6) 13.4 b (14.5) 12.6 b (16.6) 8.1 b (7.4) 11.9 b (11.2) Sex 0.037 Birth weight (p = 0.015) Male 26.8 a (11.2) 11.9 b (14.0) 7.2 **b (7.3) 10.3 b (11.4) 7.1 **b (4.3) Time × sex 0.010 LDH (IU/mL) 2.38 a (1.90) 0.50 b (0.37) 0.32 c (0.37) 0.23 c (0.21) 0.22 c (0.30) Time <0.001 Female 2.71 a (3.16) 0.52 b (0.36) 0.38 bc (0.37) 0.23 c (0.19) 0.35 bc (0.33) Sex 0.149 BF (p = 0.027) Male 2.23 a (1.41) 0.39 b (0.36) 0.20 c (0.29) 0.21 bc (0.27) 0.21 bc (0.23) Time × sex 0.701 CK (IU/L) 49.4 a (66.7) 11.6 b (8.9) 4.6 d (6.0) 6.0 cd (6.2) 7.8 bc (8.0) Time <0.001 Female 76.4 a (113.0) 12.2 b (8.0) 6.1 c (6.7) 6.1 c (5.3) 10.1 bc (9.5) Sex 0.003 Male 42.8 ***a (34.0) 9.6 b (10.2) 3.7 *d (3.5) 5.3 cd (6.7) 6.8 *bc (5.2) Time × sex 0.115 Urea (mg/dL) 14.4 ac (7.6) 0.4 b (5.8) 3.2 b (8.0) 11.6 a (8.6) 21.6 c (14.8) Time <0.001 Female 14.0 a (10.8) 1.2 b (11.6) 5.4 a (11.1) 12.2 a (11.0) 24.6 c (11.4) Sex 0.050 Male 13.2 a (13.0) 0.1 b (4.8) 1.2 ***b (5.4) 10.4 a (8.0) 16.4 *a (12.6) Time×sex 0.015 Creatinine (mg/dL) 0.66 a (0.60) 1.02 b (0.97) 0.68 a (0.48) 0.60 a (0.64) 1.52 c (1.00) Time <0.001 Female 0.48 a (0.48) 1.08 b (0.92) 0.80 ab (0.61) 0.52 a (0.72) 1.96 c (1.16) Sex 0.082 Male 0.68 a (0.66) 0.92 b (1.10) 0.44 **a (0.34) 0.76 a (0.64) 1.44 **b (0.96) Time × sex 0.004 Glucose (mg/dL) 0.8 a (3.8) 87.5 b (101.8) 2.3 a (2.6) 2.8 a (4.6) 1.6 a (1.8) Time <0.001 Female 0.9 a (3.6) 93.7 b (101.4) 2.9 a (6.1) 3.5 a (4.3) 2.4 a (2.2) Sex 0.049 Male 0.8 a (3.9) 66.1 b (99.8) 1.8 *a (2.4) 1.8 a (5.5) 1.2 *a (1.1) Time × sex 0.349 Lactate (µmol/L) 145.5 a (384.6) 2819.1 b (2688.2) 58.2 c (110.2) 49.9 cd (78.8) 16.6 d (41.6) Time <0.001 Female 183.0 a (324.3) 2752.6 b (1405.4) 54.1 a (237.0) 54.1 a (94.1) 16.6 a (49.9) Sex 0.776 Weight gain (p = 0.012) Male 116.4 a (602.9) 3027.0 b (3937.6) 58.2 a (68.6) 41.6 a (57.2) 16.6 a (37.4) Time × sex 0.264 Animals 2022, 12, 1865 9 of 14 Table 3. Cont. Sampling Time a Ca (mg/dL) 13.4 (2.9) 8.9 b (2.1) 5.9 d (2.4) 7.3 cd (2.5) 7.7 bc (2.0) Time <0.001 Female 14.1 a (2.9) 8.5 b (2.4) 5.9 c (2.3) 7.4 bc (2.4) 8.4 b (1.7) Sex 0.158 Weight gain (p = 0.040) Male 12.7 a (2.9) 9.1 b (2.2) 5.8 c (2.5) 7.3 bc (2.5) 7.1 c (2.1) Time × sex 0.014 P (mg/dL) 1.57 a (0.54) 1.96 b (2.37) 1.28 ac (0.54) 1.44 ab (1.46) 0.96 c (0.88) Time <0.001 Birth weight (p = 0.030) Female 1.74 ab (0.64) 2.00 b (2.08) 1.40 ab (0.80) 1.46 ab (1.37) 1.20 a (0.74) Sex 0.006 Weight gain (p = 0.026) Male 1.41 *abc (0.38) 1.92 b (2.54) 1.20 **ac (0.44) 1.44 ab (1.54) 0.84 *c (0.56) Time × sex 0.875 Protein (mg/dL) 297.2 a (323.9) 170.3 b (152.3) 44.1 d (75.8) 62.8 cd (61.9) 85.0 c (37.9) Time <0.001 Weight gain (p = 0.026) Female 446.2 a (400.0) 201.8 b (191.5) 77.2 c (76.0) 54.5 c (97.1) 76.4 c (54.9) Sex 0.014 BF (p = 0.020) Male 278.2 a (171.7) 166.9 b (169.4) 26.9 ***d (35.8) 65.9 c (40.2) 79.0 bc (48.2) Time × sex 0.018 CgA: chromogranin A; sAA: salivary α-amylase; TEA: total esterase activity; BChE: butyrylcholinesterase; Lip: lipase; Hp: haptoglobin; ADA1 and 2: adenosine deaminase isoenzymes 1 and 2; CUPRAC: cupric reducing antioxidant capacity; FRAS: ferric reducing ability of saliva; UA: uric acid; AOPP: advanced oxidation protein products; Pox-Act: hydrogen peroxide; d-ROMs: reactive oxygen-derived compounds (d-ROMs); AST: aspartate aminotransferase; ALT: alanine aminotransferase; ALP: alkaline phosphatase; GGT: γ-glutamil transferase; LDH: lactate dehydrogenase; CK: creatine kinase; Ca: calcium; P: phosphorous. Statistical analysis: a different letter indicates significant differences between sampling times; asterisks indicate significant differences between sexes (*: p < 0.05; **: p < 0.01; ***: p < 0.001). Animals 2022, 12, 1865 10 of 14 Most of the analytes showed a significant decrease in their values along time. The analytes that decreased can be classified into two groups: (1) analytes that started to decrease their concentration at the end of the growing phase (sAA, TEA, BChE, oxytocin, Hp, FRAS, ALT, ALP, GGT, LDH, CK, Ca and TP); and (2) analytes that started to decrease their concentration at nursery. In this second group, two sub-groups could be differentiated: (a) analytes that increased later at growing phase, but did not reach the values at lactation (ADA1, ADA2, AOPP and AST); and (b) analytes that returned to initial values at the end of the growing phase (CgA, Lip, CUPRAC, UA and urea). On the other hand, some analytes showed significant increases with time in their concentration. These analytes can be also sub-divided into three groups: (1) those that increased and remained high at the end of the growing phase (creatinine); (2) analytes that increased at the nursery period, then returned to similar values than at lactation at the end of the growing phase (d-ROMs and glucose); and (3) analytes that increased at nursery followed by a decrease under lactation values by the end of the growing phase (cortisol, lactate and P). Finally, Pox-Act concentrations did not show significant changes between sampling times. Different values between sexes were observed in some analytes. Females showed higher values of TEA, Lip, CUPRAC, FRAS, UA, AOPP, ALT, AST, GGT, CK, urea, glucose, P and TP. Those differences were mainly seen by the end of nursery and growing phases. 3.2. Performance Data Influences on the Analytes Table 4 shows the linear regression analyses results performed with those covariates that significantly affected the salivary biomarkers. Only a small percentage of the variability of the dependent variables could be predicted by salivary biomarkers. Table 4. Linear regression analysis results obtained between some performance parameters and analytical variables measured in saliva. Predictors Dependent Model R2 Overall p Value Constant Included in the B Coefficient Predictors p Value Variables Model Birth weight 0.07 <0.001 1.253 GGT −0.08 <0.001 Weight gain None BF 0.04 0.002 2.846 LDH −0.04 0.002 BF: Back fat thickness; GGT: γ-glutamil transferase; LDH: lactate dehydrogenase. 4. Discussion In this report, a comprehensive panel of 29 analytes was measured in saliva from fattening pigs that were serially monitored throughout their productive cycle. These analytes have been previously described in other studies [13,14], but the evolution of this profile of analytes during fattening has not been evaluated. The knowledge of how biomarkers could change during the different phases of fattening would be of interest for an adequate interpretation of the results and will contribute to a wider use of these analytes for the evaluation of pig health and welfare in farm conditions. The knowledge of the values of biomarkers in saliva in a healthy state can be considered as a basis for an appropriate use of them for diagnosis and prevention. For the biomarkers’ measurement, several analytical techniques were used. Most of the analytes measured in this report were present in saliva in sufficient quantity to be quantified by spectrophotometric assays. This has some advantages since the reagents for these methods are usually not expensive and also these assays can be easily set up at the laboratory. In addition, they can be automated, allowing the measurement of large panels of analytes in a short time and with a small volume of sample. In this report, a total of 25 analytes were measured by spectrophotometric automated methods. On the other hand, other biomarkers that are present in a very low concentration require more Animals 2022, 12, 1865 11 of 14 sensitive techniques. This is the case of cortisol, CgA, Hp and oxytocin that were quantified by fluorometric methods or AlphaLISA technology, which are highly sensitive and allow the detection of analytes present in a very small concentration [16]. The volume obtained at the different sampling times was enough for the measurement of all the panels of analytes. However, growing pigs provided a high volume of saliva samples, whereas piglets at lactation were less prone to chew the sponges, and generally more time was required to obtain the sufficient volume of samples. Almost all analytes measured in this trial changed according to the productive phase of the pigs. The variations found throughout the productive stage were different depending on the analytes, but in general most of the analytes decreased their concentration at the nursery and growing phases if compared with lactation. Some of them decreased at the beginning of the nursery phase, such as CgA, TEA, BChE, Lip, oxytocin, ADA, ALT, AST, GGT, CK, urea, Ca and TP, whereas others decreased by the end of nursery or at growing phases, such as sAA, Hp, ALP and redox biomarkers such as CUPRAC, FRAS, UA and AOPP. Among the possible factors that could be involved in these variations, it could be postulated the growing of the animals as well as the dietetic changes among the different phases. Aging can affect salivary components, as it has been previously demonstrated in human saliva [27,28]. The values of the different analytes obtained in this manuscript were in line with those previously reported for healthy animals in the growing phase. The range of val- ues previously reported were: 73.6–320.0 IU/L for TEA; 0.35–0.69.4 IU/mL for sAA; 0.17–2.84 UI/mL for BChE; 17.0–598.3 IU/L for Lip; 0.25–12.00 ng/mL for oxytocin; 0.12–1.56 UI/mL for ADA1; 0.68–15.26 IU/L for ADA2; 24.5–536.0 µmol/L for CUPRAC; 32.0–885.0 µmol/L for FRAS; 0.02–1.05 mg/dL for UA; and 23.4–378.4 µmol/L for AOPP [16,29,30]. This could be considered as values for healthy growing pigs since pigs of our study did not have evidence of any disease during the experimental period. In addition, most of those analytes showed higher values in animals suffering pain or discomfort due to pathologic conditions such as lameness or rectal prolapse [29], and in a previous report, LDH showed values between 0.1–0.7 IU/mL, also similar to the values indicated in this research, and increases in this biomarker were detected in lame pigs and after applying a stress by snaring [31]. CgA values previously reported in healthy growing pigs were between 0.40–1.20 µg/mL, also being similar to the values obtained in this manuscript for animals in the same productive phase [32]. In general, and also in agreement with other reports, all analytes showed a high inter-individual variability based on the high SD and IR obtained. This fact means that if saliva is going to be used as a specimen for biomarker analysis, ideally each individual should act as its own control in order to avoid the individual differences. In our experimental conditions, there were analytes that markedly increased at the beginning of the nursery phase, such as cortisol, glucose and lactate. Weaning is highly stressful for the animals, and increases in salivary stress biomarkers have been found in the very close period to weaning [33]. Although animals were left one week for acclimation, the 1.8-fold increase in cortisol observed in our trial at the beginning of the nursery period with respect to lactation levels could indicate that stress was still present in those animals. This increase was similar to the 1.5-fold increase previously reported one day after weaning [33]. Lactate also increase due to stress [34,35] as well as glucose [36]. In addition, other reasons such as the change in pancreatic function that occurs when a change in the diet from liquid to solid occurs could influence on the values of glucose [37]. In any case, the increases found in these analytes confirm that a special care should be taken in weaning in order to try to minimize the stress that is produced in this situation. Sex of the animals also can influence results in some analytes. Enzymes such as TEA, Lip, ALT, AST, GGT, and CK, redox biomarkers such as CUPRAC, FRAS, UA and AOPP, and metabolites such as urea, glucose, P and TP were higher in females than in males in selected sampling days. Sex-related differences in the salivary proteome have been identified in humans, with 65 proteins being differently expressed between males and Animals 2022, 12, 1865 12 of 14 females [38]. Additionally, sex differences have been also found in pigs in levels of some analytes such as acute phase proteins [39,40]. Further studies should be performed to elucidate the reasons for these differences, but in any case, this raises the importance of take into consideration the sex when interpreting certain analytes in saliva. In addition, in the case of female pigs, the productive stage the animal is in should be considered. For example, salivary cortisol, BChE, Lip, ADA, Hp, AOPP and lactate have been reported to change in sows depending on the time of the gestation, showing in general a significant increase at peripartum with the exception of salivary Lip [13]. Linear regression analyses detected some relationship between selected performance parameters and some biomarkers. However, only a few percentages of the changes in performance parameters can be explained by the changes recorded in the salivary biomark- ers, maybe due to the reason that the recorded weights and BF were highly homogeneous among animals. Possibly, the inclusion of other models comparing pigs with different productive performances would yield more analytes implied. In addition, only healthy animals with a standard growth and development were included in this trial, since the objective of this report was to study changes in healthy individuals. Therefore, results were not compared with animals with poor performance parameters, pathologic conditions or under inadequate welfare conditions. This could have affected the results obtained in the regression assays, leading to the low predictive value observed for the different biomarkers. In our conditions, GGT showed a positive relationship with the birth weight of the piglets. In cows, a relationship has been found between GGT levels in serum and immunoglobulin G transference in colostrum [41], so it could be postulated that a higher GGT at birth could indicate a better immune status of the animal that could be related with birth weight. LDH had a weak negative relationship with BF at the end of the growing phase. Since LDH is present in muscle, it could be postulated higher level of this enzyme could related with higher lean mass of the animal. 5. Conclusions When a comprehensive panel of 29 analytes is measured in saliva from the same healthy fattening pigs in farm conditions during their productive cycle, differences can be found depending on the productive phase and sex. These differences can be related in some cases with performance parameters and should be taken into consideration for an appropriate interpretation of the analytes. Nevertheless, further studies should be performed including animals with compromised welfare and/or health conditions in order to be compared with healthy animals of the same sex and productive stage, in order to evaluate the potential of these analytes to act as biomarkers of these conditions. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ani12141865/s1, Table S1: Analytical methods employed and their lower limits of detection (LLOD). Author Contributions: Conceptualization and design, J.J.C., D.E., P.F., P.L. and F.T.; formal analysis, A.O.-B., M.L.-A., D.E., M.B., C.P.R., L.P.-M., E.G.-M., L.F.-M., F.T., S.M.-M.; writing—original draft preparation, A.O.-B., J.J.C. and F.T.; writing—review and editing, J.J.C., D.E., P.L., E.G.-M., L.F.-M. and F.T.; supervision, J.J.C., D.E., E.G.-M. and F.T.; project administration, P.L, J.J.C., E.G.-M.; fund- ing acquisition, P.L., J.J.C., E.G.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Union’s Horizon 2020 research and innovation pro- gram under grant agreement No. 862919. In addition, it was funded by MCIN/AEI/10.13039/501100011033 (Grant Reference PCI2020-120712-2) and European Union “NextGenerationEU”/PRTR (1st ICRAD Joint Cofund Call). A.O-B. was funded by Fundación Séneca, Región de Murcia (Spain). D.E. was funded by the postdoctoral contract “Generational renewal to promote research” of the Univer- sity of Murcia. L.F. was funded by University of Murcia through a post-doctoral grant (Margarita Salas) within the mark of “Ayudas en el marco del Programa para la Recualificación del Sistema Universitario Español” through the European Union Next Generation funds. Animals 2022, 12, 1865 13 of 14 Institutional Review Board Statement: The animal study protocol was Bioethical Commission of Murcia University according to the European Council Directives regarding the protection of animals used for experimental purposes (Approval number, 235/2016; Approval date, 25 April 2016). Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: Pablo Fuentes is currently employed by Cefu S.A. company. However, we declare no conflicts of interest with regard to the writing of this manuscript. References 1. Cerón, J. Acute Phase Proteins, Saliva and Education in Laboratory Science: An Update and Some Reflections. BMC Vet. Res. 2019, 15, 197. [CrossRef] [PubMed] 2. Lamy, E.; Mau, M. Saliva Proteomics as an Emerging, Non-Invasive Tool to Study Livestock Physiology, Nutrition and Diseases. J. Proteom. 2012, 75, 4251–4258. [CrossRef] [PubMed] 3. Maciejczyk, M.; Żukowski, P.; Zalewska, A. Salivary Biomarkers in Kidney Diseases. In Saliva in Health and Disease; Tvarijon- aviciute, A., Martínez-Subiela, S., López-Jornet, P., Lamy, E., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 193–219. ISBN 978-3-030-37681-9. 4. Merlot, E.; Mounier, A.; Prunier, A. Endocrine response of gilts to various common stressors: A comparison of indicators and methods of analysis. Physiol. Behav. 2011, 102, 259–265. [CrossRef] 5. Martínez-Miró, S.; Tecles, F.; Ramón, M.; Escribano, D.; Hernández, F.; Madrid, J.; Orengo, J.; Martínez-Subiela, S.; Manteca, X.; Cerón, J.J. Causes, consequences and biomarkers of stress in swine: An update. BMC Vet. Res. 2016, 12, 171. [CrossRef] 6. Kaiser, M.; Jacobson, M.; Andersen, P.; Bækbo, P.; Cerón, J.; Dahl, J.; Escribano, D.; Jacobsen, S. Inflammatory Markers before and after Farrowing in Healthy Sows and in Sows Affected with Postpartum Dysgalactia Syndrome. BMC Vet. Res. 2018, 14, 83. [CrossRef] 7. Contreras-Aguilar, M.; Tvarijonaviciute, A.; Monkeviciene, I.; Martín-Cuervo, M.; González-Arostegui, L.; Franco-Martínez, L.; Cerón, J.; Tecles, F.; Escribano, D. Characterization of Total Adenosine Deaminase Activity (ADA) and Its Isoenzymes in Saliva and Serum in Health and Inflammatory Conditions in Four Different Species: An Analytical and Clinical Validation Pilot Study. BMC Vet. Res. 2020, 16, 384. [CrossRef] [PubMed] 8. Rubio, C.; Mainau, E.; Cerón, J.; Contreras-Aguilar, M.; Martínez-Subiela, S.; Navarro, E.; Tecles, F.; Manteca, X.; Escribano, D. Biomarkers of Oxidative Stress in Saliva in Pigs: Analytical Validation and Changes in Lactation. BMC Vet. Res. 2019, 15, 144. [CrossRef] [PubMed] 9. Giri, U.; Nagaraj, V.; Sankaran, A.; Ramesh, R.; Rajaram, S.; Muthanandam, S.; Arumugam, S.; Santhanam, V. Sialochemical Profile in Depressive Individuals under Antidepressant Therapy: An Observational Study. J. Clin. Diagn. Res. 2018, 12, VC06–VC09. [CrossRef] 10. Miller, B.; Deutsch, O.; Redlich, M.; Konttinen, Y.; Benoliel, R.; Zaks, B.; Davidovich, E.; Palmon, A.; Aframian, D. Sialochemistry and Cortisol Levels in Patients with Sjogren’s Syndrome. Oral Dis. 2012, 18, 255–259. [CrossRef] 11. Contreras-Aguilar, M.; Escribano, D.; Martínez-Subiela, S.; Martín-Cuervo, M.; Lamy, E.; Tecles, F.; Cerón, J. Changes in Saliva Analytes in Equine Acute Abdominal Disease: A Sialochemistry Approach. BMC Vet. Res. 2019, 15, 187. [CrossRef] 12. Contreras-Aguilar, M.D.; Vallejo-Mateo, P.J.; Želvytė, R.; Tecles, F.; Rubio, C.P. Changes in Saliva Analytes Associated with Lameness in Cows: A Pilot Study. Animals 2020, 10, 2078. [CrossRef] [PubMed] 13. Contreras-Aguilar, M.; López-Arjona, M.; Martínez-Miró, S.; Escribano, D.; Hernández-Ruipérez, F.; Cerón, J.; Tecles, F. Changes in Saliva Analytes during Pregnancy, Farrowing and Lactation in Sows: A Sialochemistry Approach. Vet. J. 2021, 273, 105679. [CrossRef] [PubMed] 14. Cerón, J.J.; Contreras-Aguilar, M.D.; Escribano, D.; Martínez-Miró, S.; López-Martínez, M.J.; Ortín-Bustillo, A.; Franco-Martínez, L.; Rubio, C.P.; Muñoz-Prieto, A.; Tvarijonaviciute, A.; et al. Basics for the Potential Use of Saliva to Evaluate Stress, Inflammation, Immune System, and Redox Homeostasis in Pigs. BMC Vet. Res. 2022, 18, 81. [CrossRef] [PubMed] 15. Hoving, L.; Soede, N.; van der Peet-Schwering, C.; Graat, E.; Feitsma, H.; Kemp, B. An Increased Feed Intake during Early Pregnancy Improves Sow Body Weight Recovery and Increases Litter Size in Young Sows. J. Anim. Sci. 2011, 89, 3542–3550. [CrossRef] 16. López-Arjona, M.; Escribano, D.; Mateo, S.V.; Contreras-Aguilar, M.D.; Rubio, C.P.; Tecles, F.; Cerón, J.J.; Martínez-Subiela, S. Changes in Oxytocin Concentrations in Saliva of Pigs after a Transport and during Lairage at Slaughterhouse. Res. Vet. Sci. 2020, 133, 26–30. [CrossRef] 17. Escribano, D.; Soler, L.; Gutiérrez, A.M.; Martínez-Subiela, S.; Cerón, J.J. Measurement of Chromogranin A in Porcine Saliva: Validation of a Time-Resolved Immunofluorometric Assay and Evaluation of Its Application as a Marker of Acute Stress. Animal 2013, 7, 640–647. [CrossRef] 18. Fuentes, M.; Tecles, F.; Gutiérrez, A.; Otal, J.; Martínez-Subiela, S.; Cerón, J. Validation of an Automated Method for Salivary Alpha-Amylase Measurements in Pigs (Sus Scrofa Domesticus) and Its Application as a Stress Biomarker. J. Vet. Diagn. Investig. 2011, 23, 282–287. [CrossRef] Animals 2022, 12, 1865 14 of 14 19. Tecles, F.; Contreras-Aguilar, M.; Martínez-Miró, S.; Tvarijonaviciute, A.; Martínez-Subiela, S.; Escribano, D.; Cerón, J. Total Esterase Measurement in Saliva of Pigs: Validation of an Automated Assay, Characterization and Changes in Stress and Disease Conditions. Res. Vet. Sci. 2017, 114, 170–176. [CrossRef] 20. Tecles, F.; Escribano, D.; Martínez-Miró, S.; Hernández, F.; Contreras, M.D.; Cerón, J.J. Cholinesterase in Porcine Saliva: Analytical Characterization and Behavior after Experimental Stress. Res. Vet. Sci. 2016, 106, 23–28. [CrossRef] 21. Tecles, F.; Rubio, C.; Contreras-Aguilar, M.; López-Arjona, M.; Martínez-Miró, S.; Martínez-Subiela, S.; Cerón, J. Adenosine Deaminase Activity in Pig Saliva: Analytical Validation of Two Spectrophotometric Assays. J. Vet. Diagn. Investig. 2018, 30, 175–179. [CrossRef] 22. Campos, C.; Guzmán, R.; López-Fernández, E.; Casado, A. Evaluation of the Copper(II) Reduction Assay Using Bathocuproinedisulfonic Acid Disodium Salt for the Total Antioxidant Capacity Assessment: The CUPRAC-BCS Assay. Anal. Biochem. 2009, 392, 37–44. [CrossRef] [PubMed] 23. Benzie, I.; Strain, J. The Ferric Reducing Ability of Plasma (FRAP) as a Measure of “Antioxidant Power”: The FRAP Assay. Anal. Biochem. 1996, 239, 70–76. [CrossRef] [PubMed] 24. Witko-Sarsat, V.; Friedlander, M.; Capeillère-Blandin, C.; Nguyen-Khoa, T.; Nguyen, A.; Zingraff, J.; Jungers, P.; Descamps-Latscha, B. Advanced Oxidation Protein Products as a Novel Marker of Oxidative Stress in Uremia. Kidney Int. 1996, 49, 1304–1313. [CrossRef] 25. Tatzber, F.; Griebenow, S.; Wonisch, W.; Winkler, R. Dual Method for the Determination of Peroxidase Activity and Total Peroxides-Iodide Leads to a Significant Increase of Peroxidase Activity in Human Sera. Anal. Biochem. 2003, 316, 147–153. [CrossRef] 26. Alberti, A.; Bolognini, L.; Macciantelli, D.; Caratelli, M. The Radical Cation of N,N-Diethyl-Para-Phenylendiamine: A Possible Indicator of Oxidative Stress in Biological Samples. Res. Chem. Intermed. 2000, 26, 253–267. [CrossRef] 27. Maciejczyk, M.; Nesterowicz, M.; Szulimowska, J.; Zalewska, A. Oxidation, Glycation, and Carbamylation of Salivary Biomolecules in Healthy Children, Adults, and the Elderly: Can Saliva Be Used in the Assessment of Aging? J. Inflamm. Res. 2022, 15, 2051–2073. [CrossRef] 28. Nassar, M.; Hiraishi, N.; Islam, M.S.; Otsuki, M.; Tagami, J. Age-Related Changes in Salivary Biomarkers. J. Dent. Sci. 2014, 9, 85–90. [CrossRef] 29. Contreras-Aguilar, M.; Escribano, D.; Martínez-Miró, S.; López-Arjona, L.; Rubio, C.; Martínez-Subiela, S.; Cerón, J.; Tecles, F. Application of a Score for Evaluation of Pain, Distress and Discomfort in Pigs with Lameness and Prolapses: Correlation with Saliva Biomarkers and Severity of the Disease. Res. Vet. Sci. 2019, 126, 155–163. [CrossRef] 30. Contreras-Aguilar, M.; Escribano, D.; Martínez-Subiela, S.; Martínez-Miró, S.; Cerón, J.; Tecles, F. Changes in Alpha-Amylase Activity, Concentration and Isoforms in Pigs after an Experimental Acute Stress Model: An Exploratory Study. BMC Vet. Res. 2018, 14, 256. [CrossRef] 31. Escribano, D.; Horvatić, A.; Contreras-Aguilar, M.D.; Guillemin, N.; Cerón, J.J.; Tecles, F.; Martinez-Miró, S.; Eckersall, P.D.; Manteca, X.; Mrljak, V. Changes in Saliva Proteins in Two Conditions of Compromised Welfare in Pigs: An Experimental Induced Stress by Nose Snaring and Lameness. Res. Vet. Sci. 2019, 125, 227–234. [CrossRef] 32. Escribano, D.; Gutiérrez, A.; Tecles, F.; Cerón, J. Changes in Saliva Biomarkers of Stress and Immunity in Domestic Pigs Exposed to a Psychosocial Stressor. Res. Vet. Sci. 2015, 102, 38–44. [CrossRef] [PubMed] 33. Escribano, D.; Ko, H.; Chong, Q.; Llonch, L.; Manteca, X.; Llonch, P. Salivary Biomarkers to Monitor Stress Due to Aggression after Weaning in Piglets. Res. Vet. Sci. 2019, 123, 178–183. [CrossRef] [PubMed] 34. Ritter, M.J.; Ellis, M.; Anderson, D.B.; Curtis, S.E.; Keffaber, K.K.; Killefer, J.; McKeith, F.K.; Murphy, C.M.; Peterson, B.A. Effects of Multiple Concurrent Stressors on Rectal Temperature, Blood Acid-Base Status, and Longissimus Muscle Glycolytic Potential in Market-Weight Pigs. J. Anim. Sci. 2009, 87, 351–362. [CrossRef] 35. Hamilton, D.N.; Ellis, M.; Bertol, T.M.; Miller, K.D. Effects of Handling Intensity and Live Weight on Blood Acid-Base Status in Finishing Pigs. J. Anim. Sci. 2004, 82, 2405–2409. [CrossRef] 36. Leybyuk, L.; Rozhko, M.; Rozhko, O. Study of Salivary Stress Factor Activity in Patients with Diabetes Mellitus During the Period of Adaptation to Complete Removable Laminar Dentures. Galician Med. J. 2020, 27, E202038. [CrossRef] 37. Pierzynowski, S.G.; Westrom, B.R.; Erlanson-Albertsson, C.; Ahre’N, B.; Svendsen, J.; Karlsson, B.W. Induction of Exocrine Pancreas Maturation at Weaning in Young Developing Pigs. J. Pediatr. Gastroenterol. Nutr. 1993, 16, 287–293. [CrossRef] 38. Franco-Martínez, L.; González-Hernández, J.M.; Horvatić, A.; Guillemin, N.; Cerón, J.J.; Martínez-Subiela, S.; Sentandreu, M.Á.; Brkljačić, M.; Mrljak, V.; Tvarijonaviciute, A.; et al. Differences on Salivary Proteome at Rest and in Response to an Acute Exercise in Men and Women: A Pilot Study. J. Proteom. 2020, 214, 103629. [CrossRef] [PubMed] 39. Gutiérrez, A.M.; Montes, A.; Gutiérrez-Panizo, C.; Fuentes, P.; De La Cruz-Sánchez, E. Gender Influence on the Salivary Protein Profile of Finishing Pigs. J. Proteom. 2018, 178, 107–113. [CrossRef] 40. Piñeiro, C.; Piñeiro, M.; Morales, J.; Andrés, M.; Lorenzo, E.; del Pozo, M.; Alava, M.A.; Lampreave, F. Pig-MAP and Haptoglobin Concentration Reference Values in Swine from Commercial Farms. Vet. J. 2009, 179, 78–84. [CrossRef] 41. Perino, L.J.; Sutherland, R.L.; Woollen, N.E. Serum Gamma-Glutamyltransferase Activity and Protein Concentration at Birth and after Suckling in Calves with Adequate and Inadequate Passive Transfer of Immunoglobulin G. Am. J. Vet. Res. 1993, 54, 56–59.