The Effects of Body Condition Score and Length of Pregnancy on Blood Biochemistry and Prediction of Ovine Pregnancy Toxemia

ABSTRACT
This study was planned to evaluate the Pregnancy Toxemia (ketosis) and relationship between BCS and blood metabolites and elements in pregnant ewes. Ketosis is among the most significant health problems in high-producing dairy livestock operations (cows, sheep, and goats) at both national and global scales. Ketosis can also occur as a complication in other deficiency diseases and where a loss of appetite has occurred. In this study included a total of 50 sheep to establish changes in the values of body condition score (BCS), β-hydroxybutyrate (ВНВА) and non-esterified fatty acids (NEFA) in their blood and their relationship in the development of clinical (CK) ketosis and Pregnancy Toxemia. Divided ewes for two groups (25 thin and 25 fat) pregnant ewes to study subclinical ketosis incidence. So, blood samples were collected three times with 30 days’ interval from all ewes (at the end of 3, 4 and 5 months of pregnancy). Blood concentration of β-hydroxybutyrate (ВНВА), blood metabolites (glucose, cholesterol, protein, triglyceride, urea, and packed cell volume-PCV) and elements (calcium and magnesium) were measured. Serum BHB, glucose, total protein, triglyceride, magnesium, urea and PCV during pre-parturition period (pregnancy) were not affected by body condition of animals as fat or thin (P > 0.05), but serum cholesterol was higher in fat group (P < 0.05) and serum calcium tended to be higher in fat group during pre-parturition period (P = 0.07). Serum glucose, total protein, cholesterol, calcium, urea, magnesium and PCV concentration during pre-parturition period (pregnancy) were affected by sampling date as fat or thin animals (P < 0.01), while serum blood triglyceride and (BHB), β-hydroxybutyrate concentration were not affected by sampling times of as fat or thin (P > 0.05). Prepartum blKetosis is one metabolic disease in dairy livestock (sheep, cows and goats) that is occurring due to increase of ketone bodies (especially β-hydroxybutyrate) in blood (Samani A D. 2018). Blood β-hydroxybutyrate (BHB) level reflect the magnitude of negative energy balance (NEB) and lipid mobilization in dairy animals, therefore there are diagnostic markers for subclinical and clinical ketosis. Blood β-hydroxybutyrate in sheep with subclinical ketosis is rather changing from 0.5 mmol.l-1 to 1.6 mmol.l-1, while in clinical ketosis from 1.6 mmol.l-1 to 7 mmol.l-1. Some authors research higher β-hydroxybutyrate concentration in sheep with ketosis as compared to lactating sheep (Marutsova et al, 2018). Deviations in body condition Secord (BCS) and β-hydroxybutyrate (BHBA) in small ruminants are indicative for negative energy balance and for emergence of metabolic disorders post parturition (Andrews et al. 1997). Higher blood NEFA levels are famous in fasting sheep, twin vs. singleton pregnancy ewes (Schlumbohm et al. 2008). In dairy ewes with ketosis, blood β-hydroxybutyrate < 1.0 mmol.l-1, high serum NEFA and low blood sugar concentrations are reported (Moallem et al. 2012). Contrary these data, others (Ferris et al. 1970) did not establish any significant changes in blood glucose concentration in sheep with pregnancy toxemia. Subclinical ketosis can be detected by measuring ketone in the blood, urine or milk. Fat mobilization enhancement initially non-esterified fatty acids in the liver that with its turn is partially converted in ketone bodies, mainly β-hydroxybutyrate acid (BHBA), but also acetoacetate and acetone (Araujo, 2020). At the same time, the capacity of her rumen shrinks since her high growing lambs in the embryo take up more and more space inside setting off less space for the rumen. Insulin resistance decreases glucose supply to the maternal adipose tissue and skeletal muscle, causing lipid mobilization and an increase in the β-HBA production, leading to pregnancy toxemia (Chalmeh et al. 2021; Duehlmeier et al. 2013). Subclinical ketosis occurs mainly within the first two weeks postpartum up to 6 weeks postpartum. Most of the fetal growth occurs during the last six weeks of pregnancy with large amount of maternal glucose being directed towards the feto-placental unit, a factor that increase energy demands of the ewe )Khan et al. 2021ood biochemistry analysis can help in the early detection and subsequent prevention of pregnancy toxemia.SAMPLE COLLECTION AND PHENOTYPE RECORDING
The present study was carried out at the Khaled Poshan Research Station of University of Tabriz, Tabriz, Iran. 25 fat (body weight: 79 ± 3 kg) and 25 thin (body weight: 54 ± 3 kg) pregnant ewes with the age of 3 to 4 years were selected from a sheep herd incluted150 pregnant ewes. Body condition score was evaluated based on previous researches (Russel, 1984). During the experiment, ewes were fed concentrate in addition of pasture grazing. Blood samples of the first experiment were collected at three different times with 30 days interval at the end of 3, 4 and 5 months of pregnancy. For this reason, 5 mL blood samples were collected from the jugular vein into non-heparinized tubes, and blood serum was separated by centrifuging at 2200 rpm for 10 mints. PCV and serum traits were evaluated. Beta hydroxy butyrate was assessed using ELISA method (Randox kit, UK) and glucose, triglyceride, cholesterol, total protein, urea, manganese, and calcium were measured by spectrophotometry and related kits(Pars Azmoon, Iran).
STATISTICAL ANALYSIS
Data were analyzed as a complete randomized design with repeated measures, using the PROC MIXED procedure of SAS (9. 2). The model included treatment (fat ewes versus thin ewes), ewes within treatment, time of measurement, and the treatment×time interaction. Also, birth year was considered as covariate in this analysis. Dependent variables are beta hydroxy butyrate, Glucose, triglyceride, cholesterol, total protein, urea, calcium, and PCV. Ewes within treatments are used as error terms to test. As beta hydroxy butyrate was only measured in the samples of third sampling time, it was analyzed by GLM procedure of SAS (9.2) and birth year was considered as covariate in this analysis. All results are presented as means ± standard error of means (SEM). Differences between means were calculated for statistical significances (p<0.05).
The model was represented as follows:
Yijkl = μ + Ti + Sj + TSij + Ewek (Ti) + eijkl
Where Yijkl was the dependent variable (blood metabolites), μ is the overall mean; Ti is the effect of the treatments (i = 1, 2), Sj is the time effect (j=1, 2, 3), TSij is the effect of treatment×time, Ewek (Ti) is error terms of ewes (k=1, 2, …, 25) within treatments, and eijkl is the experimental error. Effects of the treatments were declared significant