Age and Sex-Dependent Variations in Hematological, Biochemical, and Electrocardiographic Parameters in Beagle Dogs

Age and Sex-Dependent Variations in Hematological, Biochemical, and Electrocardiographic Parameters in Beagle Dogs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Age and Sex-Dependent Variations in Hematological, Biochemical, and Electrocardiographic Parameters in Beagle Dogs Laxit Bhatt, Nilam Patel, Chintu Kotadiya, Kajal Patel, Harshida Trivedi, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5481836/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzed hematological, biochemical, and electrocardiographic parameters in 436 Beagle dogs (9-84 months old) to establish baseline reference indices by age and sex, and to evaluate the effects of these factors on physiological parameters. Age and sex significantly influence physiological parameters, necessitating comprehensive reference ranges across different age groups and sexes. Blood samples and ECG traces obtained from unanesthetized, nulliparous, and non-pregnant dogs, individually housed in controlled environments, ensured the reflection of actual functional parameters while limiting variability. Significant age-related effects were observed in all hematological parameters except ESR, while sex affected only MCH and RDW. Age-sex interactions significantly influenced several hematological metrics, with the most frequent sex-related differences observed in younger dogs (9-24 and 25-36 months-old age-groups). Biochemical analysis showed significant age effects on glucose, cholesterol, HDL, LDL, AST, ALP, CK, TP, ALB, GLB, CREAT, Phosphorous, electrolytes, GGT and TBL, while sex influenced most parameters except TP, ALB, Urea, Calcium, Phosphorous, Sodium, GGT, and TBL. Electrocardiographic analysis revealed significant age and sex effects on p-wave amplitude and PR interval, respectively, with Fridericia’s formula providing the best correction for QT intervals in comparison to Van de Water’s and Bazett’s formulas. Understanding these variations is essential for accurate clinical assessments, ensuring drug safety, and developing tailored interventions in preclinical research. Biological sciences/Zoology/Animal physiology Health sciences/Biomarkers/Diagnostic markers Dogs clinical chemistry hematology electrocardiography nonclinical safety historical control data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Beagle dogs play a crucial role in biomedical research due to their unique characteristics. Their gentle and friendly nature makes them easy to handle, which is essential for laboratory settings (Choi et al., 2011). Beagles are physiologically similar to humans, suffering from similar diseases such as diabetes, epilepsy, and cancer, making them ideal for studying these conditions (Dysko et al., 2002). Their genetic uniformity ensures consistent and reliable results in experiments. In drug development, beagles are used to test the safety and efficacy of new medications before human trials, helping identify potential side effects and appropriate dosages. They are also employed in toxicology studies to understand the effects of chemicals and other substances on the body. This research is vital for ensuring the safety of both human and veterinary medicines (NRC 1988; Schulte and Arlt 2022; Choi et al., 2011). Biomarkers are crucial in preclinical research for identifying disease mechanisms, selecting drug targets, and assessing drug safety and efficacy (Califf 2018). They provide insights into pharmacodynamics and pharmacokinetics, enabling optimal dosage and administration. Biomarkers also facilitate early disease detection and monitoring, improving treatment development (Zhao et al., 2015; CDER 2018). Additionally, they enhance cost and time efficiency by identifying ineffective drugs early in the process, thereby, significantly improving the precision and efficiency of preclinical research, leading to safer and more effective therapies (Califf 2018; Zhao et al., 2015; CDER 2018). In preclinical toxicology studies, clinical pathology parameters and electrocardiography (ECG) serve as crucial biomarkers (Guth 2007; York 2017; Califf 2018). Hematology parameters such as white blood cell count, red blood cell count, hemoglobin, and hematocrit assess the hematopoietic system’s response to toxic substances. Clinical chemistry markers like alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine evaluate liver and kidney function. Coagulation parameters, including prothrombin time (PT) and activated partial thromboplastin time (aPTT), help assess the coagulation system (Sasseville et al., 2014). ECG is used to monitor heart rate, detect arrhythmias, and measure the QT interval, which can indicate cardiotoxicity (Guth 2007; Patel et al., 2017; Shah et al., 2019). These biomarkers are essential for identifying potential toxic effects of new drugs and chemicals before clinical trials, ensuring safety and efficacy. The effect of age and sex on clinical pathology and ECG parameters in beagle dogs is significant. Age-related changes in clinical pathology parameters include variations in hematology and clinical chemistry values. For instance, older dogs often exhibit increased levels of alkaline phosphatase (ALP) and blood urea nitrogen (BUN), indicating potential liver and kidney function changes (Oklahoma veterinary specialists (webpage); Stiller et al., 2021). Sex differences also play a role, with male Beagle dogs typically showing higher red blood cell counts and hemoglobin levels compared to females (Nemeth et al., 2010). In terms of ECG parameters, age influences heart rate and the duration of various ECG intervals. Older Beagle dogs tend to have a slower heart rate and prolonged QRS duration. Sex differences are evident in ECG readings as well; males generally have higher QRS voltage and longer QRS duration than females (Murphy et al., 2022). Variations in clinical pathology and ECG parameters are crucial for accurately interpreting preclinical study results, ensuring that age and sex are considered when assessing the efficacy and safety of new drugs. Therefore, it is essential to acquire baseline data on various clinical pathology and ECG parameters of beagle dogs and evaluate the effects of age and sex on these parameters to facilitate reliable interpretation across a broad range of preclinical safety studies. Previous studies examining these parameters in beagle dogs often lack sufficient data volume or have varying environmental, husbandry, or other test conditions. This study aims to provide baseline data on different clinical pathology and ECG parameters of over 400 beagle dogs housed in controlled environments and analyze the influence of age and sex on these parameters. Such a comprehensive and reliable dataset will be vital for the biomedical research community conducting experiments on beagle dogs. A brief part of this retrospective analysis was presented at the 73 rd Indian Pharmaceutical Congress held at Hyderabad, India. Materials And Methods Animals, Husbandry and Study Design The data evaluated in this study were obtained from 436 (or fewer) beagle dogs ( Canis lupus familiaris ; 218 males and 218 females of varying age) housed individually in clean kennels located at the Canine Research Facility, Zydus Research Centre, Ahmedabad, India. Dogs were housed with visual, auditory and olfactory access to each other. Moreover, dogs had access to a play area for physical exercise, allowing them to freely run, jump, roll, sit and mix for physical, mental and social well-being. Each day, the floor of the kennels was washed with water and disinfectant solution. The following husbandry conditions were maintained: temperature:18 to 29 °C, relative humidity: 30 to 70%, 15 air changes per hour, and 12:12 h light/dark cycles. Certified pellet feed (Pedigree® feed, Mars International India Pvt. Ltd, Hyderabad, India) and drinking water (filtered by reverse osmosis followed by UV treatment) were provided ad libitum to all animals. All animal husbandry-related procedures were performed as per the SOPs applicable at the institution and as per the CPCSEA Guidelines for Laboratory Animal Facilities, Government of India (CCSEA 2018). Dogs were divided into five groups based on age at the time of sampling: 9-24 months old, 25-36 months old, 37-48 months old, 49-60 months old and 61-84 months old. A total of maximum 436 observations were analysed for 10 electrocardiographic parameters and a maximum of 400 observations were analysed for 18 hematological and 21 biochemical parameters from conscious beagle dogs. Ethics and accreditations Data were collected from various studies conducted at our organization in past few years. Clinical pathology and electrocardiography were endpoint evaluations in those studies. All of those studies were approved by the institutional animal ethics committee as well as the Committee for the Control and Supervision of Experiments on Animals (CCSEA), a statutory body under the Government of India (GoI). Details of different IAEC-approved protocol numbers are presented in Supplementary Table 1 (Table S1). The animal facility is registered with CCSEA (Facility Registration Number: 77/PO/ RcBi/SL/99/CPCSEA). Moreover, the test facility is accredited with a GLP certificate from the National GLP Compliance Monitoring Authority (NGCMA), GoI for conducting regulatory studies, and AAALAC International for animal ethics. The Clinical Pathology Laboratory (CPL) is accredited by the National Accreditation Board for Testing and Calibration Laboratories (NABL), GoI, and NGCMA, GoI. The CPL has an established in-house quality control program as well as an external quality assessment program with the College of American Pathologists (Illinois, USA). Blood sampling and analysis After overnight fasting, blood was collected from the cephalic or saphenous veins of the dogs by trained personnel. For hematology analysis, blood was collected in ready-to-use vacutainers containing K 2 -EDTA. Hematology analysis was conducted on an Advia 2120i haematology analyser (Siemens Healthineers, USA). For serum biochemistry analysis, blood was collected in ready-to-use Gel+Clot activator vacutainer tubes. Blood was allowed to clot for at least 30 minutes at room temperature before centrifugation at 4000 rpm for 10 minutes at 24°C to obtain serum. Biochemistry parameters were evaluated on a Cobas c311 biochemistry analyser (Roche Diagnostics, Switzerland). For coagulation and ESR analysis, tubes containing 3.8% w/v sodium citrate were used to collect blood. ESR estimation was performed by the Westergren method, while coagulation parameters were analysed by an ECL-412 coagulation analyser (ERBA Diagnostics Mannheim GmbH, Germany). Blood samples were analysed at CPL, Zydus Research Centre, India. All instruments were periodically calibrated and serviced. The procedures followed for analysis of different parameters were as per the SOPs applicable at the institution. Hematological and biochemical parameters Hematological analysis of whole blood was carried out on an automated hematology analyser for the following parameters: number of white blood cells (WBC), number of red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), platelets (PLT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), absolute neutrophil (Abs. NEUT), absolute lymphocyte (Abs. LYMPH), absolute monocyte (Abs. MONO), absolute eosinophil (Abs. EOSIN), absolute basophil (Abs. BASO), absolute reticulocyte (Abs. RETIC) and red cell distribution width (RDW). Prothrombin time (PT) and Activated Partial Thromboplastin Time (APTT) were analyzed by an automated coagulation analyzer. The erythrocyte sedimentation rate (ESR) was determined by utilizing a Westergren tube. Biochemical analysis of serum was carried out on an automated biochemistry analyzer for the following parameters: glucose (GLU), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), creatine kinase (CK), total protein (TP), albumin (ALB), globulin (GLB), urea (UREA), creatinine (CREA), calcium (Ca), inorganic phosphorous (Phos), sodium (Na + ), potassium (K + ), chloride (Cl - ), total bilirubin (TBIL), and Gamma Glutamyl Transferase (GGT). The units and method of analysis for all hematological and biochemical parameters are described in the Supplementary Table 2 (Table S2). Electrocardiographic parameters Snapshot ECGs were recorded from conscious beagle dogs in the standing or lateral recumbency position. To improve conduction between electrodes and the skin of the animal, inert electrode gel was applied at the site of the four limb electrodes and one chest electrode before electrode attachment. The dogs were allowed to settle in the restrained position before recording the ECGs. An electrocardiograph machine, CARDIOVIT AT-1(VET) (Schiller AG, Switzerland, Software version: 8.02) was used to record ECG traces at 5 mm/mV sensitivity and 25 mm/s paper speed. Twelve lead ECGs were recorded; HR and ECG intervals were determined from lead II. The electrocardiographic parameters analyzed were as follows: heart rate, RR interval, P-wave amplitude and duration, PR interval, QRS interval and QT interval. QTc interval (corrected QT interval) was calculated by using three different QTc formulas: QTc(B) (Bazett’s formula), QTc(F) (Fridericia’s formula) and QTc(V) (Van de Water’s formula). Statistical Analysis A two-way unbalanced analysis of variance (ANOVA) was used to evaluate the effects of age, sex and age-sex interaction using Statistical Analysis Software (version 9.4; Cary, NC, USA). The difference amongst sexes was evaluated by two-sample t-test. P-value <0.05 was considered statistically significant. Linear regression analysis was performed to evaluate the correlation between HR and QT/QTc intervals. Pearson’s correlation coefficient determined the direction and extent of the linear relationship between the two variables. QTc-HR scatter plots were generated and the slope of the QTc-HR regression line for each QTc formula was determined and used to compare QTc formulae. Results Reference ranges for hematological, biochemical and electrocardiographic parameters The references values and range for hematological, biochemical and electrocardiographic parameters by sex and age are reported in the Supplementary Tables (Tables S3 to S8). Hematological parameters of beagle dogs A total of 18 hematological parameters from 400 healthy beagle dogs were analysed, compiled and reported. Age exerted a significant effect on all hematological parameters except ESR (Table 1, Figures 1-3). Sex and gender affect manifestation, epidemiology and pathophysiology of diseases. Sex and gender are essential in developing intervention, treatment and preventive strategies for various health initiatives (Regitz‐Zagrosek, 2012). In this study, sex was found to affect only MCH and RDW parameters (Table 1, Figures 1-3). Age-sex interaction showed significant effects on RBC, HGB, HCT, MCH. MCHC, PLT, Absolute Lymphocytes, Absolute Reticulocytes, PT and RDW parameters (Table 1, Figures 1-3). Most frequent sex-related significant differences were observed in 9-24 months old and 25-36 months old groups of beagle dogs for RBC, HGB, HCT, MCH, MCHC, Abs. Neutrophils, Abs. Lymphocytes, Abs. Monocytes, Platelets and PT. Biochemical parameters of beagle dogs The results of age, sex, and age-sex interaction on various biochemical parameters are reported in Table 2. Age exerted a significant effect on glucose, cholesterol, HDL, LDL, AST, ALP, CK, TP, ALB, GLB, CREAT, Phosphorous, electrolytes, GGT and TBL. Significant effects of sex were observed on all biochemical parameters except TP, ALB, Urea, Calcium, Phosphorous, Sodium, GGT and TBL. Significant effects of age-sex interaction were displayed by LDL, CK, ALB, Urea, Phosphorous, Sodium, Chloride, and TBL. For LDL, sex-based significance was observed at all age groups except 49-60 months old. For other biochemical parameters, sex-dependent significance was observed for varying age groups, however, most frequent observations were made for 25-36 months old (TC, HDL, LDL, AST, ALT, Urea, ALB, GLB, Calcium, Phosphorous, Sodium, and Potassium) (Figures 4-8). Electrocardiographic parameters of beagle dogs A total of 10 electrocardiographic parameters were estimated from 436 healthy beagle dogs. Age and sex were found to exert significant effect on p-wave amplitude and PR interval respectively, while age-sex interaction did not reveal significant effects on any ECG parameters (Table 3). Although the occurrence of sex-based significance amongst individual age groups was random, it was observed in all parameters. All the corrected QT intervals viz, QTc (B), QTc (V) and QTc (F) did not display any significance amongst age or sex (Figures 9 and 10). Regression analysis of the corrected QT interval for all samples showed that QTc (F) better corrected the QT interval for changes in heart rate when compared to QTc (B) and QTc (V) (Figure 11). Pearson correlation coefficient (r) and the slope of the regression line were estimated to compare the QT correction formulas. The values of r and slope that are closest to zero indicate least correlation and best QT correction. The slope and r-value for different formulas are reported in Table 4. Discussion In this study, hematological, biochemical and electrocardiographic analyses of 436 beagle dogs were performed in order to establish baseline reference indices by age and sex and to determine the effects of age and sex on these physiological parameters. Dogs share about 82% homology with humans among other anatomical and physiological similarities, making them a widely employed non-rodent animal model of choice in biomedical research. Variable Factors in Reference Indices As previously published reports have shown that age and sex are the two primary factors affecting different physiological parameters in dogs (Montoya Navarrete et al., 2021; Alilovic et al., 2022), it is essential to develop a comprehensive reference range of blood-based and ECG parameters across different age groups by sex. However, in addition to age and sex, blood and ECG parameters can also be affected by other factors such as anesthetics, fasting status, pregnancy status, animal husbandry, etc. Thus, different experimental conditions must be considered before interpreting results from different studies. The blood samples collected and ECG traces recorded in our study were obtained from unanesthetized animals as they better reflect the actual functional parameters of these animals. General anesthetics are known to affect blood and ECG parameters. Certain examples of anesthetic-induced changes in normal blood and ECG parameters are mentioned here: xylazine-propofol anesthesia causes a decrease in hemoglobin, packed cell volume, and total erythrocyte count, while increasing total leukocyte count, glucose, BUN, creatinine, AST, and ALT in dogs (Dewangan, 2016). Ketamine-xylazine anesthesia causes an increase in glucose and CK and a decrease in TP values (Çamkerten, 2013). Propofol and alfaxalone cause QTc prolongation in dogs. Along with QTc prolongation, these anesthetics significantly impact heart rate, RR, PR, and QRS intervals, causing depression of the ST segment (Casoria et al., 2024). Cardiac effects such as a reduction of the PR and QT intervals and an increase in heart rate were noticed in dogs treated with the combination of atropine, tiletamine, and zolazepam (ATZ), while treatment with atropine, levomeprazine, thiopental, and halothane (ALTH) caused qualitative modifications of the ST segment, T wave, and cardiac rhythm in experimental dogs (Tárraga et al., 2000). The American Association for Clinical Chemistry’s Division of Animal Clinical Chemistry (AACC-DACC) and the American Society for Veterinary Clinical Pathology’s (ASVCP) Joint Committee on Clinical Pathology Testing of Laboratory Species recommend 12-18 hours of overnight fasting for laboratory animal species prior to blood collection. The primary rationale for fasting research animals is to reduce variability in analytes such as glucose and triglycerides, which are highly sensitive to fed or fasting conditions (Weingand et al., 1996). Some studies have reported variance in analyte concentrations of alkaline phosphatase, urea, glucose, creatinine, and plasma proteins (Gauvin et al., 2024). Thus, when comparing analyte results between different studies, food intake should be considered. The animals employed in this analysis were housed individually within a controlled environment and with physical access to each other for social well-being, thereby limiting any variability possibly induced by varying animal husbandry procedures. Additionally, these animals were nulliparous and non-pregnant. Beagle dogs are reported to have marked differences in physiological blood parameters of pregnant and non-pregnant dogs, such as the number of erythrocytes, hematocrit and hemoglobin levels, total leukocyte count, and relative values of neutrophils and lymphocytes, serum cholesterol, HDL, and LDL concentrations (Kockaya, 2019; Dimço et al., 2013). Hence, experimental conditions presented in the current study must be considered prior to applying this reported reference range for various blood and ECG parameters. Hematological and Biochemical Parameters Monitoring hematological and biochemical parameters during preclinical toxicology studies is essential for several reasons (NRC 1988). These parameters enable early detection of toxic effects on physiological systems, such as bone marrow suppression or liver and kidney dysfunction. They provide a comprehensive overview of the dogs’ health, identifying underlying issues that could affect study outcomes. Specific changes can pinpoint organ-specific toxicity, like elevated liver enzymes indicating hepatic damage. Additionally, these parameters help establish dose-response relationships, determining safe dosage ranges for new compounds (OECD Section 4). Comparative analysis between treated and control groups ensures the reliability of study results, which is vital for regulatory submissions and the overall assessment of new pharmaceutical compounds’ safety and efficacy (ICH M3 (R2), 2009; Eaton and Gilbert, 2013). Age and sex-based differences were apparent in different hematological and biochemical parameters in this study. As evident from the results, most sex-based significant changes were noted in 9-24 months and 25-36 months age group. Parameters such as RBC, HGB, HCT, MCH, MCHC, #Neutrophils, #Lymphocytes, #Monocytes, Platelets, PT, ESR, Glucose, TG, TC, HDL, LDL, AST, ALT, TBIL, Urea, Creatinine, CK, Albumin, Globulin, Calcium, Phosphorous, Sodium, and Potassium displayed significant differences in male and female animals. Red blood cell counts, hemoglobin concentration, and hematocrit are clinical biomarkers of anaemia, internal hemorrhage and other blood disorders and are associated with increased risk of cardiovascular diseases (Kishimoto et al. 2020, Xie et al. 2013). Sex-based difference in these RBC parameters observed in the current study are in line with gender-based difference in RBC parameters in humans (Grau et. al. 2018). Although previous studies have reported age-related statistical significance in RBC parameters of beagle dogs, data regarding sex-based significance in beagle dogs is scarcely available. While some researchers have found higher RBC parameter values for male animals, some studies have reported no statistical significance between both sexes (Khan et. al. 2011). In the current study, it was found that at lower age, female animals had higher values of RBC parameters than males. However, with increasing age, the values of RBC parameters in male animals increased compared to the female animals of respective age groups. In dogs, younger individuals generally exhibit higher levels of red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) compared to older dogs, with males having higher levels than females. Mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) tend to decrease with age, but show minimal sex-based differences (Lee et al., 2020; Oo et al., 2017). Younger dogs typically have higher neutrophil and lymphocyte counts, while older dogs may have increased monocyte levels; female dogs often have higher lymphocyte counts than males. Platelet counts decrease with age, with females usually having higher counts than males (Canine cbc variations with age (webpage), n.d.; Kocaturk et al., 2024; Lee et al., 2020). Prothrombin time (PT) levels increase with age, indicating longer clotting times in older dogs, but show no significant sex-based differences (Oo et al., 2017; Lee et al., 2020). These variations highlight the need to consider both age and sex for accurate clinical assessments. Serum glucose and lipid parameters such as total cholesterol, HDL and LDL reported significant differences between male and female animals. In dogs, age and sex influence serum glucose and lipid parameters. Younger dogs tend to have higher glucose levels, while older dogs often show higher total cholesterol (TC) levels. HDL levels are higher in younger dogs, whereas LDL levels increase with age. Female dogs generally have higher fasting plasma glucose (FPG), total cholesterol, HDL, and LDL levels compared to males. These differences are influenced by hormonal factors, body composition, genetic makeup, and metabolic regulation. Estrogen enhances insulin sensitivity and lipid metabolism, while testosterone affects muscle mass and fat distribution. Males typically have more muscle mass, and females have higher body fat percentages, impacting glucose and lipid metabolism. Genetic differences also play a role in how these parameters are processed (Xenoulis et al., 2020; Kawasumi et al., 2014; Montoya Navarrete et al., 2021). These differences emphasize the importance of considering both age and sex for accurate diagnosis and treatment. When evaluating liver function tests, the focus is on several key enzymes and proteins: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT). ALT is found in the cytosol, whereas AST exists in both cytosolic and mitochondrial forms. Additionally, bilirubin levels (total, conjugated, and unconjugated), prothrombin time (PT), total protein, globulins, and albumin are assessed (Kwo et al., 2017; Johnston 1999). These tests help identify the specific area of liver damage, and the pattern of elevation aids in forming a differential diagnosis. Puppies generally have lower levels of AST, ALT, TBIL, albumin, and globulin compared to adult dogs due to their developing liver function and protein synthesis capabilities. However, sex-based differences in liver enzymes and protein levels in dogs are generally minimal and not clinically significant. Both male and female dogs can show variations in AST (Aspartate Aminotransferase) and ALT (Alanine Aminotransferase) levels. Male dogs may have a slightly higher threshold for bilirubin resorption, leading to more common mild bilirubinuria compared to females. There are no significant sex-based differences in albumin and globulin levels, which are crucial proteins produced by the liver (estaff (webpage) 2013; Montoya Navarrete et al., 2021; Lee et al., 2020). Sex and age-based differences in urea, creatinine, and CK levels in dogs are notable. Puppies generally have lower urea and creatinine levels due to their developing kidneys and lower muscle mass, while no significant sex differences exist for these markers. Younger dogs tend to have higher CK levels because of increased muscle activity and growth, and male dogs may exhibit slightly higher CK levels compared to females due to greater muscle mass (Dog kidney failure (webpage), 2013; Montoya Navarrete et al., 2021; Lee et al., 2020). These variations underscore the importance of considering both age and sex when interpreting biochemical markers in dogs. In dogs, age and sex influence levels of key electrolytes. Puppies generally have lower calcium levels due to developing bones and metabolism, while younger dogs have higher phosphorous levels essential for bone growth. Sodium and potassium levels are relatively stable across different age groups, with slight variations in potassium due to diet and health. There are no significant differences in calcium, phosphorous, sodium, and potassium levels between male and female dogs (Meller et al., 1984; Lee et al., 2020; Koek et al., 2021). These variations highlight the importance of considering both age and sex when interpreting these electrolyte levels in dogs. Electrocardiographic Parameters The utilization of Beagle dogs in translational research serves as a crucial bridge between animal models and human clinical trials. These dogs are frequently employed in preclinical studies to evaluate the cardiac safety of new drugs (Hanton and Rabemampianina, 2006). By understanding the electrocardiographic (ECG) patterns of Beagle dogs, researchers can identify potential cardiac side effects early in the drug development process. Age and sex significantly influence ECG parameters in Beagle dogs. Younger Beagle dogs typically exhibit higher heart rates compared to older dogs, with sinus arrhythmia more commonly observed in the latter. Although aging can result in changes to the duration and amplitude of ECG waves and complexes, these changes are generally not significant in toxicological or pharmacological studies since most subjects are young adults. There are no significant differences in ECG parameters between male and female Beagle dogs, with both sexes showing similar heart rates, PQ intervals, and QT intervals. While the heart axis may shift slightly based on body position during ECG recording, this shift is not significantly influenced by sex (Murphy et al., 2022; Mukherjee et al., 2020; Eckenfels and Trieb, 1979; Lerdweeraphon et al., 2020). Comparing ECG patterns between Beagle dogs and humans reveals some interesting differences and similarities. In humans, aging is associated with notable changes in ECG parameters, such as increased PR interval, QRS duration, and QT interval, with older adults often exhibiting a higher prevalence of arrhythmias, including atrial fibrillation. Similarly, older Beagle dogs show changes in the duration and amplitude of ECG waves, and a higher incidence of sinus arrhythmia compared to younger dogs. However, unlike humans, Beagle dogs do not exhibit significant sex-based differences in ECG parameters (Chavan et al., 2022; Eckenfels and Trieb, 1979; Hanton and Rabemampianina, 2006). Understanding these differences is crucial for accurately interpreting ECG results in both veterinary and human medical contexts. The QT interval represents the time required for the heart’s electrical system to depolarize and repolarize (Wiśniowska et al., 2016). Prolongation of the QT interval can indicate a risk of potentially life-threatening arrhythmias. Assessing the QT interval and its correction (QTc) is vital in preclinical studies to ensure safety by identifying proarrhythmic effects of new drugs, which can lead to serious arrhythmias like Torsades de Pointes. Regulatory bodies such as the FDA and EMA mandate thorough QT (TQT) studies to determine a drug's potential for QT prolongation. Understanding a drug's cardiac safety profile early in the development process can guide formulation and dosing decisions, thereby saving time and resources. Preclinical studies also help identify drug-drug interactions that may prolong the QT interval, ensuring safer combination therapies (Lester et al., 2019; Bhatt et al., 2023). Understanding the effects of age and sex on QT correction formulas in Beagle dogs is essential in veterinary cardiology. Male dogs typically have shorter QT intervals compared to females, likely due to hormonal differences (Agudelo et al., 2011). Younger dogs usually have faster heart rates and shorter QT intervals, which lengthen with age due to physiological changes in the heart. For older dogs, the Fridericia formula is preferred for its stability across various heart rates (Koyama et al., 2004; Agudelo et al., 2011). Slope and R-value are critical for understanding relationships between variables in statistical analyses. The slope indicates the rate of change in the dependent variable per unit increase in the independent variable, reflecting the direction and magnitude of the relationship. The r-value, or correlation coefficient, measures the strength and direction of the linear relationship between two variables, ranging from -1 to 1 (Bhatt et al., 2023). An effective QT correction formula should yield a QT value independent of heart rate, assessed by determining the correlation between corrected QT values and heart rate or RR intervals. The current study demonstrated that Fridericia’s formula provided corrected QT values that exhibited the least dependence on heart rate changes, with slope values for QTc (F), QTc (V), and QTc (B) measured at 0.0685, -0.1035, and 0.4778, respectively, and corresponding r-values of 0.08, -0.15, and 0.47. These results, which contrast with previous findings (Patel et al., 2017), indicate that Fridericia’s formula more effectively corrects the QT interval in Beagle dogs. Notably, Fridericia’s formula showed superior QT correction in male dogs, whereas Van de Water’s formula was more effective in female dogs. The study also observed variability in the effectiveness of QT correction formulas across different age groups. QTc (V) was the most effective formula for dogs aged 9-24 months, while QTc (F) was superior for both sexes in the 25-36 months age group. For dogs aged 37-84 months, the performance of QT correction formulas varied by age group and sex, suggesting that these variations may be attributable to the smaller sample size. Additionally, the study assessed the ability of the three QT formulas to correct QT intervals at heart rates lower and higher than the mean HR (112 bpm). For datasets with HR ≤ 112 bpm, QTc (V) provided better correction, while for datasets with HR > 112 bpm, QTc (F) demonstrated superior HR-correction capability. These findings emphasize the need for tailored QT correction formulas based on age, sex, and heart rate variations to ensure accurate assessment of cardiac safety in preclinical studies. Conclusion Age and sex significantly influence various hematological, biochemical, and electrocardiographic parameters in Beagle dogs. Younger Beagle dogs generally exhibit higher levels of red blood cells, hemoglobin, and hematocrit compared to older dogs, with males showing higher values than females as they age. Significant sex-based differences are observed in parameters such as glucose, cholesterol, and liver enzymes, with females often having higher levels of fasting plasma glucose and lipids. While aging affects the duration and amplitude of ECG waves, these changes are generally not significant in young adult subjects used in studies. There are no significant sex-based differences in ECG parameters, making both male and female Beagle dogs suitable for cardiac studies. When comparing Beagle dogs to humans, it is observed that while both species exhibit age-related changes in ECG parameters, sex-based differences are more pronounced in humans. These variations underscore the importance of considering both age and sex for accurate clinical assessments, diagnosis, and treatment in veterinary practice. Declarations DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ANIMAL ETHICS STATEMENT As this is a retrospective analysis, no animals were used in this study. FUNDING This study did not receive any funds. ACKNOWLEDGEMENTS The authors are grateful to the management and scientific team at Zydus Research Centre, India, for their assistance in preparing this manuscript. DECLARATION OF INTERESTS The authors declare that they have no conflicts of interests. AUTHOR CONTRIBUTIONS Laxit K Bhatt: Conceptualization, Formal Analysis, Investigation, Writing - Original Draft, Writing - Review & Editing. Nilam R. Patel: Data Curation, Formal Analysis, Investigation. Chintu Kotadiya: Visualization, Validation, Methodology, Writing - Original Draft. Kajal G. Patel: Data Curation, Formal Analysis, Investigation Harshida J. Trivedi: Data Curation, Methodology, Validation Tushar Patel: Data Curation, Methodology, Validation Chitrang R. Shah: Formal Analysis, Investigation, Methodology, Writing - Original Draft, Writing - Review & Editing. Sudhir R. Patel Formal Analysis, Investigation, Methodology. Vipul A. Patel: Methodology, Writing - Review & Editing. Shital D. Patel: Visualization, Validation, Writing - Review & Editing. Rajesh J. Patel: Formal Analysis, Writing - Original Draft, Writing - Review & Editing. Sunny Kumar: Formal Analysis, Software, Data Curation. 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Tables Table 1: Effect of age and sex on hematologic parameters of beagle dogs Parameters Age Sex Age-Sex Interaction WBC F(4, 400) = 7.26, P = <0.0001, S F(1, 400) = 0.99, P = 0.3208 F(4, 400) = 0.49, P = 0.7416 RBC F(4, 400) = 10.74, P = <0.0001, S F(1, 400) = 0.66, P = 0.417 F(4, 400) = 6.12, P = <0.0001, S HGB F(4, 400) = 9.97, P = <0.0001, S F(1, 400) = 0.17, P = 0.068 F(4, 400) = 5.54, P = 0.0002, S HCT F(4, 400) = 10.89, P = <0.0001, S F(1, 400) = 1.17, P = 0.28 F(4, 400) = 4.98, P = 0.0006, S MCV F(4, 400) = 4.78, P = 0.0009, S F(1, 400) = 1.66, P = 0.198 F(4, 400) = 2.29, P = 0.059 MCH F(4, 400) = 2.64, P = 0.034, S F(1, 400) = 4.27, P = 0.0039, S F(4, 400) = 3.57, P = 0.007, S MCHC F(4, 400) = 2.62, P = 0.035, S F(1, 400) = 2.09, P = 0.149 F(4, 400) = 5.3, P = 0.00039, S PLT F(4, 400) = 24.74, P = <0.0001, S F(1, 400) = 3.32, P = 0.069 F(4, 400) = 7.29, P = <0.0001, S Abs. Neutrophils F(4, 400) = 5.19, P = 0.00039, S F(1, 400) = 2.48, P = 0.116 F(4, 400) = 1.32, P = 0.1954 Abs. Lymphocytes F(4, 400) = 15.07, P = <0.0001, S F(1, 400) = 1.66, P = 0.198 F(4, 400) = 3.34, P = 0.011, S Abs. Monocytes F(4, 400) = 9.81, P = <0.0001, S F(1, 400) = 0.39, P = 0.531 F(4, 400) = 1.12, P = 0.346 Abs. Eosinophils F(4, 400) = 10.27, P = <0.0001, S F(1, 400) = 0.41, P = 0.521 F(4, 400) = 0.89, P = 0.472 Abs. Basophils F(4, 400) = 4.87, P = 0.00079, S F(1, 400) = 0.3, P = 0.586 F(4, 400) = 0.24, P = 0.914 Abs. Reticulocytes F(4, 400) = 21.29, P = <0.0001, S F(1, 400) = 3.52, P = 0.062 F(4, 400) = 2.53, P = 0.04, S PT F(4, 400) = 78.82, P = <0.0001, S F(1, 400) = 0.18, P = 0.672 F(4, 400) = 3.11, P = 0.016, S APTT F(4, 400) = 17.29, P = <0.0001, S F(1, 400) = 2.46, P = 0.117 F(4, 400) = 0.62, P = 0.65 ESR F(4, 400) = 1.56, P = 0.184 F(1, 400) = 0.15, P = 0.7 F(4, 400) = 1.02, P = 0.399 RDW F(3, 152) = 22.52, P = <0.0001, S F(1, 400) = 4.3, P = 0.04, S F(3, 400) = 5.17, P = 0.002, S P < 0.05 is significant (S). Table 2: Effect of age and sex on biochemical parameters of beagle dogs Parameters Age Sex Age-Sex Interaction Glucose F(4, 400) = 4.19, P = 0.003, S F(1, 400) = 5.89, P = 0.016, S F(4, 400) = 0.36, P = 0.837 Triglycerides F(4, 400) = 1.78, P = 0.132 F(1, 400) = 22.98, P = <0.0001, S F(4, 400) = 1.44, P = 0.22 Cholesterol F(4, 400) = 6.17, P = <0.0001, S F(1, 400) = 29.18, P = <0.0001, S F(4, 400) = 1.87, P = 0.115 HDL F(4, 400) = 10.37, P = <0.0001, S F(1, 400) = 10.11, P = 0.002, S F(4, 400) = 2.14, P = 0.076 LDL F(4, 400) = 5.86, P = 0.00009, S F(1, 400) = 30, P = <0.0001, S F(4, 400) = 2.54, P = 0.04, S AST F(4, 400) = 5.48, P = 0.0003, S F(1, 400) = 23.26, P = <0.0001, S F(4, 400) = 1.05, P = 0.383 ALT F(4, 400) = 1.9, P = 0.109 F(1, 400) = 10.91, P = 0.001, S F(4, 400) = 0.3, P = 0.881 ALP F(4, 400) = 5.04, P = 0.0006, S F(1, 400) = 7.26, P = 0.007, S F(4, 400) = 2.14, P = 0.075 CK F(4, 400) = 10.85, P = <0.0001, S F(1, 400) = 3.93, P = 0.048, S F(4, 400) = 2.49, P = 0.043, S TP F(4, 400) = 17.36, P = <0.0001, S F(1, 400) = 0, P = 0.976 F(4, 400) = 2.07, P = 0.084 ALB F(4, 400) = 3.51, P = 0.008, S F(1, 400) = 3.37, P = 0.067 F(4, 400) = 3.09, P = 0.016, S GLB F(4, 400) = 13.41, P = <0.0001, S F(1, 400) = 5.27, P = 0.022, S F(4, 400) = 1.67, P = 0.156 UREA F(4, 400) = 2.24, P = 0.064 F(1, 400) = 3.14, P = 0.077 F(4, 400) = 4.24, P = 0.002, S CREAT F(4, 400) = 5.98, P = 0.00009, S F(1, 400) = 29.43, P = <0.0001, S F(4, 400) = 2.06, P = 0.086 CALCIUM F(4, 400) = 1.75, P = 0.137 F(1, 400) = 0.74, P = 0.391 F(4, 400) = 2.79, P = 0.026 PHOS F(4, 400) = 5.04, P = 0.0006, S F(1, 400) = 1.34, P = 0.249 F(4, 400) = 4.3, P = 0.002, S SODIUM F(4, 400) = 16.1, P = <0.0001, S F(1, 400) = 1.27, P = 0.26 F(4, 400) = 11.24, P = <0.0001, S POTASSIUM F(4, 400) = 11.85, P = <0.0001, S F(1, 400) = 4.59, P = 0.033, S F(4, 400) = 1.15, P = 0.3334 CHLORIDE F(4, 152) = 9.3, P = <0.0001, S F(1, 400) = 5.09, P = 0.025, S F(4, 400) = 3.46, P = 0.009, S GGT F(4, 189) = 2.87, P = 0.025, S F(1, 189) = 0.49, P = 0.486 F(4, 189) = 0.71, P = 0.587 TBL F(3, 105) = 20.93, P = <0.0001, S F(1, 105) = 0, P = 0.967 F(3, 105) = 3.16, P = 0.028, S P < 0.05 is significant (S). Table 3: Effect of age and sex on electrocardiographic parameters Parameters Age Sex Age-Sex Interaction Heart Rate F(4, 436) = 1.42, P = 0.2273 F(1, 436) = 1.13, P = 0.2891 F(4, 436) = 1.83, P = 0.1222 RR interval F(4, 436) = 1.38, P = 0.2390 F(1, 436) = 0.68, P = 0.4084 F(4, 436) = 1.96, P = 0.1002 P-wave amplitude F(4, 436) = 3.53, P = 0.0076 , S F(1, 436) = 3.48, P = 0.0627 F(4, 436) = 1.38, P = 0.2390 P- wave duration F(4, 436) = 1.07, P = 0.3693 F(1, 436) = 2.85, P = 0.0924 F(4, 436) = 1.60, P = 0.1743 QRS interval F(4, 436) = 1.56, P = 0.1834 F(1, 436) = 1.50, P = 0.2212 F(4, 436) = 1.03, P = 0.3935 PR interval F(4, 436) = 2.35, P = 0.0540 F(1, 436) = 8.58, P = 0.0036 , S F(4, 436) = 2.31, P = 0.0572 QT interval F(4, 436) = 0.75, P = 0.5614 F(1, 436) = 2.62, P = 0.1065 F(4, 436) = 1.96, P = 0.0996 QTc (B) F(4, 436) = 1.73, P = 0.1430 F(1, 436) = 0.25, P = 0.6151 F(4, 436) = 1.37, P = 0.2419 QTc (F) F(4, 436) = 1.38, P = 0.2390 F(1, 436) = 1.19, P = 0.2754 F(4, 436) = 1.52, P = 0.1954 QTc (V) F(4, 436) = 1.20, P = 0.3107 F(1, 436) = 2.15, P = 0.1432 F(4, 436) = 1.71, P = 0.1469 P < 0.05 is significant (S). Table 4: QT correction in beagle dogs Parameters Heart Rate Uncorrected QT QTc (B) QTc (F) QTc (V) Mean (bpm) Mean (ms) Slope R-value Mean (ms) Slope R-value Mean (ms) Slope R-value Mean (ms) Slope R-value SEX All data (n=436) 112 200 -0.5391 -0.63 271 0.4778 0.47 245 0.0685 0.08 239 -0.1035 -0.15 Male (n=218) 111 201 -0.5885 -0.63 270 0.4167 0.39 245 0.0125 0.01 239 -0.1466 -0.19 Female (n=218) 114 199 -0.4850 -0.64 272 0.5425 0.56 245 0.1284 0.18 239 -0.0569 -0.10 AGE 9-24 months All data (n=234) 110 200 -0.5172 -0.66 269 0.5168 0.54 244 0.1028 0.14 238 -0.0683 -0.11 Male (n=130) 108 201 -0.5314 -0.69 267 0.5062 0.54 242 0.0923 0.13 238 -0.0707 -0.12 Female (n=104) 113 200 -0.5121 -0.64 272 0.5089 0.51 245 0.0976 0.13 239 -0.0809 -0.13 25-36 months All data (n=104) 116 199 -0.6192 -0.61 274 0.3533 0.33 246 -0.0419 -0.04 239 -0.2040 -0.24 Male (n=59) 117 199 -0.6978 -0.58 276 0.2617 0.21 247 -0.1293 -0.11 240 -0.2846 -0.28 Female (n=45) 114 199 -0.5327 -0.73 272 0.4528 0.55 244 0.0536 0.09 238 -0.1155 -0.22 37-48 months All data (n=53) 115 198 -0.5448 -0.60 273 0.4538 0.42 245 0.0480 0.05 239 -0.1361 -0.19 Male (n=8) 104 212 -0.8801 -0.63 277 0.0577 0.04 254 -0.3222 -0.23 248 -0.4405 -0.37 Female (n=45) 117 196 -0.4273 -0.56 272 0.5948 0.57 244 0.1797 0.23 237 -0.0254 -0.04 49-60 months All data (n=27) 113 200 -0.3792 -0.51 274 0.7110 0.62 247 0.2759 0.33 240 0.0755 0.11 Male (n=12) 118 197 -0.7522 -0.70 274 0.0969 0.09 246 -0.2550 -0.26 239 -0.3685 -0.43 Female (n=15) 110 203 -0.1309 -0.30 273 1.1184 0.90 247 0.6273 0.79 241 0.3627 0.66 61-84 months All data (n=18) 113 204 -0.6598 -0.79 278 0.3112 0.40 250 -0.0835 -0.13 244 -0.2427 -0.42 Male (n=9) 109 208 -0.7675 -0.70 279 0.0904 0.08 253 -0.2649 -0.26 246 -0.3824 -0.43 Female (n=9) 117 200 -0.5677 -0.92 277 0.4844 0.82 248 0.0619 0.20 241 -0.1255 -0.48 ABOVE AND BELOW MEAN HEART RATE Heart Rate ≤ 112 bpm All data (n=218) 97 209 -0.3986 -0.27 265 0.9102 0.45 244 0.4119 0.24 241 0.2220 0.15 Male (n=118) 96 210 -0.4450 -0.26 265 0.8655 0.38 245 0.3672 0.19 242 0.1831 0.11 Female (n=100) 98 208 -0.3323 -0.29 264 0.9776 0.57 244 0.4783 0.34 241 0.2801 0.24 Heart Rate > 112 bpm All data (n=218) 128 191 -0.5099 -0.51 278 0.3150 0.25 245 -0.0359 -0.03 237 -0.2050 -0.23 Male (n=100) 128 190 -0.6018 -0.59 277 0.1770 0.15 244 -0.1558 -0.15 236 -0.2931 -0.34 Female (n=118) 128 191 -0.4271 -0.44 279 0.4396 0.33 246 0.0722 0.07 237 -0.1254 -0.14 bpm: beats per minute, n: number of samples, ms: milliseconds, QTc (B): Corrected QT interval by Bazett’s formula, QTc (F): Corrected QT interval by Fridericia’s formula, QTc (V): Corrected QT interval by Van de Water’s formula Additional Declarations There is NO Competing Interest. Supplementary Files TableS1IAECProtocolNumbers.docx Supp Table_ethics approval numbers ST1CPLParametersandunits.docx Abbreviations, units of measurements and methods of analysis of different hematological and biochemical parameters. ST2DescriptiveSTAThematologymale.docx Hematological parameters of male beagle dogs ST3DescriptiveSTAThematologyfemale.docx Hematological parameters of female beagle dogs ST4DescriptiveSTATbiochemistrymale.docx Biochemical parameters for male beagle dogs ST5DescriptiveSTATbiochemistryfemale.docx Biochemical parameters for female beagle dogs ST6DescriptiveSTATECGMale.docx Electrocardiographic reference values of male beagle dogs ST7DescriptiveSTATECGFemale.docx Electrocardiographic reference values of female beagle dogs Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:56:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5481836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5481836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78808347,"identity":"d32ec425-0d60-4601-8974-a49a011f474d","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":582574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHematological parameters of beagle dogs. \u003c/strong\u003eRepresents the mean values of erythrocyte indices of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure1HematologyErythrocyteIndices.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/ecaba10358cc58d3853ff4d3.png"},{"id":78808359,"identity":"08cccbec-e123-48b4-a11a-58640edb54da","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHematological parameters of beagle dogs. \u003c/strong\u003eRepresents the mean values of leukocyte indices of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure2HematologyLeukocyteIndices.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/5a1a77ff044c9d2f24cb7c8d.png"},{"id":78808352,"identity":"6f2aae19-c902-4ea0-ba3a-9e576da95dab","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":589336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHematological parameters of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure3HematologyCoagulationParameters.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/3c2a981d6eb7db529f83655c.png"},{"id":78808345,"identity":"1c6ad9ea-882e-4125-b76d-dfe2362e6444","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":567693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlood glucose and blood lipid indexes of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure4GlucoseandLipidProfile.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/07493008999dacd8a39734f7.png"},{"id":78810915,"identity":"3c15dfb3-ebbb-46ca-af79-ce18de16caa9","added_by":"auto","created_at":"2025-03-19 09:02:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":547437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiver enzyme activities of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure5HepaticEnzymes.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/b57ea1509690312283ea4599.png"},{"id":78808361,"identity":"7c8b4a97-42a5-4df6-a169-a489ea34f9b2","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":469201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRenal function index of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure6RenalMarkers.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/8d99f8a1231227583fa52d53.png"},{"id":78809223,"identity":"66f4b4d2-7b28-4fa3-892c-4457496339cb","added_by":"auto","created_at":"2025-03-19 08:46:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":420505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiochemical enzyme and proteins of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure7CKandProteinIndices.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/b7ed67bd86e2d74f3a2c05c1.png"},{"id":78810405,"identity":"18dae392-1bc9-46ca-a1bc-4673ef14f531","added_by":"auto","created_at":"2025-03-19 08:54:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":552793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIon/electrolyte indexes of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure8CaPhandElectrolytes.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/86fa4d1cbea5a638a06eff4f.png"},{"id":78809221,"identity":"9fe2b2c2-d4c2-46ed-adee-c7019cd57329","added_by":"auto","created_at":"2025-03-19 08:46:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":624776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrocardiographic intervals of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure9ECGIntervals.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/9b0edc953960d3d3fc947508.png"},{"id":78808360,"identity":"70705c74-87e9-4b06-99f2-1df67dd24306","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":396575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQT and corrected QT intervals of beagle dogs. \u003c/strong\u003eRepresents the mean values of males and females. Two-way ANOVA was used to evaluate the effects of sex, age, and sex–age interaction. The difference between male and female in each age group was analyzed by two-sample t-test. (*, P\u0026lt;0.05; **, p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Figure10QTandQTcintervals.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/7908ca7b6abc6a55dfb6195b.png"},{"id":78810404,"identity":"d65433a1-7db5-4381-ad92-0b4b394c5fe3","added_by":"auto","created_at":"2025-03-19 08:54:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":293733,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis of different QTc formulas in beagle dogs. QT – uncorrected QT interval (●), QTc(B) – QT interval corrected using Bazett’s formula (■), QTc(F) – QT interval corrected using Fridericia’s formula (▲), QTc(V) – QT interval corrected using Van de Water’s formula (\u003cstrong\u003ex\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Figure11AllQTcFormulas.png","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/e59c679d17836fb07db83d2d.png"},{"id":99817174,"identity":"2c18c68b-dfea-43e9-ac54-94dece2c9011","added_by":"auto","created_at":"2026-01-08 14:48:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6313137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/077c334b-d979-4db7-9e14-1e8f0fa5856d.pdf"},{"id":78809213,"identity":"e4d0f20c-5425-45c5-939c-f32d8d981fb6","added_by":"auto","created_at":"2025-03-19 08:46:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20441,"visible":true,"origin":"","legend":"Supp Table_ethics approval numbers","description":"","filename":"TableS1IAECProtocolNumbers.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/1d0b6340a7e9606f56a64479.docx"},{"id":78811578,"identity":"44bf0846-ecd7-4cac-8c3f-3e1bb999431b","added_by":"auto","created_at":"2025-03-19 09:10:01","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19824,"visible":true,"origin":"","legend":"Abbreviations, units of measurements and methods of analysis of different hematological and biochemical parameters.","description":"","filename":"ST1CPLParametersandunits.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/c92050a55bddfaa0359367fe.docx"},{"id":78810401,"identity":"bf4f4212-1908-4031-b269-7221e0943661","added_by":"auto","created_at":"2025-03-19 08:54:01","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26832,"visible":true,"origin":"","legend":"\u003cp\u003eHematological parameters of male beagle dogs\u003c/p\u003e","description":"","filename":"ST2DescriptiveSTAThematologymale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/c8aae48651ccf7fa68003f21.docx"},{"id":78809218,"identity":"cf78517a-c0d1-43b7-b349-45bec37f5991","added_by":"auto","created_at":"2025-03-19 08:46:01","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26537,"visible":true,"origin":"","legend":"Hematological parameters of female beagle dogs","description":"","filename":"ST3DescriptiveSTAThematologyfemale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/4d4705575a29b9739721d43f.docx"},{"id":78809214,"identity":"b64aaadd-2ca8-476a-be93-e4870a6c7a3f","added_by":"auto","created_at":"2025-03-19 08:46:01","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28264,"visible":true,"origin":"","legend":"Biochemical parameters for male beagle dogs","description":"","filename":"ST4DescriptiveSTATbiochemistrymale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/d0361324fe7f6e504036b76b.docx"},{"id":78808344,"identity":"22a1cc68-b187-4d87-b167-11ba4909debe","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":28427,"visible":true,"origin":"","legend":"\u003cp\u003eBiochemical parameters for female beagle dogs\u003c/p\u003e","description":"","filename":"ST5DescriptiveSTATbiochemistryfemale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/ba0bca8282f3b3b6b5a78ee7.docx"},{"id":78808358,"identity":"7c74d85a-2e9d-4093-a651-0c549e14923a","added_by":"auto","created_at":"2025-03-19 08:38:01","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":19408,"visible":true,"origin":"","legend":"\u003cp\u003eElectrocardiographic reference values of male beagle dogs\u003c/p\u003e","description":"","filename":"ST6DescriptiveSTATECGMale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/8337d947c029d47380713596.docx"},{"id":78810913,"identity":"9514798e-ac5e-4168-8103-dfeb05620003","added_by":"auto","created_at":"2025-03-19 09:02:01","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":19382,"visible":true,"origin":"","legend":"Electrocardiographic reference values of female beagle dogs","description":"","filename":"ST7DescriptiveSTATECGFemale.docx","url":"https://assets-eu.researchsquare.com/files/rs-5481836/v1/fe18d3d6bcf78c7481f38eb9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Age and Sex-Dependent Variations in Hematological, Biochemical, and Electrocardiographic Parameters in Beagle Dogs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBeagle dogs play a crucial role in biomedical research due to their unique characteristics. Their gentle and friendly nature makes them easy to handle, which is essential for laboratory settings (Choi et al., 2011). Beagles are physiologically similar to humans, suffering from similar diseases such as diabetes, epilepsy, and cancer, making them ideal for studying these conditions (Dysko et al., 2002). Their genetic uniformity ensures consistent and reliable results in experiments. In drug development, beagles are used to test the safety and efficacy of new medications before human trials, helping identify potential side effects and appropriate dosages. They are also employed in toxicology studies to understand the effects of chemicals and other substances on the body. This research is vital for ensuring the safety of both human and veterinary medicines (NRC 1988; Schulte and Arlt 2022; Choi et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBiomarkers are crucial in preclinical research for identifying disease mechanisms, selecting drug targets, and assessing drug safety and efficacy (Califf 2018). They provide insights into pharmacodynamics and pharmacokinetics, enabling optimal dosage and administration. Biomarkers also facilitate early disease detection and monitoring, improving treatment development (Zhao et al., 2015; CDER 2018). Additionally, they enhance cost and time efficiency by identifying ineffective drugs early in the process, thereby, significantly improving the precision and efficiency of preclinical research, leading to safer and more effective therapies (Califf 2018; Zhao et al., 2015; CDER 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn preclinical toxicology studies, clinical pathology parameters and electrocardiography (ECG) serve as crucial biomarkers (Guth 2007; York 2017; Califf 2018). Hematology parameters such as white blood cell count, red blood cell count, hemoglobin, and hematocrit assess the hematopoietic system\u0026rsquo;s response to toxic substances. Clinical chemistry markers like alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine evaluate liver and kidney function. Coagulation parameters, including prothrombin time (PT) and activated partial thromboplastin time (aPTT), help assess the coagulation system (Sasseville et al., 2014). ECG is used to monitor heart rate, detect arrhythmias, and measure the QT interval, which can indicate cardiotoxicity (Guth 2007; Patel et al., 2017; Shah et al., 2019). These biomarkers are essential for identifying potential toxic effects of new drugs and chemicals before clinical trials, ensuring safety and efficacy.\u003c/p\u003e\n\u003cp\u003eThe effect of age and sex on clinical pathology and ECG parameters in beagle dogs is significant. Age-related changes in clinical pathology parameters include variations in hematology and clinical chemistry values. For instance, older dogs often exhibit increased levels of alkaline phosphatase (ALP) and blood urea nitrogen (BUN), indicating potential liver and kidney function changes (Oklahoma veterinary specialists (webpage); Stiller et al., 2021). Sex differences also play a role, with male Beagle dogs typically showing higher red blood cell counts and hemoglobin levels compared to females (Nemeth et al., 2010). In terms of ECG parameters, age influences heart rate and the duration of various ECG intervals. Older Beagle dogs tend to have a slower heart rate and prolonged QRS duration. Sex differences are evident in ECG readings as well; males generally have higher QRS voltage and longer QRS duration than females (Murphy et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariations in clinical pathology and ECG parameters are crucial for accurately interpreting preclinical study results, ensuring that age and sex are considered when assessing the efficacy and safety of new drugs. Therefore, it is essential to acquire baseline data on various clinical pathology and ECG parameters of beagle dogs and evaluate the effects of age and sex on these parameters to facilitate reliable interpretation across a broad range of preclinical safety studies. Previous studies examining these parameters in beagle dogs often lack sufficient data volume or have varying environmental, husbandry, or other test conditions. This study aims to provide baseline data on different clinical pathology and ECG parameters of over 400 beagle dogs housed in controlled environments and analyze the influence of age and sex on these parameters. Such a comprehensive and reliable dataset will be vital for the biomedical research community conducting experiments on beagle dogs.\u003c/p\u003e\n\u003cp\u003eA brief part of this retrospective analysis was presented at the 73\u003csup\u003erd\u003c/sup\u003e Indian Pharmaceutical Congress held at Hyderabad, India.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimals, Husbandry and Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data evaluated in this study were obtained from 436 (or fewer) beagle dogs (\u003cem\u003eCanis lupus familiaris\u003c/em\u003e; 218 males and 218 females of varying age) housed individually in clean kennels located at the Canine Research Facility, Zydus Research Centre, Ahmedabad, India. Dogs were housed with visual, auditory and olfactory access to each other. Moreover, dogs had access to a play area for physical exercise, allowing them to freely run, jump, roll, sit and mix for physical, mental and social well-being. Each day, the floor of the kennels was washed with water and disinfectant solution. The following husbandry conditions were maintained: temperature:18 to 29 \u0026deg;C, relative humidity: 30 to 70%, 15 air changes per hour, and 12:12 h light/dark cycles. Certified pellet feed (Pedigree\u0026reg; feed, Mars International India Pvt. Ltd, Hyderabad, India) and drinking water (filtered by reverse osmosis followed by UV treatment) were provided \u003cem\u003ead libitum\u003c/em\u003e to all animals. All animal husbandry-related procedures were performed as per the SOPs applicable at the institution and as per the CPCSEA Guidelines for Laboratory Animal Facilities, Government of India (CCSEA 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDogs were divided into five groups based on age at the time of sampling: 9-24 months old, 25-36 months old, 37-48 months old, 49-60 months old and 61-84 months old. A total of maximum 436 observations were analysed for 10 electrocardiographic parameters and a maximum of 400 observations were analysed for 18 hematological and 21 biochemical parameters from conscious beagle dogs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and\u0026nbsp;accreditations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from various studies conducted at our organization in past few years. Clinical pathology and electrocardiography were endpoint evaluations in those studies. All of those studies were approved by the institutional animal ethics committee as well as the Committee for the Control and Supervision of Experiments on Animals (CCSEA), a statutory body under the Government of India (GoI). Details of different IAEC-approved protocol numbers are presented in Supplementary Table 1 (Table S1).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe animal facility is registered with CCSEA (Facility Registration Number: 77/PO/ RcBi/SL/99/CPCSEA). Moreover, the test facility is accredited with a GLP certificate from the National GLP Compliance Monitoring Authority (NGCMA), GoI for conducting regulatory studies, and AAALAC International for animal ethics. The Clinical Pathology Laboratory (CPL) is accredited by the National Accreditation Board for Testing and Calibration Laboratories (NABL), GoI, and NGCMA, GoI. The CPL has an established in-house quality control program as well as an external quality assessment program with the College of American Pathologists (Illinois, USA).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood sampling and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter overnight fasting, blood was collected from the cephalic or saphenous veins of the dogs by trained personnel. For hematology analysis, blood was collected in ready-to-use vacutainers containing K\u003csub\u003e2\u003c/sub\u003e-EDTA. Hematology analysis was conducted on an Advia 2120i haematology analyser (Siemens Healthineers, USA). For serum biochemistry analysis, blood was collected in ready-to-use Gel+Clot activator vacutainer tubes. Blood was allowed to clot for at least 30\u0026nbsp;minutes at room temperature before centrifugation at 4000 rpm for 10 minutes at 24\u0026deg;C to obtain serum. Biochemistry parameters were evaluated on a Cobas c311 biochemistry analyser (Roche Diagnostics, Switzerland). For coagulation and ESR analysis, tubes containing 3.8% w/v sodium citrate were used to collect blood. ESR estimation was performed by the Westergren method, while coagulation parameters were analysed by an ECL-412 coagulation analyser (ERBA Diagnostics Mannheim GmbH, Germany). Blood samples were analysed at CPL, Zydus Research Centre, India. All instruments were periodically calibrated and serviced. The procedures followed for analysis of different parameters were as per the SOPs applicable at the institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematological and biochemical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHematological analysis of whole blood was carried out on an automated hematology analyser for the following parameters: number of white blood cells (WBC), number of red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), platelets (PLT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), absolute neutrophil (Abs. NEUT), absolute lymphocyte (Abs. LYMPH), absolute monocyte (Abs. MONO), absolute eosinophil (Abs. EOSIN), absolute basophil (Abs. BASO), absolute reticulocyte (Abs. RETIC) and red cell distribution width (RDW).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProthrombin time (PT) and Activated Partial Thromboplastin Time (APTT) were analyzed by an automated coagulation analyzer. The erythrocyte sedimentation rate (ESR) was determined by utilizing a Westergren tube.\u003c/p\u003e\n\u003cp\u003eBiochemical analysis of serum was carried out on an automated biochemistry analyzer for the following parameters: glucose (GLU), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), creatine kinase (CK), total protein (TP), albumin (ALB), globulin (GLB), urea (UREA), creatinine (CREA), calcium (Ca), inorganic phosphorous (Phos), sodium (Na\u003csup\u003e+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e), chloride (Cl\u003csup\u003e-\u003c/sup\u003e), total bilirubin (TBIL), and Gamma Glutamyl Transferase (GGT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe units and method of analysis for all hematological and biochemical parameters are described in the Supplementary Table 2 (Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectrocardiographic parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSnapshot ECGs were recorded from conscious beagle dogs in the standing or lateral recumbency position. To improve conduction between electrodes and the skin of the animal, inert electrode gel was applied at the site of the four limb electrodes and one chest electrode before electrode attachment. The dogs were allowed to settle in the restrained position before recording the ECGs. An electrocardiograph machine, CARDIOVIT AT-1(VET) (Schiller AG, Switzerland, Software version: 8.02) was used to record ECG traces at 5 mm/mV sensitivity and 25 mm/s paper speed. Twelve lead ECGs were recorded; HR and ECG intervals were determined from lead II. The electrocardiographic parameters analyzed were as follows: heart rate, RR interval, P-wave amplitude and duration, PR interval, QRS interval and QT interval. QTc interval (corrected QT interval) was calculated by using three different QTc formulas: QTc(B) (Bazett\u0026rsquo;s formula), QTc(F) (Fridericia\u0026rsquo;s formula) and QTc(V) (Van de Water\u0026rsquo;s formula).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA two-way unbalanced analysis of variance (ANOVA) was used to evaluate the effects of age, sex and age-sex interaction using Statistical Analysis Software (version 9.4; Cary, NC, USA). The difference amongst sexes was evaluated by two-sample t-test. P-value \u0026lt;0.05 was considered statistically significant. Linear regression analysis was performed to evaluate the correlation between HR and QT/QTc intervals. Pearson\u0026rsquo;s correlation coefficient determined the direction and extent of the linear relationship between the two variables. QTc-HR scatter plots were generated and the slope of the QTc-HR regression line for each QTc formula was determined and used to compare QTc formulae.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eReference ranges for hematological, biochemical and electrocardiographic parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe references values and range for hematological, biochemical and electrocardiographic parameters by sex and age are reported in the Supplementary Tables (Tables S3 to S8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematological parameters of beagle dogs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 18 hematological parameters from 400 healthy beagle dogs were analysed, compiled and reported. Age exerted a significant effect on all hematological parameters except ESR (Table 1, Figures 1-3). Sex and gender affect manifestation, epidemiology and pathophysiology of diseases. Sex and gender are essential in developing intervention, treatment and preventive strategies for various health initiatives (Regitz‐Zagrosek, 2012). In this study, sex was found to affect only MCH and RDW parameters (Table 1, Figures 1-3). Age-sex interaction showed significant effects on RBC, HGB, HCT, MCH. MCHC, PLT, Absolute Lymphocytes, Absolute Reticulocytes, PT and RDW parameters (Table 1, Figures 1-3). Most frequent sex-related significant differences were observed in 9-24 months old and 25-36 months old groups of beagle dogs for RBC, HGB, HCT, MCH, MCHC, Abs. Neutrophils, Abs. Lymphocytes, Abs. Monocytes, Platelets and PT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical parameters of beagle dogs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of age, sex, and age-sex interaction on various biochemical parameters are reported in Table 2. Age exerted a significant effect on glucose, cholesterol, HDL, LDL, AST, ALP, CK, TP, ALB, GLB, CREAT, Phosphorous, electrolytes, GGT and TBL. Significant effects of sex were observed on all biochemical parameters except TP, ALB, Urea, Calcium, Phosphorous, Sodium, GGT and TBL. Significant effects of age-sex interaction were displayed by LDL, CK, ALB, Urea, Phosphorous, Sodium, Chloride, and TBL. For LDL, sex-based significance was observed at all age groups except 49-60 months old. For other biochemical parameters, sex-dependent significance was observed for varying age groups, however, most frequent observations were made for 25-36 months old (TC, HDL, LDL, AST, ALT, Urea, ALB, GLB, Calcium, Phosphorous, Sodium, and Potassium) (Figures 4-8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectrocardiographic parameters of beagle dogs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10 electrocardiographic parameters were estimated from 436 healthy beagle dogs. Age and sex were found to exert significant effect on p-wave amplitude and PR interval respectively, while age-sex interaction did not reveal significant effects on any ECG parameters (Table 3). Although the occurrence of sex-based significance amongst individual age groups was random, it was observed in all parameters. All the corrected QT intervals viz, QTc (B), QTc (V) and QTc (F) did not display any significance amongst age or sex (Figures 9 and 10). Regression analysis of the corrected QT interval for all samples showed that QTc (F) better corrected the QT interval for changes in heart rate when compared to QTc (B) and QTc (V) (Figure 11). Pearson correlation coefficient (r) and the slope of the regression line were estimated to compare the QT correction formulas. The values of r and slope that are closest to zero indicate least correlation and best QT correction. The slope and r-value for different formulas are reported in Table 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, hematological, biochemical and electrocardiographic analyses of 436 beagle dogs were performed in order to establish baseline reference indices by age and sex and to determine the effects of age and sex on these physiological parameters. Dogs share about 82% homology with humans among other anatomical and physiological similarities, making them a widely employed non-rodent animal model of choice in biomedical research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Factors in Reference Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs previously published reports have shown that age and sex are the two primary factors affecting different physiological parameters in dogs (Montoya Navarrete et al., 2021; Alilovic et al., 2022), it is essential to develop a comprehensive reference range of blood-based and ECG parameters across different age groups by sex. However, in addition to age and sex, blood and ECG parameters can also be affected by other factors such as anesthetics, fasting status, pregnancy status, animal husbandry, etc. Thus, different experimental conditions must be considered before interpreting results from different studies.\u003c/p\u003e\n\u003cp\u003eThe blood samples collected and ECG traces recorded in our study were obtained from unanesthetized animals as they better reflect the actual functional parameters of these animals. General anesthetics are known to affect blood and ECG parameters. Certain examples of anesthetic-induced changes in normal blood and ECG parameters are mentioned here: xylazine-propofol anesthesia causes a decrease in hemoglobin, packed cell volume, and total erythrocyte count, while increasing total leukocyte count, glucose, BUN, creatinine, AST, and ALT in dogs (Dewangan, 2016). Ketamine-xylazine anesthesia causes an increase in glucose and CK and a decrease in TP values (\u0026Ccedil;amkerten, 2013). Propofol and alfaxalone cause QTc prolongation in dogs. Along with QTc prolongation, these anesthetics significantly impact heart rate, RR, PR, and QRS intervals, causing depression of the ST segment (Casoria et al., 2024). Cardiac effects such as a reduction of the PR and QT intervals and an increase in heart rate were noticed in dogs treated with the combination of atropine, tiletamine, and zolazepam (ATZ), while treatment with atropine, levomeprazine, thiopental, and halothane (ALTH) caused qualitative modifications of the ST segment, T wave, and cardiac rhythm in experimental dogs (T\u0026aacute;rraga et al., 2000).\u003c/p\u003e\n\u003cp\u003eThe American Association for Clinical Chemistry\u0026rsquo;s Division of Animal Clinical Chemistry (AACC-DACC) and the American Society for Veterinary Clinical Pathology\u0026rsquo;s (ASVCP) Joint Committee on Clinical Pathology Testing of Laboratory Species recommend 12-18 hours of overnight fasting for laboratory animal species prior to blood collection. The primary rationale for fasting research animals is to reduce variability in analytes such as glucose and triglycerides, which are highly sensitive to fed or fasting conditions (Weingand et al., 1996). Some studies have reported variance in analyte concentrations of alkaline phosphatase, urea, glucose, creatinine, and plasma proteins (Gauvin et al., 2024). Thus, when comparing analyte results between different studies, food intake should be considered.\u003c/p\u003e\n\u003cp\u003eThe animals employed in this analysis were housed individually within a controlled environment and with physical access to each other for social well-being, thereby limiting any variability possibly induced by varying animal husbandry procedures. Additionally, these animals were nulliparous and non-pregnant. Beagle dogs are reported to have marked differences in physiological blood parameters of pregnant and non-pregnant dogs, such as the number of erythrocytes, hematocrit and hemoglobin levels, total leukocyte count, and relative values of neutrophils and lymphocytes, serum cholesterol, HDL, and LDL concentrations (Kockaya, 2019; Dim\u0026ccedil;o et al., 2013). Hence, experimental conditions presented in the current study must be considered prior to applying this reported reference range for various blood and ECG parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematological and Biochemical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonitoring hematological and biochemical parameters during preclinical toxicology studies is essential for several reasons (NRC 1988). These parameters enable early detection of toxic effects on physiological systems, such as bone marrow suppression or liver and kidney dysfunction. They provide a comprehensive overview of the dogs\u0026rsquo; health, identifying underlying issues that could affect study outcomes. Specific changes can pinpoint organ-specific toxicity, like elevated liver enzymes indicating hepatic damage. Additionally, these parameters help establish dose-response relationships, determining safe dosage ranges for new compounds (OECD Section 4). Comparative analysis between treated and control groups ensures the reliability of study results, which is vital for regulatory submissions and the overall assessment of new pharmaceutical compounds\u0026rsquo; safety and efficacy (ICH M3 (R2), 2009; Eaton and Gilbert, 2013).\u003c/p\u003e\n\u003cp\u003eAge and sex-based differences were apparent in different hematological and biochemical parameters in this study. As evident from the results, most sex-based significant changes were noted in 9-24 months and 25-36 months age group. Parameters such as RBC, HGB, HCT, MCH, MCHC, #Neutrophils, #Lymphocytes, #Monocytes, Platelets, PT, ESR, Glucose, TG, TC, HDL, LDL, AST, ALT, TBIL, Urea, Creatinine, CK, Albumin, Globulin, Calcium, Phosphorous, Sodium, and Potassium displayed significant differences in male and female animals. Red blood cell counts, hemoglobin concentration, and hematocrit are clinical biomarkers of anaemia, internal hemorrhage and other blood disorders and are associated with increased risk of cardiovascular diseases (Kishimoto et al. 2020, Xie et al. 2013). Sex-based difference in these RBC parameters observed in the current study are in line with gender-based difference in RBC parameters in humans (Grau et. al. 2018). Although previous studies have reported age-related statistical significance in RBC parameters of beagle dogs, data regarding sex-based significance in beagle dogs is scarcely available. While some researchers have found higher RBC parameter values for male animals, some studies have reported no statistical significance between both sexes (Khan et. al. 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the current study, it was found that at lower age, female animals had higher values of RBC parameters than males. However, with increasing age, the values of RBC parameters in male animals increased compared to the female animals of respective age groups. In dogs, younger individuals generally exhibit higher levels of red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) compared to older dogs, with males having higher levels than females. Mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) tend to decrease with age, but show minimal sex-based differences (Lee et al., 2020; Oo et al., 2017). Younger dogs typically have higher neutrophil and lymphocyte counts, while older dogs may have increased monocyte levels; female dogs often have higher lymphocyte counts than males. Platelet counts decrease with age, with females usually having higher counts than males (Canine cbc variations with age\u0026nbsp;(webpage), n.d.;\u0026nbsp;Kocaturk et al., 2024; Lee et al., 2020). Prothrombin time (PT) levels increase with age, indicating longer clotting times in older dogs, but show no significant sex-based differences (Oo et al., 2017; Lee et al., 2020). These variations highlight the need to consider both age and sex for accurate clinical assessments.\u003c/p\u003e\n\u003cp\u003eSerum glucose and lipid parameters such as total cholesterol, HDL and LDL reported significant differences between male and female animals. In dogs, age and sex influence serum glucose and lipid parameters. Younger dogs tend to have higher glucose levels, while older dogs often show higher total cholesterol (TC) levels. HDL levels are higher in younger dogs, whereas LDL levels increase with age. Female dogs generally have higher fasting plasma glucose (FPG), total cholesterol, HDL, and LDL levels compared to males. These differences are influenced by hormonal factors, body composition, genetic makeup, and metabolic regulation. Estrogen enhances insulin sensitivity and lipid metabolism, while testosterone affects muscle mass and fat distribution. Males typically have more muscle mass, and females have higher body fat percentages, impacting glucose and lipid metabolism. Genetic differences also play a role in how these parameters are processed (Xenoulis et al., 2020; Kawasumi et al., 2014; Montoya Navarrete et al., 2021).\u0026nbsp;These differences emphasize the importance of considering both age and sex for accurate diagnosis and treatment.\u003c/p\u003e\n\u003cp\u003eWhen evaluating liver function tests, the focus is on several key enzymes and proteins: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT). ALT is found in the cytosol, whereas AST exists in both cytosolic and mitochondrial forms. Additionally, bilirubin levels (total, conjugated, and unconjugated), prothrombin time (PT), total protein, globulins, and albumin are assessed (Kwo et al., 2017; Johnston 1999). These tests help identify the specific area of liver damage, and the pattern of elevation aids in forming a differential diagnosis. Puppies generally have lower levels of AST, ALT, TBIL, albumin, and globulin compared to adult dogs due to their developing liver function and protein synthesis capabilities. \u0026nbsp;However, sex-based differences in liver enzymes and protein levels in dogs are generally minimal and not clinically significant. Both male and female dogs can show variations in AST (Aspartate Aminotransferase) and ALT (Alanine Aminotransferase) levels. Male dogs may have a slightly higher threshold for bilirubin resorption, leading to more common mild bilirubinuria compared to females. There are no significant sex-based differences in albumin and globulin levels, which are crucial proteins produced by the liver (estaff (webpage) 2013; Montoya Navarrete et al., 2021;\u0026nbsp;Lee et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSex and age-based differences in urea, creatinine, and CK levels in dogs are notable. Puppies generally have lower urea and creatinine levels due to their developing kidneys and lower muscle mass, while no significant sex differences exist for these markers. Younger dogs tend to have higher CK levels because of increased muscle activity and growth, and male dogs may exhibit slightly higher CK levels compared to females due to greater muscle mass (Dog kidney failure (webpage), 2013;\u0026nbsp;Montoya Navarrete et al., 2021;\u0026nbsp;Lee et al., 2020). These variations underscore the importance of considering both age and sex when interpreting biochemical markers in dogs.\u003c/p\u003e\n\u003cp\u003eIn dogs, age and sex influence levels of key electrolytes. Puppies generally have lower calcium levels due to developing bones and metabolism, while younger dogs have higher phosphorous levels essential for bone growth. Sodium and potassium levels are relatively stable across different age groups, with slight variations in potassium due to diet and health. There are no significant differences in calcium, phosphorous, sodium, and potassium levels between male and female dogs (Meller et al., 1984; Lee et al., 2020; Koek et al., 2021). These variations highlight the importance of considering both age and sex when interpreting these electrolyte levels in dogs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectrocardiographic Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe utilization of Beagle dogs in translational research serves as a crucial bridge between animal models and human clinical trials. These dogs are frequently employed in preclinical studies to evaluate the cardiac safety of new drugs (Hanton and Rabemampianina, 2006). By understanding the electrocardiographic (ECG) patterns of Beagle dogs, researchers can identify potential cardiac side effects early in the drug development process.\u003c/p\u003e\n\u003cp\u003eAge and sex significantly influence ECG parameters in Beagle dogs. Younger Beagle dogs typically exhibit higher heart rates compared to older dogs, with sinus arrhythmia more commonly observed in the latter. Although aging can result in changes to the duration and amplitude of ECG waves and complexes, these changes are generally not significant in toxicological or pharmacological studies since most subjects are young adults. There are no significant differences in ECG parameters between male and female Beagle dogs, with both sexes showing similar heart rates, PQ intervals, and QT intervals. While the heart axis may shift slightly based on body position during ECG recording, this shift is not significantly influenced by sex (Murphy et al., 2022; Mukherjee et al., 2020; Eckenfels and Trieb, 1979; Lerdweeraphon et al., 2020).\u003c/p\u003e\n\u003cp\u003eComparing ECG patterns between Beagle dogs and humans reveals some interesting differences and similarities. In humans, aging is associated with notable changes in ECG parameters, such as increased PR interval, QRS duration, and QT interval, with older adults often exhibiting a higher prevalence of arrhythmias, including atrial fibrillation. Similarly, older Beagle dogs show changes in the duration and amplitude of ECG waves, and a higher incidence of sinus arrhythmia compared to younger dogs. However, unlike humans, Beagle dogs do not exhibit significant sex-based differences in ECG parameters (Chavan et al., 2022; Eckenfels and Trieb, 1979; Hanton and Rabemampianina, 2006). Understanding these differences is crucial for accurately interpreting ECG results in both veterinary and human medical contexts.\u003c/p\u003e\n\u003cp\u003eThe QT interval represents the time required for the heart\u0026rsquo;s electrical system to depolarize and repolarize (Wiśniowska et al., 2016). Prolongation of the QT interval can indicate a risk of potentially life-threatening arrhythmias. Assessing the QT interval and its correction (QTc) is vital in preclinical studies to ensure safety by identifying proarrhythmic effects of new drugs, which can lead to serious arrhythmias like Torsades de Pointes. Regulatory bodies such as the FDA and EMA mandate thorough QT (TQT) studies to determine a drug\u0026apos;s potential for QT prolongation. Understanding a drug\u0026apos;s cardiac safety profile early in the development process can guide formulation and dosing decisions, thereby saving time and resources. Preclinical studies also help identify drug-drug interactions that may prolong the QT interval, ensuring safer combination therapies (Lester et al., 2019; Bhatt et al., 2023).\u003c/p\u003e\n\u003cp\u003eUnderstanding the effects of age and sex on QT correction formulas in Beagle dogs is essential in veterinary cardiology. Male dogs typically have shorter QT intervals compared to females, likely due to hormonal differences (Agudelo et al., 2011). Younger dogs usually have faster heart rates and shorter QT intervals, which lengthen with age due to physiological changes in the heart. For older dogs, the Fridericia formula is preferred for its stability across various heart rates (Koyama et al., 2004; Agudelo et al., 2011).\u003c/p\u003e\n\u003cp\u003eSlope and R-value are critical for understanding relationships between variables in statistical analyses. The slope indicates the rate of change in the dependent variable per unit increase in the independent variable, reflecting the direction and magnitude of the relationship. The r-value, or correlation coefficient, measures the strength and direction of the linear relationship between two variables, ranging from -1 to 1 (Bhatt et al., 2023). An effective QT correction formula should yield a QT value independent of heart rate, assessed by determining the correlation between corrected QT values and heart rate or RR intervals.\u003c/p\u003e\n\u003cp\u003eThe current study demonstrated that Fridericia\u0026rsquo;s formula provided corrected QT values that exhibited the least dependence on heart rate changes, with slope values for QTc (F), QTc (V), and QTc (B) measured at 0.0685, -0.1035, and 0.4778, respectively, and corresponding r-values of 0.08, -0.15, and 0.47. These results, which contrast with previous findings (Patel et al., 2017), indicate that Fridericia\u0026rsquo;s formula more effectively corrects the QT interval in Beagle dogs. Notably, Fridericia\u0026rsquo;s formula showed superior QT correction in male dogs, whereas Van de Water\u0026rsquo;s formula was more effective in female dogs.\u003c/p\u003e\n\u003cp\u003eThe study also observed variability in the effectiveness of QT correction formulas across different age groups. QTc (V) was the most effective formula for dogs aged 9-24 months, while QTc (F) was superior for both sexes in the 25-36 months age group. For dogs aged 37-84 months, the performance of QT correction formulas varied by age group and sex, suggesting that these variations may be attributable to the smaller sample size.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study assessed the ability of the three QT formulas to correct QT intervals at heart rates lower and higher than the mean HR (112 bpm). For datasets with HR \u0026le; 112 bpm, QTc (V) provided better correction, while for datasets with HR \u0026gt; 112 bpm, QTc (F) demonstrated superior HR-correction capability. These findings emphasize the need for tailored QT correction formulas based on age, sex, and heart rate variations to ensure accurate assessment of cardiac safety in preclinical studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAge and sex significantly influence various hematological, biochemical, and electrocardiographic parameters in Beagle dogs. Younger Beagle dogs generally exhibit higher levels of red blood cells, hemoglobin, and hematocrit compared to older dogs, with males showing higher values than females as they age. Significant sex-based differences are observed in parameters such as glucose, cholesterol, and liver enzymes, with females often having higher levels of fasting plasma glucose and lipids. While aging affects the duration and amplitude of ECG waves, these changes are generally not significant in young adult subjects used in studies. There are no significant sex-based differences in ECG parameters, making both male and female Beagle dogs suitable for cardiac studies. When comparing Beagle dogs to humans, it is observed that while both species exhibit age-related changes in ECG parameters, sex-based differences are more pronounced in humans. These variations underscore the importance of considering both age and sex for accurate clinical assessments, diagnosis, and treatment in veterinary practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANIMAL ETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this is a retrospective analysis, no animals were used in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the management and scientific team at Zydus Research Centre, India, for their assistance in preparing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLaxit K Bhatt: Conceptualization, Formal Analysis, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eNilam R. Patel: Data Curation, Formal Analysis, Investigation.\u003c/p\u003e\n\u003cp\u003eChintu Kotadiya: Visualization, Validation, Methodology, Writing - Original Draft.\u003c/p\u003e\n\u003cp\u003eKajal G. Patel: Data Curation, Formal Analysis, Investigation\u003c/p\u003e\n\u003cp\u003eHarshida J. Trivedi: Data Curation, Methodology, Validation\u003c/p\u003e\n\u003cp\u003eTushar Patel: Data Curation, Methodology, Validation\u003c/p\u003e\n\u003cp\u003eChitrang R. Shah: Formal Analysis, Investigation, Methodology, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eSudhir R. Patel Formal Analysis, Investigation, Methodology.\u003c/p\u003e\n\u003cp\u003eVipul A. Patel: Methodology, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eShital D. Patel: Visualization, Validation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eRajesh J. Patel: Formal Analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eSunny Kumar: Formal Analysis, Software, Data Curation.\u003c/p\u003e\n\u003cp\u003eJitendra H. Patel: Supervision, Resources, Project Administration, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eRajesh Sundar: Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eMukul R. Jain: Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgudelo, C.F., Scheer, P., Tomenendalova, J., 2011. Tomenendalova J: How to approach the QT interval in dogs - the state of the heart: a review. Veterin\u0026aacute;rn\u0026iacute; medic\u0026iacute;na 56, 14\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eALILOVIC, I., RUKAVINA, D., AJANOVIC, A., CRNKIC, C., OHRAN, H., ZAHIROVIC, A., 2022. Breed, age, and sex-related variations in hematological and some biochemicalparameters in the Tornjak dog. Turkish Journal of Veterinary \u0026amp; Animal Sciences 46, 192\u0026ndash;200. https://doi.org/10.55730/1300-0128.4166\u003c/li\u003e\n\u003cli\u003eBhatt, L.K., Shah, C.R., Patel, R.J., Patel, S.D., Patel, S.R., Patel, V.A., Patel, J.H., Dwivedi, P., Shah, N.A., Sundar, R.S., Jain, M.R., 2023. Comparison of different QT correction methods for nonclinical safety assessment in ketamine-anesthetized Indian rhesus monkeys (Macaca mulatta). Toxicology Mechanisms and Methods 33, 490\u0026ndash;501. https://doi.org/10.1080/15376516.2023.2187730\u003c/li\u003e\n\u003cli\u003eBhatt, L.K., Shah, C.R., Patel, S.D., Patel, S.R., Patel, V.A., Patel, R.J., Joshi, N.M., Shah, N.A., Patel, J.H., Dwivedi, P., Sundar, R., Jain, M.R., 2024. A Retrospective Comparison of Electrocardiographic Parameters in Ketamine and Tiletamine-Zolazepam Anesthetized Indian Rhesus Monkeys (Macaca mulatta). Int J Toxicol 43, 184\u0026ndash;195. https://doi.org/10.1177/10915818231221276\u003c/li\u003e\n\u003cli\u003eCaliff, R.M., 2018. Biomarker definitions and their applications. Exp Biol Med (Maywood) 243, 213\u0026ndash;221. https://doi.org/10.1177/1535370217750088\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;amkerten, İ., Şındak, N., \u0026Ouml;zkurt, G., İpek, H., Biricik, S.H., Şahin, T., 2013. Effect of ketamine-xylazine anesthesia on some hematological and serum biochemical values of Bozova greyhounds. Harran \u0026Uuml;niv Vet Fak Derg 2, 27\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eCanine CBC Variations with Age [WWW Document], n.d. URL https://www.moichor.com/blog-post/canine-cbc-variations-with-age (accessed 10.24.24).\u003c/li\u003e\n\u003cli\u003eCasoria, V., Greet, V., Auckburally, A., Murphy, S., Flaherty, D., 2024. Comparison of the effects of propofol and alfaxalone on the electrocardiogram of dogs, with particular reference to QT interval. Front. Vet. Sci. 10. https://doi.org/10.3389/fvets.2023.1330111\u003c/li\u003e\n\u003cli\u003eCenter for Drug Evaluation and Research, 2018. What Are Biomarkers and Why Are They Important? Transcript [WWW Document]. FDA. URL https://www.fda.gov/drugs/biomarker-qualification-program/what-are-biomarkers-and-why-are-they-important-transcript (accessed 10.24.24).\u003c/li\u003e\n\u003cli\u003eChavan, S.R., Jadhav, K.L., R. Chavan, S., N. Chandak, M., 2022. Human\u0026rsquo;s Bosom Buddy Beagle: As an Experimental Animal Model. Biosci., Biotech. Res. Asia 19, 543\u0026ndash;551. https://doi.org/10.13005/bbra/3008\u003c/li\u003e\n\u003cli\u003eChoi, S.-Y., Hwang, J.-S., Kim, I.-H., Hwang, D.-Y., Kang, H.-G., 2011. Basic data on the hematology, serum biochemistry, urology, and organ weights of beagle dogs. Lab Anim Res 27, 283. https://doi.org/10.5625/lar.2011.27.4.283\u003c/li\u003e\n\u003cli\u003eDewangan, R., Tiwari, S., Sharda, R., Kalim, M., 2016. Haemato-Biochemical Response to Xylazine-Propofol Anaesthesia in Dogs. International Journal of Science, Engineering and Technology 5, 2331\u0026ndash;2336.\u003c/li\u003e\n\u003cli\u003eDim\u0026ccedil;o , E., Abeshi , J., Dhamo , G., 2013. Effect of pregnancy in hematological profile of dogs. Albanian Journal of Agricultural Sciences 12, 159\u0026ndash;162.\u003c/li\u003e\n\u003cli\u003eDog Kidney Failure: Stages Chart and Resources [WWW Document], 2023. . TCVM Pet Supply. URL https://tcvmpet.com/blogs/news/understanding-dog-kidney-failure-stages-chart-resources (accessed 10.24.24).\u003c/li\u003e\n\u003cli\u003eDysko, R.C., Nemzek, J.A., Levin, S.I., DeMarco, G.J., Moalli, M.R., 2002. Biology and Diseases of Dogs, in: Laboratory Animal Medicine. Elsevier, pp. 395\u0026ndash;458. https://doi.org/10.1016/B978-012263951-7/50014-4\u003c/li\u003e\n\u003cli\u003eEaton, D. L. \u0026amp; Gilbert, S. G. 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Lab Anim 40, 123\u0026ndash;136. https://doi.org/10.1258/002367706776319088\u003c/li\u003e\n\u003cli\u003eICH M3 (R2) Non-clinical safety studies for the conduct of human clinical trials for pharmaceuticals - Scientific guideline | European Medicines Agency (EMA). https://www.ema.europa.eu/en/ich-m3-r2-non-clinical-safety-studies-conduct-human-clinical-trials-pharmaceuticals-scientific-guideline (2009). \u003c/li\u003e\n\u003cli\u003eJohnston, D.E., 1999. Special Considerations in Interpreting Liver Function Tests. Am Fam Physician. 59, 2223\u0026ndash;2230.\u003c/li\u003e\n\u003cli\u003eKawasumi, K., Kashiwado, N., Okada, Y., Sawamura, M., Sasaki, Y., Iwazaki, E., Mori, N., Yamamoto, I., Arai, T., 2014. Age effects on plasma cholesterol and triglyceride profiles and metabolite concentrations in dogs. BMC Vet Res 10, 57. https://doi.org/10.1186/1746-6148-10-57\u003c/li\u003e\n\u003cli\u003eKhan, S.A., Epstein, J.H., Olival, K.J., Hassan, M.M., Hossain, M.B., Rahman, K., Elahi, M.F., Mamun, M.A., Haider, N., Yasin, G., Desmond, J., 2011. Hematology and serum chemistry reference values of stray dogs in Bangladesh. Open Vet J. 1, 13.\u003c/li\u003e\n\u003cli\u003eKishimoto, S., Maruhashi, T., Kajikawa, M., Matsui, S., Hashimoto, H., Takaeko, Y., Harada, T., Yamaji, T., Han, Y., Kihara, Y., Chayama, K., Goto, C., Yusoff, F.M., Nakashima, A., Higashi, Y., 2020. Hematocrit, hemoglobin and red blood cells are associated with vascular function and vascular structure in men. Sci Rep 10, 11467. https://doi.org/10.1038/s41598-020-68319-1\u003c/li\u003e\n\u003cli\u003eKocaturk, M., Saril, A., Oz, A.D., Rubio, C.P., Ceron, J.J., Yilmaz, Z., 2024. Neutrophil-to-lymphocyte ratio and red blood cell distribution width to platelet ratio and their relationships with inflammatory and antioxidant status in dogs with different stages of heart failure due to myxomatous mitral valve disease. Vet Res Commun 48, 2477\u0026ndash;2487. https://doi.org/10.1007/s11259-024-10431-y\u003c/li\u003e\n\u003cli\u003eKockaya, M., 2019. Comparisons of some blood hematological levels and biochemical parameters in pregnant and non-pregnant Kangal shepherd dogs. Int. J. Vet. Sci. Anim. Husbandry 4, 05\u0026ndash;08. https://doi.org/10.22271/veterinary.2019.v4.i3a.184\u003c/li\u003e\n\u003cli\u003eKoek, W.N.H., Campos-Obando, N., Van Der Eerden, B.C.J., De Rijke, Y.B., Ikram, M.A., Uitterlinden, A.G., Van Leeuwen, J.P.T.M., Zillikens, M.C., 2021. Age-dependent sex differences in calcium and phosphate homeostasis. Endocr Connect. 10, 273\u0026ndash;282. https://doi.org/10.1530/EC-20-0509\u003c/li\u003e\n\u003cli\u003eKoyama, H., Yoshii, H., Yabu, H., Kumada, H., Fukuda, K., Mitani, S., Rousselot, J.-F., Hirose, H., Uchino, T., 2004. Evaluation of QT Interval Prolongation in Dogs with Heart Failure. J. Vet. Med. Sci. 66, 1107\u0026ndash;1111. https://doi.org/10.1292/jvms.66.1107\u003c/li\u003e\n\u003cli\u003eKwo, P.Y., Cohen, S.M., Lim, J.K., 2017. ACG Clinical Guideline: Evaluation of Abnormal Liver Chemistries. Am J Gastroenterol. 112, 18\u0026ndash;35. https://doi.org/10.1038/ajg.2016.517\u003c/li\u003e\n\u003cli\u003eLee, S.H., Kim, J.W., Lee, B.C., Oh, H.J., 2020. Age-specific variations in hematological and biochemical parameters in middle- and large-sized of dogs. J Vet Sci 21, e7. https://doi.org/10.4142/jvs.2020.21.e7\u003c/li\u003e\n\u003cli\u003eLerdweeraphon, W., Thanwongsa, S., Youyod, S., Imsopa, S., Kenchaiwong, W., 2020. The effects of breed, age, sex, and body weight on electrocardiographic parameters in military working dogs. Vet World 13, 1001\u0026ndash;1004. https://doi.org/10.14202/vetworld.2020.1001-1004\u003c/li\u003e\n\u003cli\u003eLester, R.M., Paglialunga, S., Johnson, I.A., 2019. QT Assessment in Early Drug Development: The Long and the Short of It. Int J Mol Sci. 20, 1324. https://doi.org/10.3390/ijms20061324\u003c/li\u003e\n\u003cli\u003eManaging High ALP Levels in Dogs: A Comprehensive Guide | Blog | Oklahoma Veterinary Specialists | Tulsa, OK [WWW Document], n.d. URL https://www.okvets.com/post/managing-high-alp-in-dogs#:~:text=Medications%3A%20Some%20medications%2C%20including%20corticosteroids,unless%20other%20symptoms%20are%20present (accessed 10.24.24).\u003c/li\u003e\n\u003cli\u003eMeller, Y., Kestenbaum, R.S., Yagil, R., Shany, S., 1984. The influence of age and sex on blood levels of calcium-regulating hormones in dogs. Clin Orthop Relat Res 296\u0026ndash;299.\u003c/li\u003e\n\u003cli\u003eMontoya Navarrete, A.L., Quezada Trist\u0026aacute;n, T., Lozano Santill\u0026aacute;n, S., Ortiz Mart\u0026iacute;nez, R., Valdivia Flores, A.G., Mart\u0026iacute;nez Mart\u0026iacute;nez, L., De Luna L\u0026oacute;pez, M.C., 2021. Effect of age, sex, and body size on the blood biochemistry and physiological constants of dogs from 4 wk. to \u0026gt; 52 wk. of age. BMC Vet Res 17, 265. https://doi.org/10.1186/s12917-021-02976-w\u003c/li\u003e\n\u003cli\u003eMukherjee, J., Mohapatra, S.S., Jana, S., Das, P.K., Ghosh, P.R., Das, K., Banerjee, D., 2020. A study on the electrocardiography in dogs: Reference values and their comparison among breeds, sex, and age groups. Vet World 13, 2216\u0026ndash;2220. https://doi.org/10.14202/vetworld.2020.2216-2220\u003c/li\u003e\n\u003cli\u003eMurphy, L., Nakamura, R., Gentile-Solomon, J., Spake, A., Szlosek, D., 2022. Assessment of age, gender, and anxiety on ECG waveform morphology in a large population of domestic dogs. Sci Rep 12, 7339. https://doi.org/10.1038/s41598-022-11378-3\u003c/li\u003e\n\u003cli\u003eNational Research Council (US) Committee on Methods for the In Vivo Toxicity Testing of Complex Mixtures. Interpretation and modeling of toxicity-test results. in Complex Mixtures: Methods for In Vivo Toxicity Testing (National Academies Press (US), 1988).\u003c/li\u003e\n\u003cli\u003eNemeth, N., Kiss, F., Furka, I., Miko, I., 2010. Gender differences of blood rheological parameters in laboratory animals. Clin Hemorheol Microcirc. 45, 263\u0026ndash;272. https://doi.org/10.3233/CH-2010-1303\u003c/li\u003e\n\u003cli\u003eOECD Guidelines for the Testing of Chemicals, Section 4: Health Effects. https://www.oecd-ilibrary.org/environment/oecd-guidelines-for-the-testing-of-chemicals-section-4-health-effects_20745788. \u003c/li\u003e\n\u003cli\u003eOo, T., Kyaw, K.N., Kyaw, S.T., Kyi, A.M., Kyi, K.T., Khin, M.M., Khin, S.T., Khaing, T.K., Khaing, E., Khaing, A.N., Aung, M., Kyaw, W.O., Po, S.P., 2017. Age Related Changes in Hematological Values of Myanmar Local Puppies. J Adv Vet Res 7, 116\u0026ndash;119.\u003c/li\u003e\n\u003cli\u003ePatel, S., Bhatt, L., Patel, R., Shah, C., Patel, V., Patel, J., Sundar, R., Bhatnagar, U., Jain, M., 2017. Identification of appropriate QTc formula in beagle dogs for nonclinical safety assessment. Regul Toxicol Pharmacol 89, 118\u0026ndash;124. https://doi.org/10.1016/j.yrtph.2017.07.026\u003c/li\u003e\n\u003cli\u003ePreclinical Development Handbook: ADME and Biopharmaceutical Properties. (Wiley, 2008). doi:10.1002/9780470249031. \u003c/li\u003e\n\u003cli\u003eRegitz-Zagrosek, V., 2012. Sex and gender differences in health. Science \u0026amp; Society Series on Sex and Science. EMBO Rep 13, 596\u0026ndash;603. https://doi.org/10.1038/embor.2012.87\u003c/li\u003e\n\u003cli\u003eSasseville, V.G., Mansfield, K.G., Brees, D.J., 2014. Safety Biomarkers in Preclinical Development: Translational Potential. Vet Pathol 51, 281\u0026ndash;291. https://doi.org/10.1177/0300985813505117\u003c/li\u003e\n\u003cli\u003eSchulte, E., Arlt, S.P., 2022. What Kinds of Dogs Are Used in Clinical and Experimental Research? Animals 12, 1487. https://doi.org/10.3390/ani12121487\u003c/li\u003e\n\u003cli\u003eShah, C., Bhatt, L., Ravichandra, B.V., Kothule, V., Kadam, S., Nataraju, G.J., Patel, J., Ranvir, R., Bhatnagar, U., Sundar, S.R., Jain, M., 2019. Influence of estrous stages on electrocardiography, clinical pathology and ovarian weight of experimental beagle dogs: a retrospective analysis. Interdiscip Toxicol. 12, 149\u0026ndash;156. https://doi.org/10.2478/intox-2019-0018\u003c/li\u003e\n\u003cli\u003eStiller, J., Defarges, A.M., Brisson, B.A., Bersenas, A.M.E., Pomrantz, J.S., Lang, B., Pearl, D.L., 2021. Diagnostic evaluation of urea nitrogen/creatinine ratio in dogs with gastrointestinal bleeding. J Vet Intern Med. 35, 1427\u0026ndash;1438. https://doi.org/10.1111/jvim.16101\u003c/li\u003e\n\u003cli\u003eT\u0026aacute;rraga, K.M., Spinosa, H.S., Camacho, A.A., 2000. Electrocardiographic evaluation of two anesthetic combinations in dogs. Arq. Bras. Med. Vet. Zootec. 52, 138\u0026ndash;143. https://doi.org/10.1590/S0102-09352000000200009\u003c/li\u003e\n\u003cli\u003eThe Committee for Control and Supervision of Experiments on Animals, 2015. CPCSEA Guidelines for Laboratory Animal Facility - Compendium of CPCSEA.\u003c/li\u003e\n\u003cli\u003eVan De Water, A., Verheyen, J., Xhonneux, R., Reneman, R.S., 1989. An improved method to correct the QT interval of the electrocardiogram for changes in heart rate. J Pharmacol Methods. 22, 207\u0026ndash;217. https://doi.org/10.1016/0160-5402(89)90015-6\u003c/li\u003e\n\u003cli\u003eWeingand, K., Brown, G., Hall, R., Davies, D., Gossett, K., Neptun, D., Waner, T., Matsuzawa, T., Salemink, P., Froelke, W., Provost, J.P., Dal Negro, G., Batchelor, J., Nomura, M., Groetsch, H., Boink, A., Kimball, J., Woodman, D., York, M., Fabianson-Johnson, E., Lupart, M., Melloni, E., 1996. Harmonization of animal clinical pathology testing in toxicity and safety studies. The Joint Scientific Committee for International Harmonization of Clinical Pathology Testing. Fundam Appl Toxicol 29, 198\u0026ndash;201.\u003c/li\u003e\n\u003cli\u003eWiśniowska, B., Tylutki, Z., Wyszogrodzka, G., Polak, S., 2016. Drug-drug interactions and QT prolongation as a commonly assessed cardiac effect - comprehensive overview of clinical trials. BMC Pharmacol Toxicol 17, 12. https://doi.org/10.1186/s40360-016-0053-1\u003c/li\u003e\n\u003cli\u003eXenoulis, P.G., Cammarata, P.J., Walzem, R.L., Suchodolski, J.S., Steiner, J.M., 2020. Serum triglyceride and cholesterol concentrations and lipoprotein profiles in dogs with naturally occurring pancreatitis and healthy control dogs. J Vet Intern Med. 34, 644\u0026ndash;652. https://doi.org/10.1111/jvim.15715\u003c/li\u003e\n\u003cli\u003eXie, L., Xu, F., Liu, S., Ji, Y., Zhou, Q., Wu, Q., Gong, W., Cheng, K., Li, J., Li, L., Fang, L., Zhou, L., Xie, P., 2013. Age- and Sex-Based Hematological and Biochemical Parameters for Macaca fascicularis. PLOS ONE 8, e64892. https://doi.org/10.1371/journal.pone.0064892\u003c/li\u003e\n\u003cli\u003eYork, M.J., 2017. Clinical Pathology, in: A Comprehensive Guide to Toxicology in Nonclinical Drug Development. Elsevier, pp. 325\u0026ndash;374. https://doi.org/10.1016/B978-0-12-803620-4.00014-1\u003c/li\u003e\n\u003cli\u003eZhao, X., Modur, V., Carayannopoulos, L.N., Laterza, O.F., 2015. Biomarkers in Pharmaceutical Research. Clinical Chemistry 61, 1343\u0026ndash;1353. https://doi.org/10.1373/clinchem.2014.231712.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Effect of age and sex on hematologic parameters of beagle dogs\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge-Sex Interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 7.26,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.99,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.3208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.49,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.7416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 10.74,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.66,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 6.12,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 9.97,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.17,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.54,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0002, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 10.89,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 1.17,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.98,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0006, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.78,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0009, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 1.66,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.29,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.64,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.034, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 4.27,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0039, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.57,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.007, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.62,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.035, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 2.09,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.00039, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 24.74,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 3.32,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 7.29,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Neutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.19,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.00039, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 2.48,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.32,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 15.07,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 1.66,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.34,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.011, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Monocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 9.81,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.39,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.12,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Eosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 10.27,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.41,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.89,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Basophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.87,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.00079, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.24,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAbs. Reticulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 21.29,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 3.52,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.53,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.04, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 78.82,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.18,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.11,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.016, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAPTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 17.29,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 2.46,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.62,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.56,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.15,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.02,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(3, 152) = 22.52,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 4.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.04, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(3, 400) = 5.17,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.002, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP \u0026lt; 0.05 is significant (S).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Effect of age and sex on biochemical parameters of beagle dogs\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge-Sex Interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.19,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.003, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 5.89,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.016, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.36,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.78,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 22.98,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.44,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 6.17,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 29.18,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.87,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 10.37,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 10.11,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.002, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.14,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.86,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.00009, \u003cstrong\u003eS\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 30,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.54,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.04, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.48,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0003, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 23.26,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.05,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.9,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 10.91,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 0.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.04,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0006, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 7.26,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.007, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.14,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 10.85,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 3.93,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.048, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.49,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.043, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 17.36,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.07,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.51,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.008, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 3.37,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.09,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.016, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eGLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 13.41,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 5.27,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.022, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.67,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eUREA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.24,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 3.14,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.24,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.002, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCREAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.98,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.00009, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 29.43,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.06,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCALCIUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.75,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 0.74,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 2.79,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003ePHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 5.04,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0006, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 1.34,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 4.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.002, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eSODIUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 16.1,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 1.27,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 11.24,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003ePOTASSIUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 11.85,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 4.59,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.033, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 1.15,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.3334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eCHLORIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 152) = 9.3,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 400) = 5.09,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.025, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 400) = 3.46,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.009, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 189) = 2.87,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.025, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 189) = 0.49,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(4, 189) = 0.71,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eTBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(3, 105) = 20.93,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = \u0026lt;0.0001, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(1, 105) = 0,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP = 0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5319%;\"\u003e\n \u003cp\u003eF(3, 105) = 3.16,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.028, \u003cstrong\u003eS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP \u0026lt; 0.05 is significant (S).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Effect of age and sex on electrocardiographic parameters\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge-Sex Interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.42,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2273\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 1.13,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2891\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.83,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1222\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eRR interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.38,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2390\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 0.68,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.4084\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.96,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1002\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eP-wave amplitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 3.53,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0076\u003c/em\u003e, \u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 3.48,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0627\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.38,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2390\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eP- wave duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.07,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.3693\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 2.85,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0924\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.60,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1743\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eQRS interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.56,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1834\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 1.50,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2212\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.03,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.3935\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003ePR interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 2.35,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0540\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 8.58,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0036\u003c/em\u003e, \u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 2.31,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0572\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eQT interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 0.75,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.5614\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 2.62,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1065\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.96,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.0996\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eQTc (B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.73,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1430\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 0.25,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.6151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.37,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2419\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eQTc (F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.38,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2390\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 1.19,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.2754\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.52,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1954\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003eQTc (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.20,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.3107\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(1, 436) = 2.15,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1432\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26%;\"\u003e\n \u003cp\u003eF(4, 436) = 1.71,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP = 0.1469\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP \u0026lt; 0.05 is significant (S).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e QT correction in beagle dogs\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart Rate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUncorrected QT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTc (B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTc (F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTc (V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (bpm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=436)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0685\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.4850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.1284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0569\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9-24 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=234)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0683\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=130)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0707\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=104)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0809\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25-36 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n=104)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.6192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.3533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0419\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.6978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.2617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.1293\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37-48 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0480\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.8801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.4405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.4273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.1797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0254\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49-60 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n=27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.7110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.2759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0755\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.7522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0969\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1.1184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.6273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3627\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61-84 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAll data (n=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.6598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.3112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0835\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.7675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0904\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0619\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eABOVE AND BELOW MEAN HEART RATE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart Rate \u0026le; 112 bpm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.9102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2220\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=118)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.4450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.8655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.3672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1831\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.3323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.9776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2801\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart Rate\u0026nbsp;\u003c/strong\u003e\u0026gt;\u003cstrong\u003e112 bpm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=218)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.5099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.3150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0359\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.6018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.1770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.1558\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.2931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=118)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.4271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0722\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.1254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ebpm: beats per minute, n: number of samples, ms: milliseconds, QTc (B): Corrected QT interval by Bazett\u0026rsquo;s formula, QTc (F): Corrected QT interval by Fridericia\u0026rsquo;s formula, QTc (V): Corrected QT interval by Van de Water\u0026rsquo;s formula\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dogs, clinical chemistry, hematology, electrocardiography, nonclinical safety, historical control data","lastPublishedDoi":"10.21203/rs.3.rs-5481836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5481836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study analyzed hematological, biochemical, and electrocardiographic parameters in 436 Beagle dogs (9-84 months old) to establish baseline reference indices by age and sex, and to evaluate the effects of these factors on physiological parameters. Age and sex significantly influence physiological parameters, necessitating comprehensive reference ranges across different age groups and sexes. Blood samples and ECG traces obtained from unanesthetized, nulliparous, and non-pregnant dogs, individually housed in controlled environments, ensured the reflection of actual functional parameters while limiting variability. Significant age-related effects were observed in all hematological parameters except ESR, while sex affected only MCH and RDW. Age-sex interactions significantly influenced several hematological metrics, with the most frequent sex-related differences observed in younger dogs (9-24 and 25-36 months-old age-groups). Biochemical analysis showed significant age effects on glucose, cholesterol, HDL, LDL, AST, ALP, CK, TP, ALB, GLB, CREAT, Phosphorous, electrolytes, GGT and TBL, while sex influenced most parameters except TP, ALB, Urea, Calcium, Phosphorous, Sodium, GGT, and TBL. Electrocardiographic analysis revealed significant age and sex effects on p-wave amplitude and PR interval, respectively, with Fridericia’s formula providing the best correction for QT intervals in comparison to Van de Water’s and Bazett’s formulas. Understanding these variations is essential for accurate clinical assessments, ensuring drug safety, and developing tailored interventions in preclinical research.","manuscriptTitle":"Age and Sex-Dependent Variations in Hematological, Biochemical, and Electrocardiographic Parameters in Beagle Dogs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 08:37:56","doi":"10.21203/rs.3.rs-5481836/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5eb8d242-2553-463b-9e78-c38ffade900d","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43788263,"name":"Biological sciences/Zoology/Animal physiology"},{"id":43788264,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2026-01-08T14:40:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 08:37:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5481836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5481836","identity":"rs-5481836","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}