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Full Research Article

Physio-biochemical Associations and Predictive Modeling of Malondialdehyde, Nitric Oxide and Reduced Glutathione in Clinically Healthy Cats

S
Surangkanang Chaiyasak1
T
Thanaporn Watcharinthada1
C
Chanakarn Veechita1
P
Preechaya Tangsaka1
W
Worapol Aengwanich2,*
1Veterinary Infectious Diseases Research Unit, Faculty of Veterinary Sciences, Mahasarakham University, 44000, Thailand.
2Stress and Oxidative stress in Animal Research Unit of Mahasarakham University; Faculty of Veterinary Science, Mahasarakham University, 44000, Thailand.

ABSTRACT

Background: Oxidative stress significantly impacts feline health, but routine biomarker testing is limited by cost, required sample volume and animal discomfort. Identifying relationships among physiological, hematological and biochemical parameters and developing predictive formulas can improve clinical screening and reduce the need for repeated blood sampling, supporting better health monitoring in companion animal practice. This study aims to investigate the relationship between physiological parameters and oxidative stress and to develop a predictive model for malondialdehyde (MDA), nitric oxide (NO) and reduced glutathione (GSH) levels in clinically healthy domestic cats.

Methods: Twenty healthy domestic short-haired cats were evaluated for physiological variables, hematological profiles and oxidative stress biomarkers, including MDA, NO, GSH, ferric reducing ability of plasma (FRAP) and catalase. Pearson’s correlation and multiple linear regression analyses were used to identify significant associations and create predictive formulas. Model accuracy was validated by comparing calculated biomarker levels with laboratory measurements using paired t-tests.

Result: Significant positive correlations were observed between NO and monocytes, GSH and body temperature, MDA and GSH and FRAP and heart rate (P<0.05). Negative correlations occurred between FRAP and eosinophils and between catalase activity and body condition score (P<0.05). Predictive formulas for estimating MDA, NO and GSH showed high reliability, with no significant differences from laboratory-measured values (P>0.05). These formulas provide clinically applicable tools for estimating oxidative stress biomarkers from simple physiological and hematological parameters, supporting routine health monitoring in companion animal practice while reducing invasive sampling and improving feline welfare.

INTRODUCTION

Cats are among the earliest domesticated animals (Xuan, 2023). Their domestication began in the Near East, possibly in Egypt, around 10,000 years ago, when humans transitioned from hunting to farming and settled in permanent communities (Trouwborst et al., 2020). The storage of food attracted rodents, which were hunted by wildcats. Over time, wildcats adapted to human presence and humans began keeping them for pest control (Lyons et al., 2012). The domestic cat, Felis catus (Linnaeus, 1758), belongs to the Felis lineage and originated from wildcats Felis silvestris (Krajcarz et al., 2022). Evidence of early domesticated cats has also been found in Kazakhstan (Haruda et al., 2020).
       
Physical examination is essential for evaluating health using inspection, palpation, percussion and auscultation to detect clinical signs (Tagesu, 2018). It helps support diagnosis, assess disease severity and guide laboratory investigations (Jain and Jain, 2021). Common assessments include fecal score (FS) (German et al., 2017), capillary refill time (CRT) (Tagesu, 2018), body condition score (BCS), mucous membrane color score (MMCS) and vital signs such as body temperature (BT), respiratory rate (RR) and heart rate (HR) (Sakundech and Aengwanich, 2021).
       
Blood analysis is an important tool for assessing health and monitoring disease progression (Hwang et al., 2016; Moyadee et al., 2019; Spada et al., 2015). Hematological parameters help detect diseases (Moyadee et al., 2019) and also reflect physiological responses and organ functions influenced by environmental changes (Hwang et al., 2016). These measures provide valuable information for health evaluation, adaptation studies and monitoring of natural feline populations (Bai et al., 2021; Hwang et al., 2016).
       
Oxidative stress occurs when reactive oxygen species (ROS) production exceeds antioxidant defense, leading to redox imbalance, lipid and protein damage and DNA fragmentation (Fan et al., 2023). ROS are linked to various diseases, including diabetes, chronic kidney disease, congestive heart failure, neurodegenerative disorders and hemolytic anemia (Michalek et al., 2020; Woolcock et al., 2020). Malondialdehyde (MDA), a product of lipid peroxidation, is commonly used as a biomarker of oxidative stress (Cordiano et al., 2023). Catalase, an important antioxidant enzyme, protects cells by converting hydrogen peroxide into water and oxygen (Gebicka and Krych-Madej, 2019; Promyos et al., 2023). Reduced glutathione (GSH), the most abundant intracellular thiol, maintains redox balance and regulates multiple cellular processes (Necasova et al., 2021; Viviano et al., 2009). Oxidative stress is often associated with decreased GSH levels (Woolcock et al., 2020).
       
The ferric reducing ability of plasma (FRAP) is widely used to measure total antioxidant capacity, especially when the types of antioxidants are unknown (Benzie and Strain, 1996). Nitric oxide (NO), a free radical gas, functions as an inter- and intracellular messenger. It is synthesized from L-arginine by nitric oxide synthase and secreted by various cells (Ghimire et al., 2017; Kayar et al., 2015; Khan et al., 2023). NO plays important roles in both normal physiology and disease and its effects depend on biological context (Kayar et al., 2015).
       
Although biomarkers of oxidative stress such as MDA, NO and GSH are widely used to assess redox status in animals, their clinical application in cats remains limited due to cost, the volume of samples required and the invasive nature of repeated blood collection. Previous studies have explored the relationship between clinical parameters and antioxidant capacity in cats; however, the findings have been inconsistent and mostly focused on single oxidative biomarkers. Importantly, it is still unclear whether physiological and hematological variables, which reflect metabolic regulation, immune function and oxygen transport, can serve as indirect indicators of oxidative balance under normal healthy conditions. Given that redox homeostasis is closely linked to physiological and hematological regulation, investigating these relationships may provide a biologically meaningful and clinically useful alternative for assessing oxidative status, thereby reducing the need for laboratory monitoring.
       
This study hypothesizes that physiological and hematological parameters are associated with oxidative stress indicators and can be used to predict MDA, NO and GSH levels in the bloodstream of healthy cats. Therefore, the objective of this study was to investigate the relationships between physiological and hematological parameters and oxidative stress indicators and to develop predictive models for estimating the concentrations of MDA, NO and GSH in healthy domestic cats. These models may have practical applications for estimating indicator levels, particularly in situations where laboratory resources are limited.

MATERIALS AND METHODS

Ethical considerations
 
This study was approved by the Institutional Ethics Committee on Animal Experimentation of Mahasarakham University (license number: IACUC-MSU-30/2020). All procedures were performed with informed owner consent.
Year of research: Duration of Research: 2021-2024  February 2021-October 2024.
 
Animals
 
Twenty clinically healthy domestic short-haired cats (n = 20; 7 males and 13 females), aged 5 months to 5 years and weighing 2.5-5.0 kg, were included in this study. All cats were neutered, vaccinated, fed commercial diets and kept indoors under similar environmental conditions. A licensed veterinarian examined each cat to confirm its health status before enrollment. The number of animals used was determined using the resource equation method described by Ilyas et al., (2017). Therefore, the sample size was considered appropriate and sufficient for the purpose of this study.
 
Physical examination and physiological assessment
 
Physical examinations and physiological measurements were performed on twenty healthy domestic cats. Before starting, each cat was allowed to rest for about 30 minutes to adjust to the room temperature. This helped reduce stress and stabilize their physiological responses. The following parameters were evaluated carefully for all cats: BCS, MMCS, CRT, FS, BT, RR and HR.
       
Body weight was measured using a digital scale (BW 7842, Nagata, Nagata Scale Co., Ltd., Taiwan). The BCS of each cat was determined using a nine-point scoring system based on body composition, following the methods of Ahammed (2021) and Bjørnvad  et al. (2011). The MMCS was assessed according to the method adapted from Humm and Kellett-Gregory (2016), where scores were recorded as 0 for white mucous membranes, 1 for pale pink and 2 for normal pink.
       
CRT was measured by gently pressing the gum and recording the time taken for the color to return. Values were classified as <2 seconds, exactly 2 seconds, or >2 seconds based on Reineke et al., (2015). The FS was evaluated at the cat owners’ homes using a seven-point scoring system adapted from Felten et al., (2022): 1 = very hard and dry feces,  2 = firm but pliable, 3 = log-shaped, moist surface, retains form, 4 = very moist, soggy, loses form when picked up, 5 = very moist but with a distinct shape,  6 = soft, textured but no clear shape, 7 = watery feces without texture.
       
Rectal body temperature was measured using a dual-scale clinical glass thermometer (Bcosmo®, Thailand). The thermometer was inserted gently into the anus until the mercury column stabilized or for a maximum of three minutes and the temperature was read from the scale following the method of Sousa et al., (2011).
       
The RR was measured by counting the number of breaths per minute while the cats were at rest, using the method described by Duguma (2016). Finally, the HR was measured using a 3M™ Littmann® Cardiology III™ stethoscope placed on the left chest and heartbeats were counted for 60 seconds to record beats per minute.
 
Hematology and biochemistry
 
Blood sampling
 
A total of 4 mL of blood was collected from the saphenous vein using a butterfly needle into EDTA tubes (for hematology) and heparin tubes (for plasma). Plasma samples were separated by centrifugation (2500 rpm; 769 x g, 5 min) and stored at -20°C until biochemical analysis.
 
Hematological investigation
 
EDTA samples were analyzed using an IDEXX ProCyte Dx hematology analyzer (USA) for total red blood cell counts (TRBC), total white blood cell counts (TWBC), platelets, packed cell volume (PCV) and hemoglobin (Hb). Blood smears were stained with Giemsa-Wright solution for white blood cell differential counts.
 
Biochemical assays
 
Ferric reducing ability of plasma (FRAP)
 
Total antioxidant capacity was measured using the FRAP assay (Juntautsa et al., 2026). Fresh FRAP reagent was prepared by mixing sodium acetate buffer (300 mM, pH 3.6), TPTZ (10 mM) and FeCl3·6H2O (20 mM). Supernatants (20 µL) were incubated with 180 µL of FRAP reagent at room temperature for 5 min. Absorbance was measured at 595 nm using a Tecan Infinite® 200 Microplate Reader (Tecan Trading AG, Switzerland), with FeSO4·7H2O as the standard.
 
Malondialdehyde (MDA)
 
MDA levels were determined using the thiobarbituric acid reactive substances (TBARS) assay (Watwiengkam et al., 2024). Briefly, 0.01 mL of plasma was mixed with 3 mL HCl (0.05 mol L-1) and 1 mL TBA (0.67%), heated at 100°C for 30 min, cooled and extracted with 4 mL n-butanol. After vortexing and centrifugation (3000 rpm; 1008xg, 10 min), the absorbance was read at 532 nm using the Tecan Infinite® 200 Microplate Reader. Calibration was performed using 1,1,3,3-tetramethoxypropane standards (0-50 µmol L-1).
 
Nitric oxide (NO)
 
NO levels were quantified using Griess’s reagent (Giustarini et al., 2008). Equal volumes of plasma and Griess reagent, prepared from sulfanilamide (1%), N-(1-naphthyl) ethylenediamine dihydrochloride (0.1%) and phosphoric acid (1.25%), were mixed and incubated for 15 min. Absorbance was measured at 540 nm with sodium nitrite standards.
 
Reduced glutathione (GSH) and catalase
 
GSH concentration and catalase activity were determined using commercial kits (Abbkine, KTB1600 and KTB1040, respectively) following the manufacturer’s instructions. Absorbance was read using the Tecan Infinite® 200 Microplate Reader.
 
Statistical analysis
 
Data analysis was carried out using Pearson’s correlation coefficient to examine relationships among physiological, hematological and biochemical parameters. Predictive models for MDA, NO and GSH were developed using both simple and multiple linear regression analyses. The accuracy of these models was assessed using the coefficient of determination (r2) and further verified by comparing predicted values with laboratory measurements through paired t-tests. Statistical significance was set at P<0.05 and model performance was evaluated using the adjusted r2.

RESULTS AND DISCUSSION

The analysis of correlations among physiological and biochemical parameters in healthy cats showed several significant relationships. Positive correlations (P<0.05) were found between NO and monocytes, GSH and BT, mean corpuscular hemoglobin concentration (MCHC) and GSH, MDA and GSH, PCV and Hb, TRBC and TWBC, TRBC and Hb, TRBC and PCV, HR and TWBC, FRAP and HR, mean corpuscular hemoglobin (MCH) and Hb, MCH and PCV, mean corpuscular volume (MCV) and Hb, MCV and PCV and MCV and MCH (Table 1). Negative correlations (P<0.05) were observed between neutrophils and lymphocytes, FRAP and eosinophils and BCS and catalase activity (Table 1).

Table 1: The correlation between the parameters of physical examination, physiology and biochemistry collected from clinically healthy cats (n=20).


       
Predictive equations for estimating MDA, NO and GSH levels were then developed using multiple and simple linear regression based on measured data and laboratory results. To check their accuracy, observed values were substituted into the formulas and reverse verification tests were performed. Comparisons between the predicted values and direct laboratory measurements of MDA, NO and GSH showed no significant differences (P>0.05) using paired t-tests (Table 2-4).

Table 2: Comparison of MDA between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).



Table 3: Comparison of NO between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).



Table 4: Comparison of GSH between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).


       
The correlations observed in this study indicate that oxidative balance in healthy cats is not an isolated biochemical state but is closely linked to physiological regulation and hematological function. This finding supports the idea that overall redox homeostasis is reflected in measurable clinical and hematological parameters, even under non-pathological conditions. This study found significant relationships between physiological, hematological and biochemical parameters in healthy domestic cats. These findings give new insights into how redox balance, immune control and metabolism work together under normal conditions. Among the oxidative stress biomarkers, NO, GSH, MDA and FRAP showed strong links with immune-related and physiological variables. This suggests that antioxidant systems, oxygen transport and overall body regulation are closely connected, which may help improve screening and monitoring in feline clinical practice.
       
One key result was the positive link between NO and monocyte counts. This suggests that monocytes may influence normal NO production even without inflammation. Bertholet et al., (1999) reported similar findings, showing that U937 monocytic cells release NO when stimulated by lipopolysaccharide, interferon-γ (IFN-γ), interleukin-1 (IL-1) and tumor necrosis factor-α (TNF-α). Normally, NO has two roles: at normal levels, it helps regulate blood flow and signaling, while excessive levels cause nitrosative stress (Förstermann and Sessa, 2012). In cats, small changes in monocytes may help control baseline NO, supporting blood circulation, immune activity and metabolic stability.
       
We also found positive associations between GSH, BT, MCHC and MDA, which highlight GSH as a central regulator of redox balance and thermal control. GSH is the main intracellular antioxidant and helps maintain balance by neutralizing ROS and restoring other antioxidants (Forman et al., 2009). The GSH-BT link agrees with Kolesnichenko et al., (2003), who showed that GSH depletion affects thermal control. Wrotek et al., (2020) also reported that oxidative stress often occurs during fever, meaning sufficient GSH reserves help buffer ROS during high metabolic activity and stabilize heat regulation in cats.
       
The positive relationship between GSH and MDA further suggests a compensatory antioxidant response during oxidative challenges. MDA, a lipid peroxidation byproduct, is widely used as a marker of oxidative damage (Ayala et al., 2014). Dewi and Ayuniastuti (2019) reported that both GSH and MDA increase together in humans exposed to high oxidant levels, indicating that GSH production rises to limit ROS-driven damage. A similar adaptive mechanism likely exists in cats, where increased GSH may protect cell membranes and keep redox levels balanced.
       
PCV, Hb and TRBC showed strong positive correlations, matching known principles of red cell physiology where Hb levels depend on red cell mass (Velguth et al., 2010). These findings are also consistent with Sakundech and Aengwanich (2021), showing hematological stability in healthy cats. Interestingly, we observed a new positive link between TRBC and TWBC, which may suggest a connection between oxygen transport and immune regulation that deserves further study.
       
HR was positively associated with TWBC, a relationship also seen in humans by Inoue et al., (2012), where higher resting HR matched higher TWBC due to shared autonomic-inflammatory pathways. In cats, this may represent cardiovascular-immune coordination, where increased cardiac output supports immune cell activity and efficient oxygen delivery during metabolic demands.
       
FRAP showed a negative relationship with eosinophil counts, suggesting antioxidant use during anti-parasitic or allergic responses (Karacan et al., 2020). Lower FRAP values in cats with higher eosinophils indicate reduced antioxidant capacity under immune activation. BCS also negatively correlated with catalase activity, meaning cats with lower energy reserves compensate by increasing antioxidant enzyme activity. Similar findings in dairy cows were reported by Bernabucci et al., (2005), where reduced energy stores led to more oxidative stress and stronger catalase activation.
       
These results show that oxidative biomarkers, blood indices and physiological traits work together to maintain immune function, antioxidant protection and metabolic balance. New associations, such as between HR and TWBC and between TRBC and TWBC, add more understanding of feline physiology and could improve early clinical detection of stress responses.
       
Among oxidative biomarkers, NO, GSH, MDA and FRAP showed interconnected roles, forming a regulatory network for vascular tone, antioxidant defense and energy metabolism. For example, NO supports blood flow, GSH maintains redox stability (Sakundech and Aengwanich, 2021), MDA reflects lipid peroxidation (Srinontong et al., 2024) and FRAP represents overall antioxidant capacity. Together, these findings extend earlier feline studies (Sakundech and Aengwanich, 2021), showing these markers function as an integrated system and may be used to detect subclinical oxidative imbalances.
       
Hematological and physiological correlations also reveal insights into oxygen delivery and immune responses. PCV, Hb and TRBC were closely linked, confirming strict control of red blood cell production in cats. Other relationships among TWBC, HR, the neutrophil-to-lymphocyte ratio (NLR) and BCS with catalase activity emphasize how immune activity, metabolism and antioxidant defenses are connected.
       
A new observation was the TWBC-HR correlation, not reported before in cats. Humans show similar results where increased sympathetic activity and catecholamine release raise TWBC and HR together (Inoue et al., 2012; Elenkov et al., 2000). This suggests cats also use a similar coordinated response to support blood circulation and immune activity during metabolic stress.
       
The negative link between neutrophil and lymphocyte counts shows the balance between innate and adaptive immunity. Neutrophils act in early defenses by producing ROS, while lymphocytes handle targeted responses. Under mild stress, glucocorticoids raise neutrophils and reduce lymphocytes, increasing the NLR (Buonacera et al., 2022). Similar findings in cats were reported by Sakundech and Aengwanich (2021). Growing evidence also supports NLR as a biomarker for inflammation and stress in cats (Fries et al., 2022).
       
The inverse BCS-catalase relationship suggests cats with low energy reserves face higher oxidative loads, driving more catalase activity to protect cells (Gebicka and Krych-Madej, 2019). Comparable results from dairy cows and other species (Bernabucci et al., 2005; Candellone et al., 2022) confirm catalase as a good indicator of oxidative balance in clinical feline monitoring.
       
Finally, predictive formulas were built to estimate MDA, NO and GSH levels using simple blood parameters and physiological data. These models explained 53-60% of biomarker variability, with adjusted r2 ranging from 0.5285 to 0.6028 and matched lab values well based on paired t-tests. These tools may help veterinarians check oxidative status in cats without frequent lab testing, reducing stress and improving welfare. They may also support screening in routine wellness visits, early detection of subclinical changes and monitoring at-risk cats (Michalek et al., 2020; Woolcock et al., 2020).
       
Although the models performed well, further validation with larger and more diverse cat populations is needed to improve reliability and wider clinical use (Harrell, 2015). Future studies should also compare results across healthy and diseased cats to enhance their diagnostic value and support preventive veterinary care. Despite the promising performance of the predictive models, the relatively small sample size may limit generalizability. Therefore, future studies involving larger and more diverse feline populations are required to validate and refine these equations before broader clinical application.

CONCLUSION

This study demonstrates a significant relationship between physiological indicators, hematological parameters and oxidative stress in healthy domestic cats. The results suggest that oxidative reduction balance is closely linked to the physiological regulation of multiple body systems. The predictive models developed in this study explained a significant proportion of the variation in MDA, NO and GSH levels and showed good agreement with laboratory measurements. These findings indicate that common clinical parameters may provide biologically meaningful estimates of oxidative status, offering a practical and non-invasive approach for feline health monitoring in veterinary practice. Further, investigation in larger populations is needed to improve clinical applicability.

ACKNOWLEDGEMENT

The authors would like to thank the Faculty of Veterinary Sciences, Mahasarakham University for the facility support.
 
Funding
 
This research project is financially supported by Mahasarakham University.

CONFLICT OF INTEREST

The authors have no conflicts of interest to declare.

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    Physio-biochemical Associations and Predictive Modeling of Malondialdehyde, Nitric Oxide and Reduced Glutathione in Clinically Healthy Cats

    S
    Surangkanang Chaiyasak1
    T
    Thanaporn Watcharinthada1
    C
    Chanakarn Veechita1
    P
    Preechaya Tangsaka1
    W
    Worapol Aengwanich2,*
    1Veterinary Infectious Diseases Research Unit, Faculty of Veterinary Sciences, Mahasarakham University, 44000, Thailand.
    2Stress and Oxidative stress in Animal Research Unit of Mahasarakham University; Faculty of Veterinary Science, Mahasarakham University, 44000, Thailand.

    ABSTRACT

    Background: Oxidative stress significantly impacts feline health, but routine biomarker testing is limited by cost, required sample volume and animal discomfort. Identifying relationships among physiological, hematological and biochemical parameters and developing predictive formulas can improve clinical screening and reduce the need for repeated blood sampling, supporting better health monitoring in companion animal practice. This study aims to investigate the relationship between physiological parameters and oxidative stress and to develop a predictive model for malondialdehyde (MDA), nitric oxide (NO) and reduced glutathione (GSH) levels in clinically healthy domestic cats.

    Methods: Twenty healthy domestic short-haired cats were evaluated for physiological variables, hematological profiles and oxidative stress biomarkers, including MDA, NO, GSH, ferric reducing ability of plasma (FRAP) and catalase. Pearson’s correlation and multiple linear regression analyses were used to identify significant associations and create predictive formulas. Model accuracy was validated by comparing calculated biomarker levels with laboratory measurements using paired t-tests.

    Result: Significant positive correlations were observed between NO and monocytes, GSH and body temperature, MDA and GSH and FRAP and heart rate (P<0.05). Negative correlations occurred between FRAP and eosinophils and between catalase activity and body condition score (P<0.05). Predictive formulas for estimating MDA, NO and GSH showed high reliability, with no significant differences from laboratory-measured values (P>0.05). These formulas provide clinically applicable tools for estimating oxidative stress biomarkers from simple physiological and hematological parameters, supporting routine health monitoring in companion animal practice while reducing invasive sampling and improving feline welfare.

    INTRODUCTION

    Cats are among the earliest domesticated animals (Xuan, 2023). Their domestication began in the Near East, possibly in Egypt, around 10,000 years ago, when humans transitioned from hunting to farming and settled in permanent communities (Trouwborst et al., 2020). The storage of food attracted rodents, which were hunted by wildcats. Over time, wildcats adapted to human presence and humans began keeping them for pest control (Lyons et al., 2012). The domestic cat, Felis catus (Linnaeus, 1758), belongs to the Felis lineage and originated from wildcats Felis silvestris (Krajcarz et al., 2022). Evidence of early domesticated cats has also been found in Kazakhstan (Haruda et al., 2020).
           
    Physical examination is essential for evaluating health using inspection, palpation, percussion and auscultation to detect clinical signs (Tagesu, 2018). It helps support diagnosis, assess disease severity and guide laboratory investigations (Jain and Jain, 2021). Common assessments include fecal score (FS) (German et al., 2017), capillary refill time (CRT) (Tagesu, 2018), body condition score (BCS), mucous membrane color score (MMCS) and vital signs such as body temperature (BT), respiratory rate (RR) and heart rate (HR) (Sakundech and Aengwanich, 2021).
           
    Blood analysis is an important tool for assessing health and monitoring disease progression (Hwang et al., 2016; Moyadee et al., 2019; Spada et al., 2015). Hematological parameters help detect diseases (Moyadee et al., 2019) and also reflect physiological responses and organ functions influenced by environmental changes (Hwang et al., 2016). These measures provide valuable information for health evaluation, adaptation studies and monitoring of natural feline populations (Bai et al., 2021; Hwang et al., 2016).
           
    Oxidative stress occurs when reactive oxygen species (ROS) production exceeds antioxidant defense, leading to redox imbalance, lipid and protein damage and DNA fragmentation (Fan et al., 2023). ROS are linked to various diseases, including diabetes, chronic kidney disease, congestive heart failure, neurodegenerative disorders and hemolytic anemia (Michalek et al., 2020; Woolcock et al., 2020). Malondialdehyde (MDA), a product of lipid peroxidation, is commonly used as a biomarker of oxidative stress (Cordiano et al., 2023). Catalase, an important antioxidant enzyme, protects cells by converting hydrogen peroxide into water and oxygen (Gebicka and Krych-Madej, 2019; Promyos et al., 2023). Reduced glutathione (GSH), the most abundant intracellular thiol, maintains redox balance and regulates multiple cellular processes (Necasova et al., 2021; Viviano et al., 2009). Oxidative stress is often associated with decreased GSH levels (Woolcock et al., 2020).
           
    The ferric reducing ability of plasma (FRAP) is widely used to measure total antioxidant capacity, especially when the types of antioxidants are unknown (Benzie and Strain, 1996). Nitric oxide (NO), a free radical gas, functions as an inter- and intracellular messenger. It is synthesized from L-arginine by nitric oxide synthase and secreted by various cells (Ghimire et al., 2017; Kayar et al., 2015; Khan et al., 2023). NO plays important roles in both normal physiology and disease and its effects depend on biological context (Kayar et al., 2015).
           
    Although biomarkers of oxidative stress such as MDA, NO and GSH are widely used to assess redox status in animals, their clinical application in cats remains limited due to cost, the volume of samples required and the invasive nature of repeated blood collection. Previous studies have explored the relationship between clinical parameters and antioxidant capacity in cats; however, the findings have been inconsistent and mostly focused on single oxidative biomarkers. Importantly, it is still unclear whether physiological and hematological variables, which reflect metabolic regulation, immune function and oxygen transport, can serve as indirect indicators of oxidative balance under normal healthy conditions. Given that redox homeostasis is closely linked to physiological and hematological regulation, investigating these relationships may provide a biologically meaningful and clinically useful alternative for assessing oxidative status, thereby reducing the need for laboratory monitoring.
           
    This study hypothesizes that physiological and hematological parameters are associated with oxidative stress indicators and can be used to predict MDA, NO and GSH levels in the bloodstream of healthy cats. Therefore, the objective of this study was to investigate the relationships between physiological and hematological parameters and oxidative stress indicators and to develop predictive models for estimating the concentrations of MDA, NO and GSH in healthy domestic cats. These models may have practical applications for estimating indicator levels, particularly in situations where laboratory resources are limited.

    MATERIALS AND METHODS

    Ethical considerations
     
    This study was approved by the Institutional Ethics Committee on Animal Experimentation of Mahasarakham University (license number: IACUC-MSU-30/2020). All procedures were performed with informed owner consent.
    Year of research: Duration of Research: 2021-2024  February 2021-October 2024.
     
    Animals
     
    Twenty clinically healthy domestic short-haired cats (n = 20; 7 males and 13 females), aged 5 months to 5 years and weighing 2.5-5.0 kg, were included in this study. All cats were neutered, vaccinated, fed commercial diets and kept indoors under similar environmental conditions. A licensed veterinarian examined each cat to confirm its health status before enrollment. The number of animals used was determined using the resource equation method described by Ilyas et al., (2017). Therefore, the sample size was considered appropriate and sufficient for the purpose of this study.
     
    Physical examination and physiological assessment
     
    Physical examinations and physiological measurements were performed on twenty healthy domestic cats. Before starting, each cat was allowed to rest for about 30 minutes to adjust to the room temperature. This helped reduce stress and stabilize their physiological responses. The following parameters were evaluated carefully for all cats: BCS, MMCS, CRT, FS, BT, RR and HR.
           
    Body weight was measured using a digital scale (BW 7842, Nagata, Nagata Scale Co., Ltd., Taiwan). The BCS of each cat was determined using a nine-point scoring system based on body composition, following the methods of Ahammed (2021) and Bjørnvad  et al. (2011). The MMCS was assessed according to the method adapted from Humm and Kellett-Gregory (2016), where scores were recorded as 0 for white mucous membranes, 1 for pale pink and 2 for normal pink.
           
    CRT was measured by gently pressing the gum and recording the time taken for the color to return. Values were classified as <2 seconds, exactly 2 seconds, or >2 seconds based on Reineke et al., (2015). The FS was evaluated at the cat owners’ homes using a seven-point scoring system adapted from Felten et al., (2022): 1 = very hard and dry feces,  2 = firm but pliable, 3 = log-shaped, moist surface, retains form, 4 = very moist, soggy, loses form when picked up, 5 = very moist but with a distinct shape,  6 = soft, textured but no clear shape, 7 = watery feces without texture.
           
    Rectal body temperature was measured using a dual-scale clinical glass thermometer (Bcosmo®, Thailand). The thermometer was inserted gently into the anus until the mercury column stabilized or for a maximum of three minutes and the temperature was read from the scale following the method of Sousa et al., (2011).
           
    The RR was measured by counting the number of breaths per minute while the cats were at rest, using the method described by Duguma (2016). Finally, the HR was measured using a 3M™ Littmann® Cardiology III™ stethoscope placed on the left chest and heartbeats were counted for 60 seconds to record beats per minute.
     
    Hematology and biochemistry
     
    Blood sampling
     
    A total of 4 mL of blood was collected from the saphenous vein using a butterfly needle into EDTA tubes (for hematology) and heparin tubes (for plasma). Plasma samples were separated by centrifugation (2500 rpm; 769 x g, 5 min) and stored at -20°C until biochemical analysis.
     
    Hematological investigation
     
    EDTA samples were analyzed using an IDEXX ProCyte Dx hematology analyzer (USA) for total red blood cell counts (TRBC), total white blood cell counts (TWBC), platelets, packed cell volume (PCV) and hemoglobin (Hb). Blood smears were stained with Giemsa-Wright solution for white blood cell differential counts.
     
    Biochemical assays
     
    Ferric reducing ability of plasma (FRAP)
     
    Total antioxidant capacity was measured using the FRAP assay (Juntautsa et al., 2026). Fresh FRAP reagent was prepared by mixing sodium acetate buffer (300 mM, pH 3.6), TPTZ (10 mM) and FeCl3·6H2O (20 mM). Supernatants (20 µL) were incubated with 180 µL of FRAP reagent at room temperature for 5 min. Absorbance was measured at 595 nm using a Tecan Infinite® 200 Microplate Reader (Tecan Trading AG, Switzerland), with FeSO4·7H2O as the standard.
     
    Malondialdehyde (MDA)
     
    MDA levels were determined using the thiobarbituric acid reactive substances (TBARS) assay (Watwiengkam et al., 2024). Briefly, 0.01 mL of plasma was mixed with 3 mL HCl (0.05 mol L-1) and 1 mL TBA (0.67%), heated at 100°C for 30 min, cooled and extracted with 4 mL n-butanol. After vortexing and centrifugation (3000 rpm; 1008xg, 10 min), the absorbance was read at 532 nm using the Tecan Infinite® 200 Microplate Reader. Calibration was performed using 1,1,3,3-tetramethoxypropane standards (0-50 µmol L-1).
     
    Nitric oxide (NO)
     
    NO levels were quantified using Griess’s reagent (Giustarini et al., 2008). Equal volumes of plasma and Griess reagent, prepared from sulfanilamide (1%), N-(1-naphthyl) ethylenediamine dihydrochloride (0.1%) and phosphoric acid (1.25%), were mixed and incubated for 15 min. Absorbance was measured at 540 nm with sodium nitrite standards.
     
    Reduced glutathione (GSH) and catalase
     
    GSH concentration and catalase activity were determined using commercial kits (Abbkine, KTB1600 and KTB1040, respectively) following the manufacturer’s instructions. Absorbance was read using the Tecan Infinite® 200 Microplate Reader.
     
    Statistical analysis
     
    Data analysis was carried out using Pearson’s correlation coefficient to examine relationships among physiological, hematological and biochemical parameters. Predictive models for MDA, NO and GSH were developed using both simple and multiple linear regression analyses. The accuracy of these models was assessed using the coefficient of determination (r2) and further verified by comparing predicted values with laboratory measurements through paired t-tests. Statistical significance was set at P<0.05 and model performance was evaluated using the adjusted r2.

    RESULTS AND DISCUSSION

    The analysis of correlations among physiological and biochemical parameters in healthy cats showed several significant relationships. Positive correlations (P<0.05) were found between NO and monocytes, GSH and BT, mean corpuscular hemoglobin concentration (MCHC) and GSH, MDA and GSH, PCV and Hb, TRBC and TWBC, TRBC and Hb, TRBC and PCV, HR and TWBC, FRAP and HR, mean corpuscular hemoglobin (MCH) and Hb, MCH and PCV, mean corpuscular volume (MCV) and Hb, MCV and PCV and MCV and MCH (Table 1). Negative correlations (P<0.05) were observed between neutrophils and lymphocytes, FRAP and eosinophils and BCS and catalase activity (Table 1).

    Table 1: The correlation between the parameters of physical examination, physiology and biochemistry collected from clinically healthy cats (n=20).


           
    Predictive equations for estimating MDA, NO and GSH levels were then developed using multiple and simple linear regression based on measured data and laboratory results. To check their accuracy, observed values were substituted into the formulas and reverse verification tests were performed. Comparisons between the predicted values and direct laboratory measurements of MDA, NO and GSH showed no significant differences (P>0.05) using paired t-tests (Table 2-4).

    Table 2: Comparison of MDA between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).



    Table 3: Comparison of NO between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).



    Table 4: Comparison of GSH between values obtained from calculation using formula and measured in the laboratory of healthy cats (n=20).


           
    The correlations observed in this study indicate that oxidative balance in healthy cats is not an isolated biochemical state but is closely linked to physiological regulation and hematological function. This finding supports the idea that overall redox homeostasis is reflected in measurable clinical and hematological parameters, even under non-pathological conditions. This study found significant relationships between physiological, hematological and biochemical parameters in healthy domestic cats. These findings give new insights into how redox balance, immune control and metabolism work together under normal conditions. Among the oxidative stress biomarkers, NO, GSH, MDA and FRAP showed strong links with immune-related and physiological variables. This suggests that antioxidant systems, oxygen transport and overall body regulation are closely connected, which may help improve screening and monitoring in feline clinical practice.
           
    One key result was the positive link between NO and monocyte counts. This suggests that monocytes may influence normal NO production even without inflammation. Bertholet et al., (1999) reported similar findings, showing that U937 monocytic cells release NO when stimulated by lipopolysaccharide, interferon-γ (IFN-γ), interleukin-1 (IL-1) and tumor necrosis factor-α (TNF-α). Normally, NO has two roles: at normal levels, it helps regulate blood flow and signaling, while excessive levels cause nitrosative stress (Förstermann and Sessa, 2012). In cats, small changes in monocytes may help control baseline NO, supporting blood circulation, immune activity and metabolic stability.
           
    We also found positive associations between GSH, BT, MCHC and MDA, which highlight GSH as a central regulator of redox balance and thermal control. GSH is the main intracellular antioxidant and helps maintain balance by neutralizing ROS and restoring other antioxidants (Forman et al., 2009). The GSH-BT link agrees with Kolesnichenko et al., (2003), who showed that GSH depletion affects thermal control. Wrotek et al., (2020) also reported that oxidative stress often occurs during fever, meaning sufficient GSH reserves help buffer ROS during high metabolic activity and stabilize heat regulation in cats.
           
    The positive relationship between GSH and MDA further suggests a compensatory antioxidant response during oxidative challenges. MDA, a lipid peroxidation byproduct, is widely used as a marker of oxidative damage (Ayala et al., 2014). Dewi and Ayuniastuti (2019) reported that both GSH and MDA increase together in humans exposed to high oxidant levels, indicating that GSH production rises to limit ROS-driven damage. A similar adaptive mechanism likely exists in cats, where increased GSH may protect cell membranes and keep redox levels balanced.
           
    PCV, Hb and TRBC showed strong positive correlations, matching known principles of red cell physiology where Hb levels depend on red cell mass (Velguth et al., 2010). These findings are also consistent with Sakundech and Aengwanich (2021), showing hematological stability in healthy cats. Interestingly, we observed a new positive link between TRBC and TWBC, which may suggest a connection between oxygen transport and immune regulation that deserves further study.
           
    HR was positively associated with TWBC, a relationship also seen in humans by Inoue et al., (2012), where higher resting HR matched higher TWBC due to shared autonomic-inflammatory pathways. In cats, this may represent cardiovascular-immune coordination, where increased cardiac output supports immune cell activity and efficient oxygen delivery during metabolic demands.
           
    FRAP showed a negative relationship with eosinophil counts, suggesting antioxidant use during anti-parasitic or allergic responses (Karacan et al., 2020). Lower FRAP values in cats with higher eosinophils indicate reduced antioxidant capacity under immune activation. BCS also negatively correlated with catalase activity, meaning cats with lower energy reserves compensate by increasing antioxidant enzyme activity. Similar findings in dairy cows were reported by Bernabucci et al., (2005), where reduced energy stores led to more oxidative stress and stronger catalase activation.
           
    These results show that oxidative biomarkers, blood indices and physiological traits work together to maintain immune function, antioxidant protection and metabolic balance. New associations, such as between HR and TWBC and between TRBC and TWBC, add more understanding of feline physiology and could improve early clinical detection of stress responses.
           
    Among oxidative biomarkers, NO, GSH, MDA and FRAP showed interconnected roles, forming a regulatory network for vascular tone, antioxidant defense and energy metabolism. For example, NO supports blood flow, GSH maintains redox stability (Sakundech and Aengwanich, 2021), MDA reflects lipid peroxidation (Srinontong et al., 2024) and FRAP represents overall antioxidant capacity. Together, these findings extend earlier feline studies (Sakundech and Aengwanich, 2021), showing these markers function as an integrated system and may be used to detect subclinical oxidative imbalances.
           
    Hematological and physiological correlations also reveal insights into oxygen delivery and immune responses. PCV, Hb and TRBC were closely linked, confirming strict control of red blood cell production in cats. Other relationships among TWBC, HR, the neutrophil-to-lymphocyte ratio (NLR) and BCS with catalase activity emphasize how immune activity, metabolism and antioxidant defenses are connected.
           
    A new observation was the TWBC-HR correlation, not reported before in cats. Humans show similar results where increased sympathetic activity and catecholamine release raise TWBC and HR together (Inoue et al., 2012; Elenkov et al., 2000). This suggests cats also use a similar coordinated response to support blood circulation and immune activity during metabolic stress.
           
    The negative link between neutrophil and lymphocyte counts shows the balance between innate and adaptive immunity. Neutrophils act in early defenses by producing ROS, while lymphocytes handle targeted responses. Under mild stress, glucocorticoids raise neutrophils and reduce lymphocytes, increasing the NLR (Buonacera et al., 2022). Similar findings in cats were reported by Sakundech and Aengwanich (2021). Growing evidence also supports NLR as a biomarker for inflammation and stress in cats (Fries et al., 2022).
           
    The inverse BCS-catalase relationship suggests cats with low energy reserves face higher oxidative loads, driving more catalase activity to protect cells (Gebicka and Krych-Madej, 2019). Comparable results from dairy cows and other species (Bernabucci et al., 2005; Candellone et al., 2022) confirm catalase as a good indicator of oxidative balance in clinical feline monitoring.
           
    Finally, predictive formulas were built to estimate MDA, NO and GSH levels using simple blood parameters and physiological data. These models explained 53-60% of biomarker variability, with adjusted r2 ranging from 0.5285 to 0.6028 and matched lab values well based on paired t-tests. These tools may help veterinarians check oxidative status in cats without frequent lab testing, reducing stress and improving welfare. They may also support screening in routine wellness visits, early detection of subclinical changes and monitoring at-risk cats (Michalek et al., 2020; Woolcock et al., 2020).
           
    Although the models performed well, further validation with larger and more diverse cat populations is needed to improve reliability and wider clinical use (Harrell, 2015). Future studies should also compare results across healthy and diseased cats to enhance their diagnostic value and support preventive veterinary care. Despite the promising performance of the predictive models, the relatively small sample size may limit generalizability. Therefore, future studies involving larger and more diverse feline populations are required to validate and refine these equations before broader clinical application.

    CONCLUSION

    This study demonstrates a significant relationship between physiological indicators, hematological parameters and oxidative stress in healthy domestic cats. The results suggest that oxidative reduction balance is closely linked to the physiological regulation of multiple body systems. The predictive models developed in this study explained a significant proportion of the variation in MDA, NO and GSH levels and showed good agreement with laboratory measurements. These findings indicate that common clinical parameters may provide biologically meaningful estimates of oxidative status, offering a practical and non-invasive approach for feline health monitoring in veterinary practice. Further, investigation in larger populations is needed to improve clinical applicability.

    ACKNOWLEDGEMENT

    The authors would like to thank the Faculty of Veterinary Sciences, Mahasarakham University for the facility support.
     
    Funding
     
    This research project is financially supported by Mahasarakham University.

    CONFLICT OF INTEREST

    The authors have no conflicts of interest to declare.

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