Bovine ketosis: A comprehensive review on pathophysiology, diagnosis, monitoring strategies, therapeutics, and prevention in dairy cattle

play pivotal roles in antioxidant defense. These minerals are integral to the activity of enzymes such as superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase, which mitigate the harmful effects of ROS. SOD (Cu-, Zn-, Mn-dependent) converts superoxide radicals into hydrogen peroxide, which is then broken down by catalase (Fe-dependent) and GSH-Px (Se-dependent) into water, thereby neutralizing oxidative damage (Bowman et al., 2008) [10] . High-yielding dairy cows often enter a state of negative energy balance in early lactation due to the mismatch between energy intake and the demands of milk production. This metabolic imbalance triggers the mobilization of body fat reserves and leads to hypoglycemia, predisposing animals to metabolic conditions such as fatty liver and ketosis (Bobe et al., 2004; Djokovic et al., 2011) [8,18,45] . Factors like age, genetics, over-conditioning, and inadequate prepartum nutrition further exacerbate NEB (Bobe et al., 2004 and Le Blanc et al., 2005) [8] . The downstream consequences include increased incidence of reproductive disorders, displaced abomasum, immune suppression, and decreased feed intake and milk yield (Houe et al., 2001; Ingvartsen et al., 2003 and Le Blanc, 2008) [41,42] . Ketosis, in particular, represents a significant metabolic disorder characterized by elevated levels of ketone bodies βhydroxybutyrate, acetoacetate (AcAc), and acetone (Ace) in the blood, urine, and milk. The condition typically arises shortly after parturition when NEB is most pronounced. Reduced glucose availability leads to increased lipolysis and the release of non-esterified fatty acids (NEFAs) from adipose tissue, which are transported to the liver. There, they are incompletely oxidized due to limited oxaloacetate availability and are instead converted to ketone bodies. While these ketones serve as alternative energy sources, especially for the brain, their accumulation impairs feed intake, suppresses immune function, and increases the risk of concurrent diseases. Diagnosis of ketosis in the early or subclinical stage is vital for preventing production losses, yet there remains a need to correlate blood biochemical markers including glucose, NEFAs, and ketone concentrations for effective monitoring.

Conventional view of ketosis

Transition dairy cows undergo profound physiological and metabolic changes as they move from late gestation to early lactation. This phase, commonly referred to as the transition period, is critical for the health and productivity of dairy cows, yet it is frequently associated with metabolic disorders most notably ketosis. Ketosis is a prevalent condition characterized by elevated levels of ketone bodies acetoacetate (AcAc), acetone (Ac), and β-hydroxybutyrate in blood, urine, and milk during early lactation (Oetzel, 2004 and Tehrani-Sharif et al., 2011) [64,85] . Since the late 1990s, ketosis has surpassed other metabolic disorders such as ruminal acidosis and milk fever, becoming one of the most significant challenges to metabolic health in North American dairy herds (Oetzel, 2007) [63] . Epidemiological studies indicate that nearly 40% of dairy cows in North America develop some form of ketosis within weeks of parturition, with prevalence varying widely across farms and peaking as high as 80% in certain herds (Duffield, 2000) [23] . The repercussions of ketosis are far-reaching, including reduced milk yield, poor reproductive performance (e.g., infertility), and increased susceptibility to periparturient diseases such as displaced abomasum, mastitis, metritis, lameness, and retained placenta. These effects also contribute to a higher culling rate and economic losses (Duffield et al., 2009; Ospina et al., 2010; McArt et al., 2011 and Raboisson et al., 2014) [56,66,70] . The economic cost of a single case of ketosis has been estimated at approximately CAD $50-100 per affected cow (Duffield, 2000) which is approximately 3000-6000 Indian Rupees [23] . Despite decades of research and comprehensive reviews on ketosis (Duffield, 2000; Ospina et al., 2010 and Gordon, 2013) [23,31,66] , the precise pathophysiology remains incompletely understood. One prevailing theory emphasizes the development of negative energy balance (NEB) around parturition due to reduced dry matter intake (DMI) and the high energy demands of milk synthesis (Herdt, 2000) [40] . During NEB, adipose tissue mobilizes free fatty acids into circulation, mainly as non-esterified fatty acids (NEFAs), which follow one of four hepatic metabolic pathways: (1) Complete oxidation via the tricarboxylic acid (TCA) cycle to produce energy, (2) Incomplete oxidation resulting in ketone body production, (3) Re-esterification into triacylglycerols (TAGs) for storage, or (4) Export from the liver as very lowdensity lipoproteins (VLDL) (Grummer, 2008) [35] . Although these hepatic adaptations are essential to meet the energy demands of the postpartum cow, the liver's gluconeogenic capacity is often insufficient. Between 60% and 85% of circulating glucose is directed toward the mammary gland for lactose synthesis, limiting systemic glucose availability (Knowlton et al., 1998) [48] . This glucose deficit contributes to hypoglycemia and hypoinsulinemia, which further promote lipolysis and ketogenesis (Gordon, 2013) [31] . Hepatic ketogenesis, in turn, leads to the accumulation of ketone bodies that serve as alternate energy substrates, particularly for extrahepatic tissues such as the brain (Newman and Verdin, 2014) [62] . However, excessive ketone production can overwhelm homeostatic mechanisms and impair liver function, resulting in TAG accumulation, fatty liver, and further disruption of gluconeogenesis. Interestingly, fat accumulation in the liver can begin during the dry-off period or even the previous lactation, potentially predisposing cows to ketosis in the subsequent calving cycle (Drackley et al., 2001 and Hayirli, 2006) [21,39] . Although most research has focused on postpartum measurements of ketone bodies for the diagnosis of ketosis, limited studies have investigated prepartum metabolic markers in blood and urine that may predict postpartum ketosis. Further research is necessary to elucidate the metabolic alterations during the prepartum phase and to better understand the underlying pathobiology of ketosis in transition dairy cows.

Classification of Ketosis

The classification of bovine ketosis varies among researchers, with two primary frameworks commonly employed in both clinical and academic settings. The first approach classifies ketosis based on the concentration of β-hydroxybutyrate in the blood and the presence or absence of clinical symptoms, while the second system categorizes the disorder based on its etiology and the timing of onset during lactation.

Classification by Clinical Manifestation: Subclinical vs. Clinical Ketosis:

One widely accepted method differentiates between subclinical ketosis (SCK) and clinical ketosis (CK). This classification relies primarily on measuring BHBA levels in the blood and assessing the presence of outward clinical signs. In subclinical ketosis, cows exhibit elevated ketone body concentrations-typically in the range of 1200 to 1400 µmol/L BHBA-but do not show visible signs of illness. These animals generally maintain their appetite and dry matter intake (DMI), and may go unnoticed without metabolic testing (Duffield, 2000) [23] . In contrast, clinical ketosis is characterized by more severe hyperketonemia (BHBA between 2600 to 3000 µmol/L), hypoglycemia, and clear clinical signs. These signs include anorexia or reduced appetite, marked body weight loss, decline in milk production, dry, firm feces, and general lethargy. CK is more readily diagnosed due to these visible indicators. However, it is important to note that the distinction between clinical and subclinical forms is not always clear-cut. Studies have shown that cows with high ketone levels may not always display symptoms, while those with comparatively low levels may exhibit clear illness (McArt et al., 2011) [56] . Thus, the individual cow's ability to tolerate and metabolize ketone bodies plays a pivotal role in symptom expression (Herdt, 2000) [40] . In large, loose-housed herds, clinical assessment becomes increasingly challenging, making BHBA-based testing a more objective and practical approach for herd-level monitoring. This has led to the proposal that the disorder may be more accurately termed hyperketonemia, encompassing both symptomatic and asymptomatic cases, rather than maintaining a rigid dichotomy of subclinical and clinical forms. Type I Ketosis: This is the classic form of ketosis and generally occurs 3 to 6 weeks postpartum, coinciding with peak milk production. It is analogous to Type I diabetes mellitus in humans, where there is a deficiency of insulin and chronic hypoglycemia. In this type, cows experience insufficient gluconeogenesis due to limited availability of glucose precursors, primarily propionate from rumen fermentation and amino acids from muscle protein breakdown. As a result, energy demands exceed supply, triggering lipolysis and subsequent ketone body formation to compensate for glucose scarcity. Type I ketosis is typically associated with normal to low insulin levels and occurs in otherwise healthy cows during high milk output.

Type II Ketosis

Occurring immediately postpartum, Type II ketosis is often associated with obesity, overfeeding during the dry period, and concurrent disorders such as fatty liver syndrome. It resembles Type II diabetes mellitus in humans, being linked to insulin resistance, where cows present with elevated levels of both glucose and insulin at the time of ketosis diagnosis. The excessive energy intake during the dry period promotes adipose tissue deposition, which is mobilized rapidly after parturition. The liver becomes overwhelmed with NEFAs, leading to triglyceride accumulation, impaired gluconeogenesis, and hepatocellular dysfunction. Type II ketosis tends to have a more complicated metabolic profile and is often more difficult to manage than Type I.

Butyric Acid Silage Ketosis

This form of ketosis is associated with the consumption of silage containing high levels of butyric acid, a potent ketogenic precursor. Butyric acid silage results from improper fermentation, often due to clostridial contamination under moist or carbohydrate-deficient ensiling conditions. Clostridial activity leads to an accumulation of butyric acid, which is absorbed by the cow and directly contributes to increased ketogenesis. The clinical expression of this type of ketosis depends on the amount of silage consumed, lactation stage, and the nutritional balance of the overall diet. Risk factors such as early lactation, ruminal acidosis, high milk yield, low dietary energy, and excess dietary protein may further predispose animals to this form.

Clinical Relevance of Classification

Although distinctions between the types of ketosis help understand its underlying pathophysiology, the most practical and widely used categorization remains the division into subclinical and clinical forms, especially for on-farm diagnostics (Duffield, 2000) [23] . The variable presentation of clinical signs relative to blood BHBA levels further complicates classification, supporting the argument for adopting the broader term hyperketonemia (Herdt, 2000) [40] . From a clinical management perspective, this approach provides a more inclusive framework for addressing the metabolic complexity of ketosis and implementing timely intervention strategies.

Incidence of ketosis

The incidence of ketosis in India has been extensively studied by various researchers, shedding light on its prevalence among dairy cows. Different studies conducted by researchers have reported varying rates of occurrence of both subclinical and clinical ketosis. Duffield (2000) [23] highlighted that the early phase of lactation stands as a critical period, noting a prevalence of 40 to 60% cases of subclinical ketosis and an incidence of clinical ketosis ranging from 2% to 15% in cows. Studies conducted by Bihani et al. (2002) [7] indicated a lower prevalence of ketosis at 9.90% during the second to fifth parity, specifically occurring within 60 to 80 days postpartum, with the highest prevalence recorded during the months of November to January (Akamatsu et al., 2007). Mir and Malik (2002) [1,61] noted varying prevalence rates, with the highest incidence observed in cows aged 8-9 years (47.36%) during the third lactation phase (45.10%) and within 1-2 months post-partum (42.10%). Singh (2002) [79] described ketosis as a common metabolic disease occurring within the first 10-60 days postpartum. High-producing Holstein cows were reported to have a higher prevalence of subclinical ketosis (38%) compared to low producer dairy cows (Pourjafar and Heidari, 2003) [69] . Chakrabarti (2006) [13] documented a higher prevalence of ketosis in high-yielding cows, generally occurring within a month after calving. Other studies, such as those conducted by Sakha et al. (2007) [77] , Arya (2008), Thirunavukkarasu et al. (2010, 2011) [2,86] , and various researchers, showed prevalence rates ranging from 9.38% to 37.25%, with variations observed based on seasons, with higher incidences during the summer and rainy seasons compared to winter. Research by LeBlanc ( 2010 [3,9,15,51,55,66] further highlighted varying prevalence rates of ketosis, showcasing the significance of post-partum BHBA concentrations in determining the incidence, with different thresholds used for detection in different studies.

Overall, these studies collectively emphasize the substantial impact of ketosis on dairy cattle, particularly during the postpartum and transition periods, with prevalence rates varying based on factors like lactation phase, age, parity, breed, and seasonal influences.

Impact of Ketosis on Health, Reproduction, Milk Production, and Economics in Dairy Cows

Ketosis, both in its clinical (CK) and subclinical (SCK) forms, imposes significant physiological, productive, and economic burdens on dairy cows and the dairy industry at large. A vast body of literature confirms that ketosis adversely affects the health, fertility, milk output, and milk quality in dairy animals, thereby impairing farm profitability (Duffield, 2005 [22,24,31,52,56,66] . Subclinical ketosis, in particular, is often underdiagnosed due to the absence of overt clinical symptoms, yet it has been consistently associated with a higher risk of subsequent periparturient diseases. These include displaced abomasum, retained placenta, metritis, endometritis, mastitis, lameness, cystic ovarian disease and impaired immune response (Hammon et al., 2006 [24,38,66,70,83] . The cascade of disease progression due to SCK frequently results in decreased reproductive efficiency, extended calving intervals, increased services per conception, and higher rates of early culling during early lactation (McArt et al., 2012) [55] . While clinical ketosis manifests with obvious signs, SCK silently deteriorates performance across multiple physiological parameters. Regarding milk production, several pivotal studies have quantified the production deficits linked to ketosis. Dohoo and Martin (1984) [19] reported a 4.4% to 6.6% decline in daily milk yield (approximately 1.0 to 1.4 kg/day) in ketotic cows compared to healthy animals. Similarly, Duffield et al. (2009) [24] noted milk losses of 1 to 2 kg per day in cows with ketosis. [55] provided additional granularity, showing that for every 0.1 mmol/L increase in βhydroxybutyrate BHBA beyond the SCK threshold of 1.2 mmol/L, cows experienced a further 0.5 kg/day milk reduction during the first 30 days in milk (DIM). These cumulative milk losses are especially detrimental in highyielding dairy herds, directly impacting total lactation yield and profitability. Ketosis also alters milk composition, which can have marketing and processing implications. Research by Kauppinen (1984) and Miettinen (1994) [47,59] demonstrated that cows with CK or SCK tend to have a higher milk fat percentage. This is believed to be a metabolic consequence of increased lipolysis and fat mobilization. However, the milk protein percentage was found to decrease in SCK-affected cows (Miettinen & Setala, 1993) [60] , indicative of compromised protein metabolism and imbalanced nutrient partitioning. These changes can negatively influence milk quality and its suitability for processing, thereby affecting the market price. The economic cost of ketosis is considerable. According to Duffield (2000) [23] , each case of SCK incurs a cost of approximately CAD $50 to $100, which translates to ₹3,050 to ₹6,100 per cow. Geishauser et al. (2001) [27] estimated a similar cost of CAD $78 per case (~₹4,750). Liang (2013) [53] projected that the combined cost of SCK and CK could range between USD $55.19 to $123.94, equivalent to ₹4,580 to ₹10,290 per case. These estimations incorporate losses due to reduced milk production, treatment costs, veterinary visits, labour, and impaired fertility. When considered at the herd level, the economic burden becomes even more significant due to the high prevalence of the condition. Studies have shown SCK affects between 26.4% and 55.7% of cows within a herd, while CK incidence ranges from 2% to 15% during the first month postpartum (Gordon et al., 2013 and Oetzel, 2013) [31,65] . In a 100-cow herd, for instance, 40-60 cows may be affected by ketosis, potentially leading to cumulative financial losses exceeding ₹3 to ₹6 lakhs per lactation cycle, depending on severity and management. Given the economic and physiological implications, it is imperative that ketosis be detected early and managed proactively. Comprehensive monitoring of BHBA levels, appropriate transition feeding strategies, and minimizing negative energy balance are essential to safeguard cow health and optimize dairy farm economics.

McArt et al. (2012)

Risk Factors of Ketosis in Dairy Cows

Ketosis, particularly in its subclinical form (SCK), is a pervasive metabolic disorder in dairy cattle, posing challenges to animal health, fertility, and milk production. Understanding the risk factors associated with this condition is essential for developing effective prevention and management strategies. Various physiological, nutritional, and environmental determinants have been identified across multiple studies (Gillund et [6,14,28,55,66,70,72,73,83,93] .

Breed Susceptibility

Breed is a significant determinant in the risk of developing ketosis. Studies indicate that Jersey cows have a higher prevalence (19%) compared to Holsteins (14%), with crossbred Jerseys also showing elevated ketone levels (Raboisson et al., 2014) [70] . Additionally, Swedish Red and White cows have been observed to produce higher levels of acetone and total ketone bodies in milk than Swedish Friesians (Raboisson et al., 2014) [70] . According to Dohoo and Martin [19] , Holsteins and Ayrshires were 3.6 and 2.8 times more likely, respectively, to develop ketosis compared to Guernsey cows (Raboisson et al., 2014) [70] .

Parity (Number of Lactations)

Multiparous cows (especially in third lactation or beyond) face increased metabolic demands, predisposing them to ketosis. Rathbun et al. (2017) [72] reported that the incidence of ketosis increased from 8.6% in first-lactation cows to 22.2% in cows with three or more lactations. However, Chandler et al. (2018) [14] observed that primiparous Jersey cows had the highest prevalence, suggesting that breedspecific physiology may influence parity effects.

Days in Milk (DIM)

Ketosis is most prevalent within the first two weeks postpartum, with a peak around day five of lactation and also documented that approximately 80% of SCK cases occur within this window McArt et al. (2012) [55] . Herd-level data from Canada and Germany also show a marked decline in SCK prevalence after 30 days in milk (Lievaart et al., 2005 and Duffield et al., 2009) [24] .

Prepartum NEFA Levels and Body Condition

Elevated prepartum non-esterified fatty acids (NEFA) and high body condition scores are predictive of increased ketosis risk. Excessive lipid mobilization due to negative energy balance (NEB) leads to hepatic lipidosis and impaired gluconeogenesis, predisposing cows to metabolic disorders (Ospina et al., 2010) [66] .

Nutritional and Feeding Practices

Transition cow nutrition plays a pivotal role in ketosis development. Inadequate or imbalanced energy intake during the close-up dry period can trigger ketosis (McArt et al., 2012 and Berge & Vertenten, 2014) [6,55] . Swedish studies found that partial mixed rations (PMR) were associated with higher SCK prevalence compared to total mixed rations (TMR) or separate forage-concentrate feeding (Lievaart et al., 2005). In pasture-based systems, such as those in Australia and New Zealand, individual feed intake is difficult to monitor, increasing the risk of undetected NEB (Roche et al., 2013 and Raboisson et al., 2014) [70,73] .

Season of Calving

Calving season affects ketosis risk, likely due to forage quality and climatic factors. In Sweden, spring-calving cows showed higher prevalence due to poor silage quality (Berge & Vertenten, 2014). Other studies have associated increased risk with autumn and winter, depending on regional management systems (Suthar et al., 2013; Rathbun et al., 2017) [6,72,83] .

Herd Size and Management

Larger herds generally exhibit lower ketosis prevalence, attributed to structured management, regular health monitoring, and group-based nutrition strategies (Duffield et al., 2009; Zbinden et al., 2017) [93] . Smaller herds may lack these resources, resulting in delayed detection and intervention (Lievaart et al., 2005).

Housing System

Housing type influences cow comfort, feed intake, and metabolic health. Cubicle housing systems are linked with lower SCK prevalence, while free-stall barns with straw floors and grazing systems are associated with higher risk, due to inconsistent monitoring and feeding (Duffield et al., 2009) [24] .

Colostrum Yield and Lactation History

High colostrum production and extended lactations prior to dry-off increase metabolic stress postpartum. Additionally, longer dry periods before calving are associated with higher ketosis incidence due to over-conditioning and increased lipomobilization postpartum (Gillund et al., 2001 and Roche et al., 2013) [28,73] .

Diagnosis of Ketosis

Accurate and timely diagnosis of ketosis is crucial for minimizing production losses and preventing secondary health complications in dairy cows. Over the years, diagnostic approaches have evolved from traditional clinical observations to advanced biochemical and metabolomic tools.

Blood β-Hydroxybutyrate (BHBA) Measurement

Quantifying β-hydroxybutyrate (BHBA) in blood is widely regarded as the most reliable diagnostic approach for ketosis. Among the three major ketone bodies-BHBA, acetoacetate, and acetone-BHBA demonstrates the greatest stability and persistence in circulation, making it the preferred biomarker (Duffield et al., 2009; Suthar et al., 2013) [24,83] . Typically, blood BHBA levels above ~1.2 mmol/L indicate subclinical ketosis, whereas values exceeding 3.0 mmol/L are often associated with clinical disease (Oetzel, 2004; McArt et al., 2012) [55,64] . Laboratory assays, especially enzymaticspectrophotometric methods, provide precise quantification with high diagnostic accuracy (Grünberg, 2014).

Milk BHBA Testing

Milk is an attractive diagnostic matrix due to its routine collection and non-invasive nature. Although BHBA levels in milk are lower compared to blood, a strong positive correlation between the two has been established, validating its use for herd-level screening (Enjalbert et al., 2001) [26] . Technologies such as Fourier Transform Infrared (FTIR) spectroscopy now permit high-throughput milk BHBA assessments, allowing integration of ketosis surveillance into regular milk quality testing (Dórea et al., 2018; Gruber & Mansfeld, 2019) [20,34] .

Blood Glucose Assessment

Although not specific for ketosis, blood glucose measurement remains a useful adjunct tool for assessing energy balance. Declining glucose concentrations in early lactation often signal a negative energy balance and may precede hyperketonemia (Zahra et al., 2006; Duffield, 2000) [23,92] . Veterinary-adapted portable glucometers allow on-farm testing, although validation is essential due to potential species-specific variations (Iwersen et al., 2009).

Urine Ketone Detection

Urine-based tests, such as Ketostix® or Acetest®, are widely employed in field conditions for their affordability and ease of use. These tests rely on a colorimetric nitroprusside reaction that detects acetoacetate, with the intensity of the color change reflecting ketone levels (González & Schelcher, 1999). While rapid and inexpensive, they are less specific and subject to user interpretation. Additionally, urine collection can be challenging, limiting their utility for large-scale screening (Duffield, 2000) [23] .

Laboratory Enzymatic Assays

Enzymatic colorimetric methods remain the gold standard for research and diagnostic laboratories. These assays, which detect BHBA in both blood and milk, are highly sensitive and reproducible, though less practical for routine field application (Enjalbert et al., 2001; Grünberg, 2014) [26] .

Advanced Diagnostic Technologies

FTIR spectroscopy has become a valuable tool for herd-level monitoring, simultaneously evaluating multiple milk components, including ketones, without destroying samples (Dórea et al., 2018) [20] . Fluorometric assays, such as those described by Larsen and Nielsen (2005), measure BHBA via enzymatic oxidation linked to fluorescence generation, enabling high-throughput application. Gas-liquid chromatography (GLC), nuclear magnetic resonance (NMR), and gas chromatography-mass spectrometry (GC-MS) are also employed in research settings for precise quantification of ketone bodies and related metabolites (Kristensen et al., 2007; Dettmer et al., 2007). Though highly accurate, these techniques are cost-intensive and less feasible for routine farm use.

Cow-Side Rapid Tests

Handheld blood ketone meters, such as Precision Xtra® (Abbott), provide immediate and highly accurate BHBA results under field conditions. When compared with laboratory assays, these meters have demonstrated excellent sensitivity and specificity at clinically relevant cutoffs (Iwersen et al., 2009; Grunberg, 2014). Milk-based strips such as KetoTest™ also allow non-invasive BHBA testing but are comparatively more expensive (Suthar et al., 2013) [83] .

Alternative Monitoring Parameters

The milk fat-to-protein (F:P) ratio has been proposed as an indirect marker of negative energy balance, with values exceeding 1.35-1.50 suggesting increased risk of ketosis (Richardt, 2004; Jenkins et al., 2015) [43] . Similarly, milk urea nitrogen levels may reflect imbalances between dietary protein and energy intake (Jonker et al., 2002). Milk fatty acid profiling offers further insight, as increased long-chain fatty acids, particularly oleic acid, are indicative of lipid mobilization associated with ketosis (Van Haelst et al., 2008). Advanced FTIR systems now permit large-scale fatty acid monitoring during routine milk analysis (Gruber & Mansfeld, 2019) [34] .

Emerging and Complementary Approaches

Non-esterified fatty acid (NEFA) measurement is increasingly recognized as a valuable predictor of ketosis risk, especially when measured prepartum (Ospina et al., 2010; LeBlanc, 2010) [66] . However, its application is limited by the need for laboratory processing and higher costs. Exhaled breath analysis represents another promising noninvasive approach, with acetone levels correlating strongly with BHBA concentrations (Dobbelaar et al., 1996; Kuntzel et al., 2012). Similarly, metabolomic profiling using NMR or LC/GC-MS has revealed unique biochemical signatures predictive of ketosis, offering deeper insights into its multifactorial pathogenesis (Zhang et al., 2013; Tran et al., 2020). Although currently limited by cost and technical complexity, metabolomics holds great potential for precision dairy herd health management.

Cowside Testing Methods

Urine Ketone Tests

Cow-side urine testing for acetoacetate utilizes nitroprussidebased reagents. Acetest® and Ketostix® (Bayer Corp.) are commonly used, offering high sensitivity but variable specificity (Oetzel, 2004) [64] . Limitations include difficulty in urine collection-only 50% of cows can be induced to urinate-and cost per test (₹17), which restricts their use in large herds (González and Schelcher, 1999; Duffield, 2000) [23] . Furthermore, prolonged sample-reagent contact may yield false positives.

Milk Ketone Tests

Milk testing is more practical on-farm due to ease of sample collection. Nitroprusside powders such as Utrecht powder and KetoCheck™ detect milk acetoacetate, though with poor sensitivity and limited diagnostic value (Oetzel, 2004) [64] . In contrast, BHBA-specific test strips like KetoTest™ offer better accuracy, enabling both individual cow diagnosis and herd-level screening. These tests are more expensive (₹142 per test) but are preferred for non-invasive monitoring (Suthar et al., 2013) [83] .

Blood Ketone Meters

Handheld ketone meters like Precision Xtra® (Abbott) are increasingly used for rapid, accurate detection of BHBA. Validated against laboratory gold standards, they provide 100% sensitivity and specificity at the 1.4 mmol/L BHBA cutoff (Iwersen et al., 2009). Despite requiring venipuncture, these meters are portable, do not require calibration, and yield immediate results. Concerns remain regarding animal handling and the need for trained personnel (Grunberg, 2014 and Gruber and Mansfeld, 2019) [34] . 7.10 Alternative Monitoring Parameters 7.10.1. Fat to Protein Ratio (F:P) in Milk Milk fat and protein levels change with energy status during lactation. The fat-to-protein (F:P) ratio increases during negative energy balance due to elevated lipolysis (Gross et al., 2011) [33] . Ratios above 1.35-1.50 are associated with increased ketosis risk (Richardt, 2004 and Jenkins et al., 2015) [43] . However, dietary factors and sensor calibration issues can affect accuracy, limiting its sensitivity and specificity (Kamphuis et al., 2013 and King et al., 2018) [46] .

Urea Content in Milk

Milk urea levels reflect dietary protein and energy intake. Low urea and protein concentrations in early lactation indicate insufficient nutrient supply and impaired rumen function (Gruber and Mansfeld, 2019) [34] . High urea with low protein suggests protein excess with energy deficit (Jonker et al., 2002 and Burgos et al., 2007).

Fatty Acid Profiles in Milk

Milk fatty acids originate from diet, de novo synthesis, and lipolysis. NEB leads to higher long-chain FA (notably oleic acid) and reduced short-chain FA synthesis (Palmquist et al., 1993) [68] . Elevated oleic acid levels may indicate subclinical ketosis (Van Haelst et al., 2008). FTIR technology facilitates large-scale FA profiling during milk testing (Gruber and Mansfeld, 2019) [34] .

Emerging and Complementary Methods

Non-Esterified Fatty Acids (NEFA)

NEFA concentrations increase with adipose mobilization in early lactation. Elevated prepartum NEFA levels predict ketosis risk (Ospina et al., 2010; LeBlanc, 2010) [66] . NEFA is more stable than BHBA throughout the day, but laboratory processing is costly (₹920/sample). Misreadings may occur due to stress or sample mishandling (Duffield, 2000) [23] .

Exhaled Breath Analysis

Acetone in exhaled breath correlates with blood BHBA (r = 0.81) and milk ketones (Dobbelaar et al., 1996). Breath sampling devices have been developed for cattle, enabling non-invasive detection of volatile organic compounds associated with ketosis (Kuntzel et al., 2012).

Metabolomics

Metabolomics is a cutting-edge field of systems biology that focuses on the large-scale analysis of low-molecular-weight metabolites in biological fluids such as blood, milk, urine, and rumen fluid, offering a detailed snapshot of the metabolic state of the animal (Tran et al., 2020). In the context of ketosis in dairy cows, metabolomics provides powerful tools for investigating the complex biochemical alterations induced by negative energy balance (NEB) during early lactation. Traditional markers such as β-hydroxybutyrate and non-esterified fatty acids (NEFA) offer valuable but limited information, reflecting only specific metabolic shifts like lipolysis and ketogenesis. In contrast, metabolomics enables a holistic view of disrupted pathways, including glycolysis, gluconeogenesis, fatty acid metabolism, and amino acid turnover (Gross et al., 2011 and Zhang et al., 2013) [33] . Advanced analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), often coupled with gas chromatography (GC-MS) or liquid chromatography (LC-MS), have been employed to generate comprehensive metabolic profiles of ketotic and healthy cows (Tran et al., 2020). These methods are highly accurate and reproducible, although MS offers greater sensitivity and the ability to detect low-abundance metabolites compared to NMR (Van Haelst et al., 2008). Metabolomic studies have identified significant changes in metabolites associated with fatty acid mobilization (e.g., increased oleic acid and acylcarnitines), impaired gluconeogenesis (e.g., decreased glucogenic amino acids such as alanine and valine), and elevated markers of oxidative stress and systemic inflammation (Gross et al., 2011 and Zhang et al., 2013) [33] . Notably, certain metabolites like oleic acid have been found to rise in milk before clinical onset of ketosis, making them valuable early biomarkers (Van Haelst et al., 2008). Statistical tools such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) have enabled the differentiation of ketotic from non-ketotic cows with high predictive accuracy, sometimes exceeding 90% (Zhang et al., 2013; Tran et al., 2020). Although metabolomics is currently limited by high costs, technical complexity, and the need for specialized laboratories, its potential for early diagnosis, improved understanding of pathophysiology, and precision herd management is immense. As on-farm technologies evolve, metabolomics is expected to become a valuable component of dairy cow health monitoring strategies, especially in identifying subclinical ketosis before it impacts productivity and welfare.

Effects of Hyperketonemia on Production and Health

Hyperketonemia (HYK), defined as elevated levels of βhydroxybutyrate in blood (≥1.2 mmol/L), exerts significant detrimental effects on both productivity and overall health in early lactation dairy cows (Oetzel, 2020).

Impact on Milk Production

Multiple studies have reported that cows experiencing HYK show a 3-7% reduction in milk yield compared to normoketonemic counterparts (Ospina et al., 2010; McArt et al., 2011) [56,66] . The severity of milk loss is directly proportional to BHBA concentration; for every 0.1 mmol/L increase in BHBA above the 1.2 mmol/L threshold, cows produced approximately 0.5 kg (1.1 lb) less milk (McArt et al., 2011) [56] . Moreover, cows developing HYK within 3-7 days postpartum suffered greater production losses than those affected later (8-16 DIM).

Association with Displaced Abomasum (DA)

Hyperketonemic cows are at a significantly increased risk of developing DA, particularly those with early or severe cases. For example, cows with BHBA levels of 2.4 mmol/L had a 3.1 times greater risk of DA than cows at 1.2 mmol/L (McArt et al., 2012) [55] . Additionally, cows diagnosed with subclinical ketosis (SCK) between 3-5 DIM had a 6.1-fold increased risk for DA compared to those identified later in lactation (McArt et al., 2012) [56] .

Risk of Metritis

Since metritis and HYK often occur concurrently in the early postpartum window, they are frequently interlinked. Studies suggest that HYK increases the odds of metritis by 3.4 times, although causality remains under debate (Duffield et al., 2009) [24] .

Herd Removal and Mortality

Cows diagnosed with SCK in early lactation are at higher risk for early culling or death. Specifically, each 0.1 mmol/L increase in BHBA raised the risk of removal by 1.4 times, and cows with BHBA levels of 2.4 mmol/L had a 57-fold higher risk of removal compared to those at 1.2 mmol/L (McArt et al., 2012) [55] . Early detection and intervention are thus critical to herd retention.

Reproductive Performance

The relationship between HYK and reproduction is inconsistent across studies. Some have reported reduced conception rates and delayed first service in ketotic cows (Ospina et al., 2010; Rutherford et al., 2016) [66,76] , while others found no significant reproductive effect (Chapinal et al., 2012) [15] . Synchronization protocols may mitigate some negative impacts on fertility, leading to variability in outcomes.

Treatment and Prevention

Preventing and treating ketosis, particularly subclinical ketosis (SCK), in dairy herds is critical due to its negative impact on animal health, reproductive efficiency, milk production, and overall farm economics. Ketosis has been associated with increased susceptibility to displaced abomasum (DA)-one of the most expensive diseases in dairy cattle, averaging approximately US$650 (₹54,275) per case (Liang et al., 2017) [53] . As such, effective therapeutic strategies and proactive prevention protocols are essential components of herd health management. Traditionally, parenteral administration of dextrose has been a cornerstone in treating ketosis. Venkateshwarlu and Choudhuri (2000) [91] reported that intravenous administration of 540 ml of 25% dextrose over two days was superior in reducing recovery time and restoring milk yield compared to other combinations involving sodium propionate, methionine, nicotinic acid, propylene glycol, and jaggery. Similarly, Baishya et al. (2002) [4] observed that a regimen involving 10% fructose (500 ml I/V.) with Vetalog (6 mg I/M.), followed by dextrose and dexamethasone, yielded faster recovery in Jersey cows with SCK. Despite its widespread use, propylene glycol (PG) has emerged as the most consistent and effective treatment for both clinical and subclinical ketosis. [11,56] collectively demonstrate that PG alone is sufficient to resolve ketosis without requiring additional interventions like intravenous dextrose, which may carry risks such as jugular thrombophlebitis and increase labor demands. Recent trials have evaluated adjunct therapies to PG, including dexamethasone (Gordon et al., 2013), insulin (Tatone, Gordon et al., 2016) [31,32,84] , and vitamin B12 (Tatone, Duffield et al., 2016) [84] , but these combinations have not consistently improved postpartum health or fertility outcomes beyond those achieved with PG alone (Jeong et al., 2018) [44] . However, innovative combinations, such as the one evaluated by Chirivi et al. (2023), show promising results. In their study, cows treated with PG + niacin + flunixin meglumine showed greater improvements in metabolic status, higher resolution of hyperketonemia, reduced concentrations of NEFA and acute-phase proteins, and higher blood glucose and insulin levels. These findings suggest that a multitargeted approach, addressing both ketogenesis and inflammatory lipolysis, may enhance treatment outcomes beyond standard therapy. Similarly, Tufani et al. (2011) [88] demonstrated that administering 25% glucose (1 L i.v., followed by 500 ml for 2 days), combined with B-complex vitamins and dexamethasone, achieved rapid recovery (mean: 1.7 ± 0.26 days). Gupta (2012) [36] further confirmed the superiority of PG over niacin-tannic acid-jaggery combinations in lactating buffaloes. Additional supportive therapies include yeast cultures such as Saccharomyces cerevisiae, which improve serum glucose, enhance milk yield, and reduce the incidence of periparturient disorders (Stanislaw & Przemyslaw, 2009; Bakr et al., 2015) [5,82] . Prevention of ketosis, especially SCK, relies on comprehensive herd-level strategies involving dietary optimization, metabolic monitoring, and management of body condition score (BCS). A strategically balanced feeding program, ensuring sufficient energy, protein, and micronutrient intake, is essential during the transition period. Both over-conditioning (high BCS) and excessive BCS loss prepartum increase the risk of postpartum ketosis (Melendez & Risco, 2021) [67] . Inclusion of gluconeogenic precursors like glycerol, calcium propionate, and propylene glycol in prepartum and early lactation diets has been shown to stimulate hepatic gluconeogenesis, increase circulating insulin, and reduce hepatic triglyceride accumulation and NEFA mobilization. These additives should be top-dressed on the total mixed ration to ensure equitable intake (Ospina et al., 2013 and Melendez & Risco, 2021) [67] . Among nutritional interventions, monensin stands out as a preventive additive. As a ruminal ionophore, it shifts volatile fatty acid production toward propionate, a major gluconeogenic precursor. When administered as a slowrelease bolus or powder, monensin has been shown to lower the incidence of ketosis, improve reproductive performance, and increase milk yield (Melendez et al., 2007 and Duffield et al., 2012) [57] . It is especially effective when used in cows with low BCS during the dry period, helping them calve with better metabolic reserves. Furthermore, the routine use of PG as a preventive supplement should be tailored based on the herd's history of ketosis incidence.

Conclusion

Ketosis, particularly subclinical ketosis (SCK), remains one of the most economically significant and biologically complex metabolic disorders affecting high-yielding dairy cows during the transition period. It is characterized by a negative energy balance (NEB) that drives excessive mobilization of body fat, leading to elevated concentrations of non-esterified fatty acids (NEFA) and β-hydroxybutyrate. These alterations not only impair liver function and reduce milk yield, but also predispose cows to secondary disorders like displaced abomasum, metritis, mastitis, immunosuppression, and reproductive inefficiency. Despite being often clinically silent, the subtle yet pervasive impact of SCK has prompted the evolution of diverse diagnostic, monitoring, and management strategies. The diagnosis of ketosis has moved beyond traditional laboratory methods, with advancements enabling both highprecision laboratory assays and rapid cowside tests. Laboratory techniques such as enzyme catalysis, Fourier-transform infrared (FTIR) spectrometry, fluorometry, gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) offer accurate quantification of ketone bodies, though are less feasible for routine field use. On-farm methods-such as dipsticks and portable blood ketone meters-provide cost-effective, real-time, and reliable monitoring tools. Additionally, emerging diagnostic approaches based on milk components like the fat-to-protein (F:P) ratio, milk urea nitrogen, fatty acid profiling, and predicted NEFA values, alongside breath analysis and metabolomics, hold promising potential for early detection and metabolic profiling. Treatment strategies for ketosis are multifaceted, with propylene glycol firmly established as the cornerstone therapeutic due to its gluconeogenic efficiency, ease of administration, and consistent efficacy in resolving hyperketonemia and restoring energy balance. Adjunctive therapies, including intravenous dextrose, insulin, corticosteroids, B-complex vitamins, and feed supplements like Saccharomyces cerevisiae, have shown variable results, with recent studies supporting combinations such as propylene glycol, niacin, flunixin meglumine for enhanced metabolic and anti-inflammatory outcomes. Nevertheless, treatment success ultimately depends on timely intervention, accurate diagnosis, and appropriate case selection. Importantly, prevention remains the most sustainable strategy in mitigating the widespread impact of ketosis. A proactive herd-level approach focusing on nutritional balance, maintenance of optimal BCS, cow comfort, and strategic supplementation during the dry and transition periods is essential. Preventive use of gluconeogenic precursors such as glycerol, calcium propionate, and propylene glycol, alongside monensin as a ruminal modulator, has shown robust efficacy in minimizing NEB, reducing the risk of ketosis, and improving reproductive and lactational performance. Modern tools like metabolomics and milk-based sensors further enable precision herd management and individualized nutritional interventions.

In conclusion, the effective control of ketosis lies in a balanced triad of early diagnosis, evidence-based treatment, and comprehensive prevention. With continued advancements in diagnostic technologies, a deeper understanding of metabolic pathways, and integration of precision nutrition and herd health practices, the dairy industry is well-positioned to mitigate the burden of ketosis. Through informed veterinary guidance, data-driven management, and strategic interventions, the goal of healthier cows, improved productivity, and enhanced farm profitability can be confidently achieved.

References

1. Akamatsu H, Saitoh Y, Serizawa M, Miyake K, Ohba Y, Nakashima K. Changes of serum 3-methylhistidine concentration and energy-associated metabolites in dairy cows with ketosis. J Vet Med Sci. 2007;69(10):10911093.
2. Arya N. Studies on haemato-biochemical and mineral status of ketotic cows [MVSc thesis]. Bikaner: Rajasthan Agricultural University; 2008.
3. Asl AN, Nazifi S, Ghasrodashti AR, Olyaee A. Prevalence of subclinical ketosis in dairy cattle in Southwestern Iran and detection of cutoff point for NEFA and glucose concentrations for diagnosis of subclinical ketosis. Prev Vet Med. 2011;100:38-43.
4. Baishya B, Nath R, Sarmah R. Therapeutic management of subclinical ketosis in crossbred cows. Indian J Anim Res. 2002;36(1):59-60.
5. Bakr AA, El-Wakeel AS, Saleh AA. Effect of yeast (Saccharomyces cerevisiae) supplementation on health status, milk production, and metabolic disorders in dairy cows during transition and early lactation. Int J Dairy Sci. 2015;10(5):230-240.
6. Berge AC, Vertenten G. A field study to determine the prevalence, dairy herd management systems, and fresh cow clinical conditions associated with ketosis in western European dairy herds. J Dairy Sci. 2014;97:2145-2154.
7. Bihani DK. Clinico-biochemical studies on ketosis in cattle [PhD thesis]. Bikaner: Rajasthan Agricultural University; 2002.
8. Bobe G, Young JW, Beitz DC. Pathology, etiology, prevention, and treatment of fatty liver in dairy cows. J Dairy Sci. 2004;87(10):3105-3124.
9. Borchardt S, Staufenbiel R, Habil. Evaluation of the use of nonesterified fatty acids and β-hydroxybutyrate concentrations in pooled serum samples for herd-based detection of subclinical ketosis in dairy cows during the first week after parturition. J Am Vet Med Assoc. 2012;240(8):1003-1011.
10. Bowman GJ, Richards, Vazquez-Anion M. Improving the antioxidant status of the dairy cow through nutrition. In: Proceedings of Intermountain Nutrition Conference; 2008 Jan 29-30; Salt Lake City, Utah, USA.
11. Capel MB, Bach KD, Mann S, McArt JAA. A randomized controlled trial to evaluate propylene glycol alone or in combination with dextrose as a treatment for hyperketonemia in dairy cows. J Dairy Sci. 2021;104(2):2185-2194.
12. Castillo CJ, Hernandez A, Bravo M, Lopez-Alonso V, Pereira, Benedito JL. Oxidative status during late pregnancy and early lactation in dairy cows. Vet J. 2005;169:286-292.
13. Chakrabarti A. Metabolic diseases. In: Textbook of Clinical Veterinary Medicine. 3rd ed. Ludhiana: Kalyani Publishers; 2006. p. 564.
14. Chandler TL, Pralle RS, Dórea JRR, Poock SE, Oetzel GR, Fourdraine RH, White HM. Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows. J Dairy Sci. 2018;101:247691.
15. Chapinal N, Carson ME, LeBlanc SJ, Leslie KE, Godden S, Capel M, Santos JEP, Overton MW, Duffield TF. The association of serum metabolites in the transition period with milk production and early lactation reproductive performance. J Dairy Sci. 2012;95:1301-1309.
16. Chapinal N, Leblanc SJ, Carson ME, Leslie KE, Godden S, Capel M, Santos JEP, Overton TR, Duffield TF. Herd level association of serum metabolites in the transition period with disease, milk production, and early lactation reproductive performance. J Dairy Sci. 2012;95(10):5676-5682.
17. Dimri U, Ranjan R, Sharma MC, Varshney VP. Effect of vitamin E and selenium supplementation on oxidative stress indices and cortisol level in blood in water buffaloes during pregnancy and early postpartum period. Trop Anim Health Prod. 2010;42:405-410.
18. Djokovic R, Samanc H, Jovanovic M, BoskovicBogosavljevic S. Changes in blood values of glucose, insulin and inorganic phosphorus in healthy and ketotic cows after intravenous infusion of propionate solution. Acta Vet Brno. 2007;76:533-539.
19. Dohoo IR, Martin SW. Subclinical ketosis: Prevalence and associations with production and disease. Can J Comp Med. 1984;48:1-5.
20. Dórea JRR, Rosa GJ, Weld KA, Armentano LE. Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows. J Dairy Sci. 2018;101(7):5878-5889.
21. Drackley JK, Overton TR, Douglas GN. Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period. J Dairy Sci. 2001;84:E100-E112.
22. Duffield TF. Minimizing subclinical metabolic diseases in dairy cows. WCDS Adv Dairy Technol. 2005;18:4355.
23. Duffield TF. Subclinical ketosis in lactating dairy cattle. Vet Clin North Am Food Anim Pract. 2000;16:231-53.
24. Duffield TF, Lissemore KD, McBride BW, Leslie KE. Impact of hyperketonemia in early lactation dairy cows on health and production. J Dairy Sci. 2009;92:571-580.
25. Duffield TF, Lissemore KD, McBride BW, Leslie KE. Impact of hyperketonemia in early lactation dairy cows on health and production. J Dairy Sci. 2009;92:571-580.
26. Enjalbert F, Nicot MC, Bayourthe C, Moncoulon R. Ketone bodies in milk and blood of dairy cows: relationship between concentrations and utilization for detection of subclinical ketosis. J Dairy Sci. 2001;84:5839. doi:10.3168/jds.S0022-0302(01)74511-0.
27. Geishauser T, Leslie K, Duffield T, Sandals D, Edge V. The association between selected metabolic parameters and left abomasal displacement in dairy cows. J Vet Med A Physiol Pathol Clin Med. 1998;45:499-511.
28. Gillund P, Reksen O, Gröhn YT, Karlberg K. Body condition related to ketosis and reproductive performance in Norwegian dairy cows. J Dairy Sci. 2001;84:1390-6.
29. Gitto E, Reiter RJ, Karbownik M, Tan DX, Gitto P, Barberi S, et al. Causes of oxidative stress in the pre-and perinatal period. Biol Neonate. 2002;81:146-157.
30. Goff JP, Horst RL. Physiological changes at parturition and their relationship to metabolic disorders. J Dairy Sci. 1997;80:1260-1268.
31. Gordon JL, Duffield TF, Kelton DF. Evaluation of the effectiveness of an intraruminal monensin controlledrelease capsule on milk production and metabolic parameters in transition dairy cows. J Dairy Sci. 2013;96(2):921-934.
32. Gordon JL. Risk factors for and treatment of ketosis in lactating dairy cows [PhD thesis]. Guelph (ON): University of Guelph; 2013.
33. Gross J, van Dorland HA, Bruckmaier RM, Schwarz FJ. Performance and metabolic profile of dairy cows during a lactational and deliberately induced negative energy balance with subsequent realimentation. J Dairy Sci. 2011;94(4):1820-30. doi:10.3168/jds.2010-3707.
34. Gruber S, Mansfeld R. Herd health monitoring in dairy farms-discover metabolic diseases. An overview. Tierarztl Prax Ausg G Grosstiere Nutztiere. 2019;47(4):246-255.
35. Grummer RR. Nutritional and management strategies for the prevention of fatty liver in dairy cattle. Vet J. 2008;176:10-20.
36. Gupta AK. Comparative efficacy of different therapeutic regimens in treatment of subclinical ketosis in buffaloes. Indian J Vet Med. 2012;32(2):124-126.
37. Gupta GC, Rai P. A note on biochemical profile in pre and post parturition status in cattle and buffaloes. Indian J Vet Med. 1987;7:45-46.
38. Hammon DS, Evjen IM, Dhiman TR, Goff JP, Walters JL. Neutrophil function and energy status in Holstein cows with uterine health disorders. Vet Immunol Immunopathol. 2006;113:21-29.
39. Hayirli A. The role of exogenous insulin in the complex of hepatic lipidosis and ketosis associated with insulin resistance phenomenon in postpartum dairy cattle. Vet Res Commun. 2006;30:749-774.
40. Herdt TH. Ruminant adaptation to negative energy balance-Influences on the etiology of ketosis and fatty liver. Vet Clin North Am Food Anim Pract. 2000;16:215230.
41. Houe H, Ostergaard S, Thilsing-Hansen T, Jorgensen RJ, Larsen T, Sorensen JT, et al. Milk fever and subclinical hypocalcaemia -an evaluation of parameters on incidence risk, diagnosis, risk factors and biological effects as input for a decision support system for disease control. Acta Vet Scand. 2001;42:1-29.
42. Ingvartsen KL, Dewhurst RJ, Friggens NC. On the relationship between lactational performance and health: is it yield or metabolic imbalances that cause production diseases in dairy cattle? Livest Prod Sci. 2003;83(23):277-308.
43. Jenkins NT, Peña G, Risco C, Barbosa CC, Vieira-Neto A, Galvão KN. Utility of inline milk fat and protein ratio to diagnose subclinical ketosis and to assign propylene glycol treatment in lactating dairy cows. Can Vet J. 2015;56:850-854.
44. Jeong JK, Choi IS, Kang HG, et al. Effects of different treatments for ketosis on milk yield and reproductive performance in dairy cows. J Dairy Sci. 2018;101(1):340347.
45. Djokovic R, Ilic Z, Kurcubic V, Petrovic M, Doskovic V. Functional and morphological state of the liver in Simmental dairy cows during transitional period. Rev Med Vet (Toulouse). 2011;162:574-579.
46. Kamphuis C, Dela Rue B, Eastwood C. Field evaluation of online sensors for detecting subclinical ketosis in dairy cows. J Dairy Sci. 2013;96(10):6223-6235.
47. Kauppinen K. Annual milk yield and reproductive performance of ketotic and non-ketotic dairy cows. Zentralbl Veterinarmed A. 1984;31:694-704.
48. Knowlton KF, Dawson TE, Glenn BP, Huntington GB, Erdman RA. Glucose metabolism and milk yield of cows infused abomasally or ruminally with starch. J Dairy Sci. 1998;81:3248-3258.
49. Küntzel H, et al. Field analysis of exhaled breath for detecting ketone bodies in cattle. Vet Res Commun. 2014;38(6):445-454.
50. Odens LJ, Burgos R, Innocenti M, VanBaale MJ, Baumgard LH. Effects of varying doses of supplemental conjugated linoleic acid on production and energetic variables during the transition period. J Dairy Sci. 2007;90(1):293-305
51. LeBlanc SJ. Monitoring metabolic health of dairy cattle in the transition period. J Reprod Dev. 2010;56:529-35.
52. LeBlanc SJ, Leslie K, Duffield TF. Metabolic predictors of displaced abomasum in dairy cattle. J Dairy Sci. 2005;88:159-170.
53. Liang D. Estimating the economic losses from diseases and extended days open with a farm-level stochastic model [master's thesis]. Lexington (KY): University of Kentucky; 2013.
54. Lyle RR. Glucose kinetics and hepatic gluconeogenesis in ketotic and fasted steers [doctoral dissertation]. Ames (IA): Iowa State University; 1983.
55. McArt JA, Nydam DV, Oetzel GR. Epidemiology of subclinical ketosis in early lactation dairy cattle. J Dairy Sci. 2012;95:5056-5066.
56. McArt JA, Nydam DV, Ospina PA, Oetzel GR. A field trial on the effect of propylene glycol on milk yield and resolution of ketosis in fresh cows diagnosed with subclinical ketosis. J Dairy Sci. 2011;94:6011-6020.
57. Melendez P, Goff JP, Risco CA, Archbald LF, Littell R, Donovan GA. Pre-partum monensin supplementation improves body reserves at calving and milk yield in Holstein cows dried-off with low body condition score. Res Vet Sci. 2007;82:349-357.
58. Melendez P, Goff JP, Risco CA, Archbald LF, Littell R, Donovan GA. Incidence of subclinical ketosis in cows supplemented with a monensin controlled-release capsule in Holstein cattle, Florida, USA. Prev Vet Med. 2006;73(1):33-42.
59. Miettinen PVA. Relationship between milk acetone and milk yield in individual cows. J Vet Med A. 1994;41:102-109.
60. Miettinen PVA, Setälä JJ. Relationships between subclinical ketosis, milk production and fertility in Finnish dairy cattle. Prev Vet Med. 1993;17:1-8.
61. Mir AQ, Malik HU. Oral glucose therapy -a new approach in the treatment of bovine ketosis. Indian J Vet Med. 2003;23:16-18.
62. Newman JC, Verdin E. Ketone bodies as signaling metabolites. Trends Endocrinol Metab. 2014;25:42-52.
63. Oetzel GR. Herd-level ketosis -diagnosis and risk factors. In: Proceedings of the 40th Annual Conference of the American Association of Bovine Practitioners; 2007 Jan 3-6;
64. Oetzel GR. Monitoring and testing dairy herds for metabolic disease. Vet Clin North Am Food Anim Pract. 2004;20:651-674.
65. Oetzel GR. Understanding the impact of subclinical ketosis. In: Proceedings of the 24th Florida Ruminant Nutrition Symposium; 2013 Feb 3-5; Gainesville, FL, USA.
66. Ospina PA, Nydam DV, Stokol T, Overton TR. Evaluation of nonesterified fatty acids and βhydroxybutyrate in transition dairy cattle in the northeastern United States: Critical thresholds for prediction of clinical diseases. J Dairy Sci. 2010;93:546554.
67. Melendez P, Risco CA. Reproduction, events and management pregnancy: Periparturient disorders. In: Fuquay JW, editor. Encyclopedia of Dairy Sciences. 3rd ed. Vol. 1. Cambridge (MA): Academic Press; 2021. p. 1031-7.
68. Palmquist DL, Beaulieu AD, Barbano DM. Feed and animal factors influencing milk composition. J Dairy Sci. 2006;79(6):1032-1043.
69. Pourjafar M, Heidari M. A study on subclinical ketosis in Holstein cattle of Torbat-Heydarieh (Khorasan Province). Acta Vet Scand Suppl. 2003;98.
70. Raboisson D, Mounié M, Maigné E. Diseases, reproductive performance, and changes in milk production associated with subclinical ketosis in dairy cows: A meta-analysis and review. J Dairy Sci. 2014;97:7547-63.
71. Raboisson D, Mounié M, Maigné E. Diseases, reproductive performance, and changes in milk production associated with subclinical ketosis in dairy cows: A meta-analysis and review. J Dairy Sci. 2014;97(12):7547-7563.
72. Rathbun FM, Pralle RS, Bertics SJ, Armentano LE, Cho K, Do C, et al. Relationships between body condition score change, prior mid-lactation phenotypic residual feed intake, and hyperketonemia onset in transition dairy cows. J Dairy Sci. 2017;100(5):3685-3696.
73. Roche JR, Bell AW, Overton TR, Loor JJ. Nutritional management of the transition cow in the 21st century: A paradigm shift in thinking. Anim Prod Sci. 2013;53(9):1000-1013.
74. Roche JR, Kay JK, Friggens NC, Loor JJ, Berry DP. Assessing and managing body condition score for the prevention of metabolic disease in dairy cows. Vet Clin North Am Food Anim Pract. 2013;29(2):323-336.
75. Rudzik L. Infrarotfibel: Infrarotspektroskopie für die Lebensmittel-und Milchindustrie. Hamburg: Behr's; 1993. p. 19-48.
76. Rutherford AJ, Oikonomou G, Smith RF. The effect of subclinical ketosis on activity at estrus and reproductive performance in dairy cattle. J Dairy Sci. 2016;99(6):4808-4815.
77. Sakha M, Ameri M, Sharifi H, Taheri I. Bovine subclinical ketosis in dairy herds in Iran. Vet Res Commun. 2007;31(6):673-679.
78. Sharma N, Singh NK, Singh OP, Pandey V, Verma PK. Oxidative stress and antioxidant status during transition period in dairy cows. Asian-Australas J Anim Sci. 2011;24(4):479-484.
79. Smith BI, Risco CA. Subclinical ketosis in dairy cattle. Vet Clin North Am Food Anim Pract. 2005;21(2):503521.
80. Sordillo LM, O'Boyle N, Gandy JC, Corl CM, Hamilton E. Shifts in thioredoxin reductase activity and oxidant status in dairy cattle with mastitis. J Dairy Sci. 2007;90(3):1186-1192.
81. Stanislaw L, Przemyslaw M. Effect of yeast supplementation on metabolic profile in dairy cows. Pol J Vet Sci. 2009;12(4):489-493.
82. Suthar VS, Canelas-Raposo J, Deniz A, Heuwieser W. Prevalence of subclinical ketosis and relationships with postpartum diseases in European dairy cows. J Dairy Sci. 2013;96(5):2925-2938.
83. Tatone EH, Gordon JL, Hubbs J, LeBlanc SJ, DeVries TJ, Duffield TF. A systematic review and meta-analysis of the diagnostic accuracy of point-of-care tests for the detection of hyperketonemia in dairy cows. Prev Vet Med. 2016;130:18-32.
84. Tehrani-Sharif M, Hadadi M, Noughabi HH, Mohammadi A, Rostami F, Sharifi H. Bovine subclinical ketosis in dairy herds in Nishaboor, Iran. Comp Clin Pathol. 2012;21(6):1637-1641.
85. Thirunavukkarasu M, Kathiravan G, Kalaikannan A, Jebarani W. Prevalence of ketosis in dairy farms: A survey in Tamil Nadu. Tamilnadu J Vet Anim Sci. 2010;6(4):193-195.
86. Tran H, Sreevatsan S, McConnel C, et al. Application of metabolomics to diagnosis and prevention of bovine ketosis. Animals (Basel). 2021;11(2):456.
87. Tufani NA, Makhdoomi DM, Khan HM, Khan AA, Dar SH, Mir MS. Comparative therapeutic evaluation of different regimens in clinical ketosis in cows. Indian J Vet Med. 2011;31(2):93-95.
88. Van der Drift SGA, Jorritsma R, Schonewille JT, Knijn HM, Stegeman JA. Routine detection of hyperketonemia in dairy cows using Fourier transform infrared spectroscopy analysis of β-hydroxybutyrate and acetone in milk in combination with test-day information. J Dairy Sci. 2012;95(9):4886-4898.
89. Van Haelst YNT, Beeckman A, Van Knegsel ATM, Fievez V. Elevated concentrations of oleic acid and longchain fatty acids in milk fat are associated with increased risk of ketosis. J Dairy Sci. 2008;91(11):4686-4690.
90. Venkateshwarlu G, Choudhuri PC. Studies on therapeutic management of clinical ketosis in crossbred cows. Indian J Vet Med. 2000;20(1):20-22.
91. Zahra LC, Duffield TF, Leslie KE, Overton TR, Putnam D, LeBlanc SJ. Effects of rumen-protected choline and monensin on milk production and metabolism of periparturient dairy cows. J Dairy Sci. 2006;89(12):48084818.
92. Zbinden RS, Falk M, Münger A, Dohme-Meier F, van Dorland HA, Bruckmaier RM, et al. Metabolic load in dairy cows kept in herbage-based feeding systems and suitability of potential markers for compromised wellbeing. J Anim Physiol Anim Nutr (Berl). 2017;101(4):767-778.
93. Zhang Z, Liu G, Wang H, Li X, Wang Z. Detection of subclinical ketosis in dairy cows. Pak Vet J. 2012;32(2):156-160.