Blood-based biomarkers of protein digestibility, utilization, and nitrogen excretion in animals: Concepts, evidence, and applications

Abstract

Improving dietary protein utilization is a central goal in animal nutrition because protein is costly, closely linked to growth and production, and a major driver of nitrogen losses to the environment. Protein digestibility is traditionally quantified using fecal or ileal measurements; however, these approaches are labor-intensive and difficult to apply at scale. Consequently, there is growing interest in blood-based biomarkers that can provide indirect yet practical insight into (i) the digestion and absorption of dietary protein and amino acids (AA) and (ii) the extent to which surplus absorbed nitrogen is converted into excretory products, primarily urea in mammals and uric acid in birds. Blood biomarkers are attractive because they are minimally invasive, allow repeated sampling over time, and can be readily integrated into precision feeding, herd monitoring, and genetic selection programs. This review synthesizes current knowledge on the physiological links between protein digestion, AA utilization, and nitrogen excretion, and critically evaluates blood biomarkers across ruminants and monogastric species. Biomarkers discussed include plasma urea nitrogen (PUN) or serum urea nitrogen (SUN), milk urea nitrogen (MUN), plasma uric acid, blood AA profiles and postprandial kinetics, emerging peptide-derived AA indicators, and metabolomics-based biomarker panels. Key confounding factors affecting interpretation such as renal function, energy balance, rumen nitrogen recycling, physiological stage, and sampling time are examined, and practical standards for biomarker application and validation are proposed. Overall, the review highlights the value of a panel-based biomarker approach for improving assessment of protein utilization efficiency and nitrogen excretion risk in animal production systems.

1. Introduction

Dietary protein utilization is a central determinant of productivity, efficiency, and sustainability in animal production systems. Protein is one of the most expensive dietary components, and inefficiencies in protein digestion and utilization lead not only to reduced animal performance but also to excessive nitrogen excretion, which contributes to environmental pollution through ammonia volatilization, nitrate leaching, and greenhouse gas emissions (Cai & Akiyama, 2016; Ghaly & Ramakrishnan, 2015; Walling & Vaneeckhaute, 2022). Consequently, improving protein digestibility and nitrogen use efficiency (NUE) has become a major objective in modern animal nutrition research across ruminant and monogastric species.

Protein digestibility traditionally refers to the proportion of dietary protein that is hydrolyzed and absorbed as AA or small peptides in the gastrointestinal tract. In research settings, digestibility is commonly quantified using total fecal collection, marker-based techniques, or ileal sampling to distinguish undigested dietary protein from endogenous losses. While these methods provide direct and mechanistically meaningful estimates of protein digestion, they are labor-intensive, invasive, and often impractical for large-scale studies or routine monitoring in commercial production systems (Kim et al., 2020; Van Wyk, 2017). These limitations have stimulated growing interest in indirect indicators, particularly blood-based biomarkers that can reflect protein digestion, absorption, utilization, and excretion in a minimally invasive manner.

Blood biomarkers are attractive because blood integrates metabolic signals arising from digestion, absorption, hepatic processing, and peripheral tissue utilization. Unlike fecal or urinary measurements, blood sampling can be repeated over time, enabling dynamic assessment of postprandial responses and metabolic adaptation to dietary changes. Advances in analytical technologies, including high-throughput biochemical assays, targeted AA profiling, and metabolomics, have further expanded the potential of blood-based indicators to serve as functional biomarkers of protein nutrition (Pedlar et al., 2019; Singh & Chandra, 2024).

A fundamental physiological link between dietary protein intake and blood biomarkers lies in the fate of absorbed AA. When dietary protein is efficiently digested and AA are supplied in appropriate proportions relative to animal requirements, a greater fraction of absorbed nitrogen is retained as body or milk protein. In contrast, excess or imbalanced AA are deaminated, and their nitrogen is converted into excretory products primarily urea in mammals and uric acid in birds. These metabolites circulate in blood before excretion via urine or, in the case of lactating animals, partial transfer into milk. As a result, blood concentrations of urea or uric acid provide integrated signals of nitrogen surplus and inefficiency in protein utilization (Lin, 2023; Marín-García et al., 2022).

In ruminants, the relationship between protein digestion and blood biomarkers is further complicated by rumen microbial metabolism. A substantial proportion of dietary protein is degraded in the rumen to peptides, AA, and ammonia, which can be incorporated into microbial protein if sufficient fermentable energy is available. Inefficient capture of ruminal ammonia leads to ammonia absorption, hepatic urea synthesis, and increased circulating urea concentrations. Urea can then be excreted in urine or recycled back to the rumen via saliva and epithelial transport, linking blood urea levels to both rumen function and whole-animal nitrogen balance (Getahun et al., 2019; Silva et al., 2019). This unique physiology makes blood urea nitrogen (BUN) or PUN one of the most widely studied biomarkers of protein feeding adequacy and nitrogen excretion potential in ruminant systems.

In monogastric animals, such as pigs and poultry, protein digestion occurs primarily in the stomach and small intestine, and absorbed AA enter the systemic circulation more directly. In these species, postprandial plasma AA profiles and kinetics are particularly informative, as they reflect both the extent and the rate of protein digestion and absorption. Recent studies demonstrate that differences in protein source, processing, or AA balance alter plasma AA appearance patterns, which in turn influence protein synthesis and nitrogen excretion (Eugenio et al., 2023; Reichl, 2019). In poultry, plasma uric acid concentration has emerged as a key biomarker of AA catabolism and nitrogen excretion, especially in studies evaluating reduced-crude-protein diets supplemented with crystalline AA (Selle et al., 2021; Wang et al., 2024).

Beyond classical single-analyte markers such as urea or uric acid, modern nutrition research increasingly adopts a systems-level perspective. Metabolomics-based studies reveal that dietary protein level and AA balance influence a wide array of circulating metabolites related to AA catabolism, the urea cycle, oxidative stress, and energy metabolism. These metabolite patterns or “metabolic signatures” can provide deeper insight into the efficiency of protein utilization and the biological mechanisms underlying nitrogen excretion (Mandal et al., 2024; Yata et al., 2024). Such approaches hold promise for identifying composite biomarker panels that outperform single indicators in predicting digestibility-related outcomes.

Despite their promise, blood biomarkers should not be viewed as direct substitutes for classical digestibility measurements. Rather, they represent functional indicators of how dietary protein is handled by the animal after digestion and absorption. Blood urea or uric acid primarily reflect nitrogen surplus and excretion potential, while plasma AA profiles and emerging peptide-derived AA assays are more closely linked to digestion and absorption dynamics. Interpretation of these biomarkers requires careful consideration of confounding factors such as energy balance, physiological stage, renal function, feeding frequency, and sampling time relative to meals (Chen et al., 2023; Han et al., 2024).

Given increasing societal pressure to reduce nitrogen losses from animal agriculture, there is strong incentive to develop practical, scalable tools to monitor protein utilization efficiency. Blood biomarkers offer a promising bridge between mechanistic nutrition research and on-farm decision-making, enabling rapid assessment of dietary protein adequacy, identification of inefficiencies, and evaluation of precision feeding strategies. However, their application requires a clear understanding of their physiological basis, strengths, and limitations.

Beyond their importance in animal production systems, blood-based biomarkers are increasingly relevant in broader animal research contexts, including experimental animal models used in nutritional, physiological, and biomedical investigations (Choudhary, 2025; Choudhary & Sarkar, 2025). Reliable biomarker-based assessment of protein metabolism can improve monitoring of animal responses to dietary interventions while reducing reliance on more invasive or labor-intensive methodologies. In parallel, advances in artificial intelligence (AI), machine learning, and precision livestock technologies are creating new opportunities for integrating complex biomarker datasets into predictive models of nutrient utilization and nitrogen excretion. Emerging AI-assisted analytical approaches may facilitate the identification of complex biomarker patterns and support data-driven nutritional management strategies in both research and commercial animal production systems (Choudhary, 2026; Choudhary et al., 2025).

Therefore, the objective of this review is to synthesize current knowledge (2015–2025) on blood-based biomarkers used to assess protein digestibility, utilization, and nitrogen excretion in animals. The review focuses on the biological rationale linking digestion to circulating biomarkers, evaluates evidence across ruminants and monogastric species, and discusses methodological considerations and future directions for integrating blood biomarkers into protein nutrition research and practice.

2. Why blood biomarkers for protein digestibility and nitrogen excretion?

Protein nutrition in animals encompasses two closely related but conceptually distinct processes: digestibility and absorption, and utilization and excretion. Protein digestibility refers to the extent to which dietary protein is hydrolyzed in the gastrointestinal tract and absorbed as AA and small peptides, either at the ileal or total-tract level. In contrast, protein utilization reflects the proportion of absorbed nitrogen that is retained in body tissues or secreted in animal products such as milk, while the remaining surplus nitrogen is converted into waste products primarily urea in mammals and uric acid in birds and subsequently excreted (Moughan, 2018; Moughan et al., 2018b). Although these processes are physiologically linked, they are regulated by different mechanisms and are influenced by dietary composition, energy supply, and animal metabolic status.

Classical methods for assessing protein digestibility rely on fecal or ileal digesta collection, marker techniques, or surgical cannulation. While these approaches provide direct and mechanistically robust estimates of protein digestion, they are labor-intensive, invasive, costly, and difficult to implement on a large scale or under commercial production conditions (Castillo & Hernández, 2021; Dallas et al., 2017). These practical constraints have driven growing interest in blood-based biomarkers as indirect but scalable indicators of protein digestion, absorption, and metabolic fate. Blood sampling is minimally invasive, can be repeated over time within the same animal, and is compatible with high-throughput analytical platforms, including targeted biochemical assays and modern “omics” technologies. As a result, recent nutrition research increasingly frames blood biomarkers as tools for precision nutrition, capable of capturing not only dietary exposure but also the animal’s metabolic response and efficiency of nutrient utilization (Pedlar et al., 2019; Picó et al., 2019).

A key physiological rationale for using blood biomarkers to assess protein utilization and nitrogen excretion lies in the metabolic fate of absorbed AA. These AA are primarily directed toward protein synthesis; however, when supply exceeds anabolic demand or when the AA profile is imbalanced relative to requirements, surplus AA are deaminated. The nitrogen released during deamination is converted to urea in mammals or uric acid in birds, which circulates in the blood before being excreted via urine or, in lactating animals, partially secreted into milk (Li et al., 2020; de Oliveira, 2024). Consequently, blood concentrations of urea or uric acid serve as integrated indicators of nitrogen surplus and inefficiency in protein utilization, reflecting blood-level responses to dietary protein supply relative to energy and AA requirements (Lin, 2023; Marín-García et al., 2022).

In ruminants, the relationship between dietary protein, blood biomarkers, and nitrogen excretion is further complicated by rumen microbial metabolism and urea recycling. A substantial portion of dietary protein is degraded in the rumen to ammonia, which can be incorporated into microbial protein when sufficient fermentable energy is available. When this capture is inefficient, ammonia is absorbed into the bloodstream and converted to urea in the liver. Circulating urea can then be excreted in urine or recycled back to the rumen via saliva and urea transporters in the rumen epithelium, creating a dynamic link between rumen nitrogen balance, hepatic metabolism, and blood urea concentration (Getabalew & Negash, 2020; Getahun et al., 2019). Recent reviews of urea transport and hydrolysis in the rumen emphasize that inefficient microbial utilization of ammonia increases urea synthesis and nitrogen losses, thereby strengthening the association between dietary protein balance, rumen function, and blood urea as a biomarker of nitrogen excretion potential (Guarnido-Lopez et al., 2021; Hailemariam et al., 2021).

3. Biological basis: from dietary protein to blood and nitrogen excretion

3.1. Digestion and absorption generate AA and peptides entering portal blood

Dietary proteins are hydrolyzed in the gastrointestinal tract into free AA and small peptides, which are absorbed across the intestinal epithelium and subsequently appear in portal and systemic circulation. The concentration and temporal patterns of circulating AA are strongly influenced by digestion kinetics, intestinal transporter activity, and first-pass utilization by the gut and liver. In pigs, controlled experimental studies demonstrate that the physical and chemical form of dietary protein whether intact protein, hydrolyzed protein, or free crystalline AA markedly alters the rate, magnitude, and duration of postprandial AA appearance in plasma (Eugenio, 2022; Eugenio et al., 2022). Faster plasma AA peaks are generally observed with free or hydrolyzed AA sources, whereas intact proteins produce slower and more sustained AA release, which can influence downstream protein synthesis and nitrogen metabolism (Fig. 1). These findings support the concept that blood AA kinetics, including peak concentration and area under the curve (AUC), can serve as functional indicators of protein digestion and absorption and provide insight into the metabolic fate of absorbed nitrogen (Trommelen et al., 2021; Wolfe et al., 2021).

Fig. 1.

Physiological pathway of dietary protein digestion, absorption, metabolism, and nitrogen biomarkers in animals. Dietary proteins are hydrolyzed in the gastrointestinal tract into peptides and amino acids, which are absorbed across the intestinal epithelium and enter the portal circulation. Absorbed amino acids are utilized for protein synthesis or undergo catabolism, resulting in the production of nitrogenous waste products. In mammals, surplus nitrogen is primarily converted to urea via the urea cycle and appears in blood as plasma urea nitrogen or blood urea nitrogen, whereas in birds it is excreted mainly as uric acid. The figure also illustrates the contribution of rumen nitrogen recycling in ruminants and the relationship between amino acid metabolism and nitrogen excretion. Abbreviations: (AA) amino acid(s); (NH3) ammonia; (PUN) plasma urea nitrogen; (BUN) blood urea nitrogen. The figures were created and assembled using Microsoft PowerPoint, BioRender, and Adobe Photoshop.

3.2. AA utilization versus catabolism drives urea in mammals and uric acid in birds

Following absorption, AA are primarily utilized for protein synthesis in tissues; however, when dietary AA supply exceeds anabolic demand or when the AA profile is imbalanced relative to the animal’s requirements, surplus AA are oxidized. The nitrogen released during AA catabolism is subsequently converted into urea in mammals or uric acid in birds and enters the bloodstream prior to excretion. In pigs, recent modeling approaches integrating postprandial plasma AA and metabolite profiles demonstrate that AA imbalance reduces the efficiency of protein synthesis and increases nitrogen catabolism, thereby elevating the risk of nitrogen excretion (Niyazov & Ostrenko, 2020; Wu et al., 2018).

In poultry, uric acid represents the principal nitrogenous end product, and circulating uric acid concentrations are closely linked to dietary crude protein (CP) level and AA adequacy. Studies in broilers show that excessive CP intake or inadequate AA balance increases uric acid synthesis and nitrogen excretion, whereas reduced CP diets supplemented with essential AA can improve nitrogen utilization and alter uric acid metabolism (Belloir et al., 2017; Musigwa et al., 2020). Thus, plasma uric acid serves as a key biomarker of AA catabolism and nitrogen excretion in avian species.

3.3. Ruminant-Specific pathway: rumen ammonia, hepatic urea, recycling, and excretion

Ruminants exhibit a distinct nitrogen metabolism due to the presence of the rumen, where a substantial proportion of dietary protein is degraded by microorganisms to peptides, AA, and ammonia. When sufficient fermentable energy is available, rumen microbes incorporate ammonia into microbial protein, which later contributes to the host’s AA supply. However, when microbial capture of ammonia is inefficient such as under conditions of protein energy imbalance excess ammonia is absorbed across the rumen wall and converted to urea in the liver. Circulating urea may then be excreted in urine or recycled back to the rumen via saliva and urea transporters in the rumen epithelium, forming a dynamic urea nitrogen recycling system (Hailemariam et al., 2021; Zhong et al., 2022).

Recent reviews emphasize that urea transport, recycling, and hydrolysis are central regulators of NUE in ruminants, but direct quantification of these fluxes in vivo is technically challenging. Consequently, blood-based indicators particularly PUN or SUN are increasingly used as practical integrative biomarkers of dietary protein balance, rumen nitrogen capture, and whole-animal nitrogen excretion potential (Lavery & Ferris, 2021; Nichols et al., 2022). These biomarkers provide valuable insight into nitrogen metabolism when interpreted alongside dietary composition and production responses.

4. What should an ideal blood biomarker represent?

An ideal blood biomarker for evaluating protein nutrition should capture biologically meaningful information along the continuum from dietary protein digestion and absorption to nitrogen utilization and excretion. Because no single biomarker can fully represent all aspects of protein metabolism, recent literature increasingly conceptualizes blood biomarkers as falling into complementary “tiers,” each reflecting a different physiological level of protein handling by the animal (Corella & Ordovás, 2015; Holen et al., 2016).

The first tier includes direct indicators of protein digestion and absorption, which are most closely aligned with digestibility. These biomarkers reflect the appearance of absorbed AA and peptides in circulation following a meal. Measures such as postprandial plasma AA concentrations, AA AUC, time to peak AA concentration, and emerging peptide-derived AA indices provide insight into the rate and extent of protein hydrolysis and intestinal absorption. Experimental studies in pigs demonstrate that differences in protein source, processing, or chemical form significantly alter plasma AA kinetics, supporting the use of these measures as functional indicators of protein digestion and absorption dynamics (Nørgaard et al., 2021; Pierzynowska et al., 2025). However, these biomarkers require precise sampling relative to feeding time and are therefore more suitable for controlled experimental settings.

The second tier comprises indicators of nitrogen surplus and excretion, which are most closely related to the efficiency with which absorbed protein is utilized rather than digested. Among these, plasma or serum urea nitrogen is the most widely used biomarker in mammals, reflecting hepatic conversion of surplus AA nitrogen into urea prior to excretion. In dairy cattle and other ruminants, MUN is strongly correlated with blood urea concentrations and is frequently used as a practical proxy for assessing dietary protein adequacy and nitrogen excretion potential (Lavery & Ferris, 2021; Zhao et al., 2025). In avian species, plasma uric acid serves a similar role, as uric acid is the principal nitrogenous waste product formed during AA catabolism. Numerous studies show that dietary CP level and AA balance influence circulating uric acid concentrations, linking this biomarker to nitrogen excretion efficiency in poultry (Kriseldi, 2016; Selle et al., 2021).

The third tier consists of systems-level metabolic “efficiency signatures”, typically derived from metabolomics analyses of blood plasma or serum. These biomarkers do not represent single analytes but rather coordinated patterns of metabolites associated with AA catabolism, the urea cycle, oxidative stress responses, and related metabolic pathways. Recent metabolomics studies demonstrate that shifts in pathways such as arginine and proline metabolism, glutathione metabolism, and tryptophan degradation are associated with differences in NUE and protein feeding strategies (Hou et al., 2020; Li et al., 2024). Although these signatures are less directly interpretable than single-analyte biomarkers, they offer a holistic view of protein metabolism and may ultimately improve prediction of NUE when integrated with classical indicators.

Collectively, these three tiers highlight that an ideal blood biomarker should not be viewed in isolation. Rather, effective assessment of protein digestibility and nitrogen excretion is most likely achieved through a complementary biomarker framework, combining digestion-related AA kinetics, surplus-related urea or uric acid indicators, and broader metabolic signatures that capture system-wide efficiency.

5. Core blood biomarkers for protein excretion and protein utilization

5.1. Plasma/serum/blood urea nitrogen

Urea is synthesized in the liver mainly from ammonia and AA nitrogen, so its concentration in blood reflects the integrated balance among nitrogen supply, AA utilization for protein deposition, nitrogen recycling, and nitrogen excretion. In ruminants, ammonia entering hepatic metabolism is strongly influenced by ruminal protein degradation and subsequent ammonia absorption, as well as AA catabolism when absorbed AA exceed anabolic demand. In monogastric animals, blood urea is driven primarily by post-absorptive AA deamination and hepatic ureagenesis (Fig. 2). Therefore, elevated plasma/serum/blood urea nitrogen generally indicates greater nitrogen surplus, reduced efficiency of AA utilization for growth or milk synthesis, or a mismatch between protein supply and available energy needed for nitrogen retention (Moughan et al., 2018a; Zhao et al., 2025). Because it integrates rumen-derived nitrogen load (in ruminants) and systemic AA catabolism (in all mammals), BUN is consistently identified as a practical biomarker of dietary protein status and NUE in applied animal nutrition (Cantalapiedra-Hijar et al., 2018; Mortazavi et al., 2025).

Fig. 2.

Postprandial blood biomarker dynamics and factors influencing interpretation of protein digestibility and nitrogen excretion. Panel A illustrates postprandial amino acid appearance profiles for proteins differing in digestion rate and absorption kinetics. Panel B depicts the relationship between amino acid surplus and the blood urea response. Panel C summarizes species-specific nitrogen excretion pathways and associated biomarkers. Panel D highlights key physiological and nutritional factors that may influence biomarker interpretation. Panel E presents practical sampling considerations for biomarker assessment. Abbreviations: (AA) amino acid(s); (Cmax) maximum plasma amino acid concentration; (Tmax) time to maximum concentration; (AUC) area under the concentration-time curve; (BUN) blood urea nitrogen; (PUN) plasma urea nitrogen; (MUN) milk urea nitrogen. The figures were created and assembled using Microsoft PowerPoint, BioRender, and Adobe Photoshop.

In dairy cattle, controlled dietary interventions demonstrate that lowering CP supply can reduce ruminal ammonia, decrease circulating urea and milk urea nitrogen, and reduce urinary urea nitrogen excretion, collectively illustrating a consistent linkage between dietary protein supply, systemic urea formation, and nitrogen loss pathways (Mutsvangwa et al., 2016). In small ruminants, recent studies in lambs similarly show that metabolizable protein (MP) level affects nitrogen digestion and metabolism and is accompanied by changes in serum biochemical traits, with serum urea responding to shifts in nitrogen supply and utilization efficiency (Xu et al., 2019) (Table 1). In pigs, both experimental and selection-focused research indicates that BUN increases when digestible protein supply exceeds AA requirements, supporting its utility for identifying nitrogen overload in high-performance systems (Mansilla et al., 2017). Importantly, genomic analyses also support BUN as an indicator trait associated with NUE and correlated growth/feed efficiency phenotypes, highlighting its relevance beyond short-term feeding trials (Ojo et al., 2024).

Table 1.

Blood biomarkers used to assess protein digestibility, utilization, and nitrogen excretion in animals.

Biomarker	Primary biological process represented	Nutritional endpoint	Species applicability	Key strengths	Limitations / confounders	References
(PUN/SUN/BUN)	Hepatic urea synthesis from surplus AA and ammonia nitrogen	Nitrogen surplus, nitrogen excretion potential, NUE	Mammals (ruminants, pigs)	Easy to measure; low cost; integrative whole-animal indicator	Influenced by renal function, energy balance, physiological stage, rumen urea recycling	(Bezerra et al., 2025; Li et al., 2025b; Sobotka & Drażbo, 2025; Yoo et al., 2025)
MUN	Diffusion of blood urea into milk	Nitrogen surplus (proxy for blood urea)	Dairy cattle	Non-invasive; routinely available in milk recording	Limited to lactation; affected by milk yield and stage of lactation	(Heerden, 2025; Zhao et al., 2025)
Plasma uric acid	AA catabolism and uric acid synthesis	Nitrogen excretion efficiency	Birds (broilers, layers, waterfowl)	Biologically direct excretory endpoint	Influenced by renal function, hydration, feeding status	(Alabi & Adedokun, 2025; Houdijk et al., 2025)
Plasma AA profile	Post-absorptive appearance of AA	Digestibility / utilization	Monogastrics (mainly pigs)	Mechanistic insight into AA availability	Requires controlled feeding and precise timing	(Banton, 2025; Chen et al., 2025a)
Postprandial AA kinetics (Cmax, Tmax, AUC)	Rate and extent of AA absorption	Digestibility / absorption dynamics	Monogastrics; research models	High resolution of digestion kinetics	Labor-intensive; multiple blood samples	(Fan et al., 2025; Li et al., 2025a)
Peptide-derived AA in plasma	Intestinal absorption of peptides	Digestibility / absorption	Experimental livestock models	Closer to digestion than free AA alone	Emerging methodology; limited validation	(Gelli et al., 2025)
Metabolomics-derived biomarker panels	System-level AA catabolism and metabolic regulation	NUE, protein utilization	Multi-species	High information content; pathway-level insight	Cost; need for standardization and validation	(Fouts et al., 2025; Olagoke-Komolafe & Oyeboade, 2025)
Creatinine / SDMA	Renal clearance capacity	Contextual (interpretive)	Multi-species	Improves interpretation of urea/uric acid	Not related to digestibility or utilization	(Talukdar et al., 2025)

Note: Abbreviations: (AA) amino acid(s); (AUC) area under the concentration-time curve; (BUN) blood urea nitrogen; (Cmax) maximum plasma amino acid concentration; (MUN) milk urea nitrogen; (NUE) nitrogen use efficiency; (PUN) plasma urea nitrogen; (SDMA) symmetric dimethylarginine; (SUN) serum urea nitrogen; (Tmax) time to maximum plasma amino acid concentration.

Evidence from rabbit nutrition studies further supports the value of PUN as a biomarker of dietary amino acid adequacy and protein utilization. Experimental studies demonstrated that PUN responds sensitively to dietary amino acid imbalances, particularly deficiencies in lysine, sulfur amino acids, and threonine, reflecting increased amino acid catabolism and urea production. Lower PUN concentrations were associated with more balanced amino acid supply, improved nutrient retention, and enhanced protein utilization efficiency. These findings indicate that PUN can serve not only as an indicator of nitrogen surplus but also as a practical nutritional biomarker for evaluating amino acid balance and dietary protein quality in growing rabbits, thereby extending its applicability beyond traditional ruminant and swine nutrition systems (Marín-García et al., 2021, 2023; Marín-García et al., 2020a, 2020b).

BUN is inexpensive, widely available, and easy to implement in both research and routine monitoring. It responds consistently to dietary CP level and AA energy balance across diverse production systems, making it a practical indicator of nitrogen surplus and excretion risk (Cheng et al., 2023; Raimann et al., 2016). Because it shows heritable variation and is associated with NUE related phenotypes, it can also be incorporated into genetic evaluation and selection frameworks, particularly in pigs (Berghaus et al., 2023).

Blood urea is not a direct marker of digestibility; rather, it reflects post-absorptive nitrogen metabolism and the need to dispose of surplus nitrogen. Its interpretation is therefore influenced by renal function and hydration status (affecting clearance), energy balance (because AA oxidation increases when energy is limiting), and physiological stage (growth, lactation, or maintenance). In ruminants, interpretation is further complicated by urea recycling and by the degree of synchronization between rumen degradable protein and fermentable carbohydrate supply, which affects microbial capture of ammonia and subsequent hepatic urea production (Greeff, 2021; Klatt, 2019). Blood urea also varies with sampling time relative to feeding; consequently, consistent sampling protocols are essential for research and herd/flock monitoring applications. In practice, pairing urea with contextual markers such as creatinine or symmetric dimethylarginine (SDMA) for renal function, or metabolomics signatures reflecting AA catabolism can improve interpretability and help distinguish dietary nitrogen surplus from altered clearance or health-related effects (Gilsenan et al., 2024; Sargent et al., 2021).

5.2. MUN as a proxy for blood urea in dairy systems

Although not a blood biomarker, MUN is strongly correlated with BUN and is widely used as a non-invasive indicator of protein feeding adequacy and NUE in dairy systems (Fig. 2). Because urea diffuses readily between blood and milk, MUN closely tracks systemic urea and reflects the same underlying processes AA catabolism, hepatic ureagenesis, and nitrogen surplus associated with inefficient ruminal ammonia capture or excess dietary protein supply (Babiciu et al., 2025; Papović et al., 2021). Evidence syntheses and applied studies support MUN as a practical tool for evaluating dietary protein status and NUE, particularly when interpreted together with dietary CP level and production responses (Chanda et al., 2024). From a laboratory perspective, analytical evaluations of enzymatic spectrophotometric procedures indicate that MUN can be measured with acceptable precision and repeatability, supporting its routine use as a standardized diagnostic metric in dairy nutrition programs (Bezerra et al., 2025).

5.3. Plasma uric acid in birds (and uric acid–related indicators)

In birds, nitrogen is excreted primarily as uric acid; therefore, plasma uric acid is a biologically appropriate biomarker of AA catabolism and nitrogen surplus. This marker is particularly relevant in modern poultry nutrition, where reduced-crude-protein diets supplemented with essential AA are increasingly used to improve NUE and reduce environmental nitrogen losses (Cambra-López et al., 2022; Selle et al., 2023).

Broiler studies and contemporary reviews consistently report that excessive CP intake increases nitrogen excretion and uric acid output, whereas reducing CP while maintaining essential AA adequacy can reduce nitrogen excretion and may lower circulating uric acid concentrations, reflecting improved nitrogen utilization (Darsi et al., 2025; Kriseldi et al., 2018). In waterfowl, recent veterinary work also demonstrates that dietary protein source and level influence uric acid metabolism and kidney-related endpoints, reinforcing uric acid as a central biomarker of avian protein metabolism and nitrogen disposal (Małyszko et al., 2017). At the same time, interpretation is complicated because avian excreta contain both urinary uric acid and fecal nitrogen fractions, and the proportion of nitrogen excreted as uric acid varies with diet composition, hydration status, health, and short-term feeding dynamics (Barszcz et al., 2024).

Plasma uric acid is closer than urea to the avian excretory endpoint, but it still reflects utilization and catabolism rather than digestibility (Fig. 2). It is also influenced by renal function, hydration status, physiological stress, and sampling timing relative to feeding, so these factors must be controlled to improve interpretability in both experimental and applied contexts (Fairbrother, 2020; Scope & Schwendenwein, 2020).

6. Blood AA and postprandial kinetics as digestibility-related biomarkers

6.1. Conceptual link between protein digestibility and blood AA dynamics

When a dietary protein source is highly digestible and its AA are efficiently absorbed, this is expected to be reflected in characteristic changes in circulating AA profiles following a meal. Key kinetic parameters include the maximum plasma AA concentration (Cmax), time to reach peak concentration (Tmax), AA area under the concentration time curve (AUC), and the relative patterns of indispensable versus dispensable AA (Fig. 2). Together, these parameters describe the rate and extent of AA appearance in blood and provide indirect information on protein digestion and intestinal absorption (Fig. 3).

Fig. 3.

Postprandial plasma AA kinetics following proteins differing in digestibility. The figure summarizes three complementary biomarker tiers. Digestion and absorption indicators include plasma amino acid profiles, amino acid kinetics, and peptide-derived amino acids, which reflect protein digestion and intestinal absorption. Nitrogen utilization and excretion indicators include blood urea nitrogen, plasma urea nitrogen, milk urea nitrogen, and uric acid, which reflect amino acid catabolism and nitrogen disposal. Metabolomics-based efficiency signatures provide systems-level information on metabolic pathways associated with protein utilization and nitrogen use efficiency. Physiological stage, renal function, energy balance, and feeding status are important factors affecting biomarker interpretation. Abbreviations: (Cmax) maximum plasma amino acid concentration; (Tmax) time to maximum concentration; (AUC) area under the concentration-time curve; (AA/AAs) amino acid(s); (BUN) blood urea nitrogen; (SUN) serum urea nitrogen; (PUN) plasma urea nitrogen; (MUN), milk urea nitrogen; (SDMA) symmetric dimethylarginine; (ATP) adenosine triphosphate. The figures were created and assembled using Microsoft PowerPoint, BioRender, and Adobe Photoshop.

In pigs, several controlled feeding studies demonstrate that diets differing in protein source, processing, or AA form (intact protein, hydrolyzed protein, or crystalline AA) produce distinct postprandial plasma AA and metabolite profiles. Faster and higher plasma AA peaks are typically observed with free or hydrolyzed AA, whereas intact proteins tend to generate slower, more sustained AA appearance patterns, consistent with differences in digestion and absorption kinetics (Karalis, 2023; Zhang et al., 2019). These findings establish a mechanistic link between protein digestion and absorption and downstream metabolic utilization and nitrogen loss, supporting the use of blood AA kinetics as functional biomarkers related to digestibility (Paraskeuas et al., 2017).

6.2. What blood AA patterns can reveal

Blood AA responses provide valuable insight into several aspects of protein nutrition. First, they can be used to detect AA imbalance, which promotes increased AA oxidation and nitrogen excretion when the dietary AA profile does not match the animal’s requirements (Estes, 2017; Soultoukis & Partridge, 2016). Second, AA kinetics allow comparison of “fast” versus “slow” protein sources in terms of functional bioavailability, which is relevant for optimizing synchronization between AA supply and tissue protein synthesis. Third, plasma AA patterns can be used to evaluate whether formulated diets deliver the expected AA appearance profiles in vivo, thereby validating diet formulation assumptions (Eugenio et al., 2022).

Beyond targeted AA measurements, recent metabolomics and integrated AA-profiling studies reveal that nutritional strategies designed to improve NUE are associated with coordinated changes in circulating metabolites related to arginine and proline metabolism, glutathione metabolism, and tryptophan degradation. These pathway-level shifts suggest that combined blood metabolite and AA signatures can serve as sensitive indicators of NUE and metabolic response to protein feeding strategies (Nunes et al., 2024; Yi et al., 2024).

6.3. Practical constraints and applicability

Despite their mechanistic value, the use of blood AA kinetics as digestibility-related biomarkers presents several practical challenges. Accurate characterization of AA appearance requires timed blood sampling, typically involving baseline (fasted) samples followed by multiple postprandial time points to capture the full concentration time curve. In addition, reliable interpretation depends on access to precise AA quantification methods, such as targeted liquid chromatography mass spectrometry (LC–MS) or high performance AA analyzers, and on strict control of meal size, feeding frequency, and sampling timing (Le et al., 2020; Ortega et al., 2023).

As a result, blood AA kinetics are currently best suited for controlled research settings, mechanistic nutrition studies, and high-value precision feeding applications, rather than routine on-farm monitoring. When applied appropriately, however, they offer a powerful complementary approach to classical digestibility measurements by linking digestion and absorption processes with systemic metabolic responses and nitrogen utilization outcomes (Table 2).

Table 2.

Evidence across animal species linking blood biomarkers to protein digestibility, utilization, and nitrogen excretion.

Species / production system	Dietary protein or AA intervention	Blood biomarker(s) evaluated	Direction and nature of biomarker response	Biological interpretation	Primary nutritional endpoint	References
Dairy cattle	Reduced CP with balanced AA	PUN, MUN	↓ blood and milk urea	Reduced nitrogen surplus and urinary nitrogen excretion	Nitrogen excretion / NUE	(Siegert et al., 2025; Zhang et al., 2025b)
Dairy cattle	Altered rumen degradable vs undegradable protein	PUN, MUN	Changes in urea depending on rumen nitrogen capture	Reflects rumen ammonia utilization and urea recycling	Utilization / excretion	(Kim et al., 2025)
Beef cattle	Protein–energy synchronization	Blood urea	↓ urea with improved synchronization	Improved microbial capture of ammonia	Utilization efficiency	(Zhao et al., 2025)
Sheep / goats	Varying MP supply	Serum urea	↑ urea with excess MP	Nitrogen oversupply relative to requirements	Excretion	(de Oliveira et al., 2025)
Pigs	Hydrolyzed vs intact protein	Plasma AA kinetics	Faster AA appearance, higher Cmax	Faster digestion and absorption	Digestibility / absorption	(Daly et al., 2025; Pierzynowska et al., 2025)
Pigs	AA imbalance or excess digestible protein	BUN, plasma metabolites	↑ urea; altered AA-catabolism pathways	Increased AA oxidation and nitrogen loss	Utilization / excretion	(Erinle, 2025; Nørgaard et al., 2021)
Pigs (genetic studies)	Selection for feed/nitrogen efficiency	BUN	Lower BUN in efficient genotypes	BUN as indicator trait for NUE	Utilization efficiency	(Bonhoff, 2025)
Broiler chickens	Reduced CP with AA supplementation	Plasma uric acid	↓ uric acid	Improved nitrogen utilization	Nitrogen excretion	(Darsi et al., 2025; Siegert et al., 2025)
Laying hens	High vs low protein diets	Plasma uric acid	↑ uric acid with excess CP	Increased AA catabolism	Excretion	(Chathuranga & Heo, 2025)
Waterfowl (goslings)	Protein source and level	Plasma uric acid	Diet-dependent changes	Protein metabolism and renal load	Excretion	(Chen et al., 2025b)
Pigs / ruminants	Precision protein feeding	Metabolomics panels	Pathway-level metabolite shifts	System-level NUE signatures	Integrated response	(Wei et al., 2025; Yun et al., 2025)

Note: Abbreviations: (AA) amino acid(s); (BUN) blood urea nitrogen; (CP) crude protein; (Cmax) maximum plasma amino acid concentration; (MP) metabolizable protein; (MUN) milk urea nitrogen; (NUE) nitrogen use efficiency; (PUN) plasma urea nitrogen.

7. Emerging “Closer-to-Digestion” biomarkers: peptide-derived AA in plasma

A key limitation of monitoring only free circulating AA is that a substantial proportion of dietary protein is absorbed transiently in the form of small peptides rather than as free AA. Traditional plasma AA profiling therefore captures only part of the absorbed nitrogen pool and may underestimate differences in protein digestion and absorption among diets. Recent methodological advances address this limitation by enabling quantification of peptide-derived AA in plasma following chemical or enzymatic hydrolysis, thereby providing a more integrated measure of absorbed protein-derived nitrogen (Weijzen et al., 2022).

A recent methods focused study developed a high-throughput ninhydrin based assay to quantify peptide-derived AA in plasma and demonstrated its application as a functional biomarker of protein digestion and absorption in vivo (Fig. 2). This approach revealed that peptide derived AA constitute a meaningful and dynamic fraction of circulating nitrogen following feeding and that their abundance varies with dietary protein form and digestion kinetics (Li et al., 2025a). These findings support the concept that plasma peptide-derived AA measurements may provide a closer approximation of intestinal absorption processes than free AA measurements alone.

Conceptually, peptide-derived AA biomarkers may be particularly informative under conditions where protein digestion is altered, such as during gastrointestinal disease, reduced endogenous enzyme activity, or when evaluating diets differing in protein processing, hydrolysis, or peptide delivery characteristics. In such contexts, peptide-based indicators could improve discrimination among protein sources that appear similar when assessed using traditional urea-based or free AA-based biomarkers (Ryan et al., 2025).

Although still at an early stage of application in livestock nutrition, peptide-derived AA biomarkers are promising because they target the absorption product stream more directly than systemic nitrogen surplus indicators such as blood urea. When combined with free AA kinetics and classical nitrogen excretion markers, these emerging biomarkers have the potential to improve mechanistic understanding of protein digestibility and to enhance precision in evaluating dietary protein quality and utilization (Cambra-López et al., 2022; Picó et al., 2019).

8. Metabolomics-based blood biomarker panels for protein utilization and nitrogen excretion

8.1. Why metabolomics is gaining attention

Metabolomics has emerged as a powerful approach for characterizing the systemic metabolic consequences of dietary protein supply and AA balance. Unlike single-analyte biomarkers, metabolomics captures coordinated changes across multiple metabolic pathways, including AA catabolism, urea-cycle related intermediates, redox and oxidative stress pathways such as glutathione metabolism, and host microbial co-metabolites influenced by diet. These pathways are directly linked to nitrogen utilization and excretion, making metabolomics particularly relevant for studying protein efficiency and nitrogen losses in animals (Corsetti et al., 2024; Wishart, 2019).

A major advantage of untargeted metabolomics is that it does not require prior selection of candidate biomarkers. Instead, hundreds to thousands of metabolites can be screened simultaneously, allowing the discovery of previously unrecognized metabolic indicators associated with protein utilization, AA balance, and nitrogen metabolism. This hypothesis-generating capability is particularly valuable in animal nutrition because many physiological responses to dietary protein are mediated through interconnected metabolic pathways that may not be captured by traditional biomarkers such as urea, uric acid, or individual amino acids. Consequently, untargeted metabolomics has become an increasingly important tool for identifying novel biomarker candidates and generating mechanistic insights into nutrient utilization (Nunes et al., 2024; Wishart, 2019).

Precision nutrition frameworks increasingly identify blood plasma or serum as central matrices for metabolomic profiling because they integrate signals from digestion, absorption, hepatic metabolism, peripheral tissue utilization, and diet-driven shifts in microbial activity. As a result, plasma metabolomics provides a holistic snapshot of metabolic status that reflects both dietary protein characteristics and the animal’s physiological response (Nunes et al., 2024; Zhang et al., 2020).

8.2. Evidence and examples in animals

In pigs, nutritional metabolomics studies demonstrate that dietary interventions targeting protein level, AA balance, or protein source produce measurable shifts in plasma metabolite profiles related to gut function, AA utilization, and nitrogen metabolism. These studies show that metabolite signatures can reveal mechanistic insights into how diets influence nutrient utilization beyond what is captured by classical growth or nitrogen balance measurements (Murray et al., 2022; Spring et al., 2020).

Rabbit models have also contributed to the development of metabolomics-based biomarkers relevant to protein nutrition. Recent targeted and untargeted metabolomics studies demonstrated that alterations in dietary amino acid balance rapidly modify plasma metabolic profiles in growing rabbits, leading to the identification of metabolites such as creatinine, urea, hydroxypropionic acid, and hydroxyoctadecadienoic acid as potential biomarkers of amino acid imbalance and protein utilization. Furthermore, metabolomics investigations based on the ideal protein concept identified additional metabolites, including pseudourine, citric acid, pantothenic acid, and enterolactone sulfate, as promising indicators of NUE and dietary amino acid adequacy. These findings support the value of rabbit models for biomarker discovery and illustrate how untargeted metabolomics can identify novel biomarkers beyond conventional indicators such as urea and amino acids, thereby enhancing assessment of protein utilization and nitrogen metabolism across animal species (Marín-García et al., 2022; Marín-García et al., 2024a, 2024b).

Recent work combining plasma metabolomics with targeted AA profiling has identified metabolite pathway differences associated with improved NUE. Specifically, alterations in pathways related to arginine biosynthesis, glutathione metabolism, and tryptophan metabolism have been linked to nutritional strategies aimed at optimizing protein utilization and reducing nitrogen excretion (Lei, 2023). These findings suggest that metabolomics-derived biomarker panels can capture integrated metabolic responses to protein feeding strategies and provide sensitive indicators of NUE.

In ruminant research, metabolomics-based approaches are increasingly used for biomarker discovery related to production traits, metabolic health, and nutritional status. Reviews in this field emphasize that complex traits such as protein utilization efficiency and nitrogen excretion are unlikely to be adequately described by single biomarkers, and instead support the use of multi-metabolite panels that reflect system-level metabolic regulation (Goldansaz, 2021; Zhang et al., 2025a). This perspective aligns with broader efforts in ruminant nutrition to integrate metabolomics with classical indicators such as urea nitrogen and performance data.

8.3. Strengths and limitations

The primary strength of metabolomics based blood biomarker panels lies in their high information content and ability to identify coordinated metabolic signatures associated with NUE or protein overfeeding. By capturing multiple pathways simultaneously, metabolomics can reveal subtle metabolic shifts that precede measurable changes in nitrogen excretion or animal performance (El-Hack et al., 2025; Lisuzzo, 2024).

However, several limitations constrain broader application. Metabolomics requires strict standardization of sampling protocols, including control of fasting or postprandial state and sampling time, as metabolic profiles are highly dynamic. In addition, advanced analytical instrumentation, specialized bioinformatics expertise, and robust validation across independent cohorts are necessary to ensure reproducibility and biological relevance. Consequently, while metabolomics offers substantial promise for advancing understanding of protein utilization and nitrogen excretion, its routine use in applied nutrition remains largely confined to research and high-value precision feeding contexts (Nunes et al., 2024).

9. Biomarkers linked to excretion processes and confounding physiology

9.1. Creatinine and renal-linked markers as context indicators (Not digestibility-specific)

Because circulating urea and uric acid concentrations are strongly influenced by renal clearance, indicators of kidney function can improve interpretation of whether elevated nitrogenous waste metabolites reflect true dietary nitrogen surplus or reduced excretory capacity. In veterinary practice, serum creatinine is widely used as a routine marker of renal function, and more recently SDMA has been evaluated as an additional biomarker with potential sensitivity for detecting changes in kidney function across species. Comparative veterinary studies report that SDMA and creatinine can provide complementary information on renal status, supporting the use of such markers when interpreting BUN or uric acid levels in nutrition and metabolism studies (Guess, 2016). Although renal biomarkers are not direct indicators of protein digestibility, they function as important context markers, helping distinguish changes in urea-related traits driven by dietary protein imbalance from those driven by altered clearance associated with hydration status, health, or renal dysfunction (Hokamp & Nabity, 2016; Pelegrin-Valls et al., 2020).

9.2. Protein metabolism biomarkers across physiological stages

Physiological stage can substantially influence baseline levels of blood biomarkers linked to protein metabolism, which has important implications for interpretation in both research and applied monitoring. In dairy cattle, the transition period (late gestation through early lactation) is characterized by major shifts in intake, endocrine regulation, and tissue mobilization, which can alter AA metabolism, nitrogen partitioning, and circulating metabolite profiles. Transition-period studies increasingly evaluate panels of “protein metabolism biomarkers” in association with body reserve dynamics and health outcomes, demonstrating that the same biomarker can have different baseline ranges and biological interpretations depending on stage of lactation and metabolic state (Heirbaut et al., 2023; Siachos et al., 2024). These findings reinforce the need to interpret blood-based indicators of protein utilization (e.g., urea nitrogen, AA profiles, metabolomic signatures) within a framework that accounts for physiological stage, energy balance, and production status, rather than applying universal cutoffs across all animals and time points (Bhardwaj et al., 2017; González-Montaña & Alonso, 2021).

10. Practical standards for using blood biomarkers to infer protein digestibility and nitrogen excretion

10.1. Define the objective clearly

The first methodological requirement is to define whether the primary goal is to infer protein digestibility/absorption or nitrogen surplus and excretion potential, because these endpoints are represented by different biomarker classes. If the objective is to evaluate digestion and absorption, priority should be given to biomarkers that capture the postprandial appearance of AA and peptides in blood, including plasma AA kinetics, AA AUC, and emerging peptide-derived AA indices. These digestion-proximal biomarkers are most informative when paired with conventional digestibility measurements (e.g., ileal or total tract estimates) to calibrate interpretation and distinguish absorption differences from post-absorptive metabolism (Chen, 2017; Lavery & Ferris, 2021). In contrast, if the objective is to evaluate nitrogen surplus and excretion risk, then plasma/serum/blood urea nitrogen (PUN/SUN/BUN) in mammals, or plasma uric acid in birds, should be prioritized as integrative indicators of nitrogen disposal and inefficiency in AA utilization (Fig. 4). Their interpretation is strengthened when paired with dietary CP level, AA balance, and energy supply, because nitrogen surplus reflects mismatches between nitrogen supply and anabolic capacity (Lavery & Ferris, 2021; Oliveira et al., 2022).

Fig. 4.

Decision framework for selecting and interpreting blood biomarkers to assess protein digestibility and nitrogen excretion in animals. The framework illustrates biomarker selection based on the primary nutritional objective. For studies focused on protein digestibility and absorption, recommended biomarkers include plasma amino acid profiles, postprandial amino acid kinetics, and peptide-derived amino acids. For studies focused on nitrogen surplus and excretion, recommended biomarkers include plasma/blood urea nitrogen, milk urea nitrogen, and plasma uric acid. Systems-level biomarkers derived from metabolomics may complement both approaches. Interpretation should consider physiological stage, feeding time, energy balance, renal function, and species-specific differences, and biomarkers should be validated against reference outcomes such as digestibility coefficients, nitrogen balance, urinary nitrogen excretion, and production performance. Abbreviations: AA, amino acid(s); Cmax, maximum plasma amino acid concentration; Tmax, time to maximum plasma amino acid concentration; AUC, area under the concentration-time curve. The figures were created and assembled using Microsoft PowerPoint, BioRender, and Adobe Photoshop.

10.2. Standardize sampling design

Standardization of sampling procedures is essential because most protein-related blood biomarkers show temporal variation. Sampling time relative to feeding must be consistent, particularly for postprandial AA kinetics and for interpreting urea responses, which can change with meal timing and post-absorptive nitrogen metabolism (Krogh et al., 2020; Millward, 2024). For AA kinetics studies, baseline (fasted) samples and multiple post-meal time points are required to capture complete concentration time curves and to compute kinetic parameters such as Cmax, Tmax, and AUC (Serrano-Rodríguez et al., 2025). Even when using simpler biomarkers such as BUN, repeated measures collected over multiple days can reduce noise from daily variation and improve the reliability of estimates in both research and field monitoring (Krogh et al., 2020; Lavery & Ferris, 2021).

10.3. Interpret biomarkers within physiological and health context

Blood biomarkers should always be interpreted in the context of physiological stage, health status, and excretory capacity, because these factors can shift baseline values independent of diet. For example, in dairy cattle, stage of lactation and transition-period metabolic adaptation influence nitrogen partitioning and circulating metabolite profiles, affecting interpretation of urea and broader “protein metabolism” biomarker panels (Pedlar et al., 2019; Zhao et al., 2025). Similarly, kidney function and hydration status directly affect urea or uric acid clearance, meaning that elevated blood urea cannot be assumed to represent dietary nitrogen surplus without considering renal context. In this regard, renal-linked markers such as creatinine or SDMA can strengthen interpretation of urea-based indicators by helping to distinguish dietary effects from clearance-related effects (Zhao et al., 2025).

For ruminants, interpretation requires additional consideration of rumen nitrogen dynamics. Synchronization between rumen-degradable protein and fermentable carbohydrates strongly influences microbial capture of ammonia, hepatic urea production, and the extent of urea recycling to the rumen. Therefore, the relationship between dietary CP and blood urea is not strictly linear, and urea recycling complicates direct inference of excretion without considering rumen fermentation conditions (Getahun et al., 2019; Zeleke et al., 2025).

10.4. Validate biomarkers against “ground truth” measures

Whenever possible, blood biomarkers should be calibrated against direct reference outcomes (“ground truth”), such as urinary nitrogen or urinary urea nitrogen, milk nitrogen output in dairy cattle, nitrogen balance trials, or digestibility coefficients measured at the ileal or total-tract level. Such validation is essential to ensure that biomarkers are truly predictive under the diet types, management systems, and physiological states of interest (Huhtanen et al., 2015; Schmid et al., 2024). Recent dairy nutrition studies provide strong examples of this approach by demonstrating that reducing dietary CP can simultaneously decrease PUN and urinary urea nitrogen excretion, illustrating how paired measurement improves confidence in using urea-related biomarkers as indicators of nitrogen surplus and excretion risk (Huhtanen et al., 2015) (Table 3). Similarly, in pigs, genomic and nutrition research linking BUN to NUE traits demonstrates the value of validating biomarker interpretation against performance and NUE-related outcomes across cohorts (Schmid et al., 2024).

Table 3.

Practical considerations, confounding factors, and validation strategies for using blood biomarkers to assess protein digestibility, utilization, and nitrogen excretion.

Biomarker/biomarker class	Major confounding factors	Sampling and methodological standards	What the biomarker can reliably infer	Recommended validation (“ground truth”)	References
(PUN/SUN/BUN)	Renal function, hydration, energy balance, physiological stage, rumen urea recycling (ruminants)	Consistent sampling time relative to feeding; repeated measures across days	Nitrogen surplus and excretion potential; NUE	Urinary urea nitrogen, total urinary nitrogen, nitrogen balance	(Bezerra et al., 2025; Costa et al., 2025; Zhang et al., 2025b)
MUN	Stage of lactation, milk yield, energy balance	Composite milk samples; standardized milking time	Proxy for blood urea and nitrogen surplus	Urinary nitrogen, milk nitrogen output	(Albarrán-Portillo et al., 2025; Mangwe et al., 2025)
Plasma uric acid (birds)	Renal function, hydration, feeding status, stress	Consistent feeding and sampling time	Nitrogen excretion efficiency	Excreta uric acid nitrogen, total nitrogen excretion	(Darsi et al., 2025; de Lima et al., 2025; Li et al., 2025c)
Plasma AA profiles	Feeding time, meal size, gut health	Fasted or standardized postprandial sampling	Relative AA availability and utilization	Ileal digestibility, protein source comparisons	(Banton, 2025; Cui et al., 2025)
Postprandial AA kinetics (Cmax, Tmax, AUC)	Sampling frequency, meal composition	Baseline + multiple post-meal samples	Digestion and absorption dynamics	Ileal digestibility, nitrogen balance	(Bonnici et al., 2025; Xiao et al., 2025)
Peptide-derived AA in plasma	Analytical method, hydrolysis protocol	Controlled feeding; standardized sample processing	Protein digestion and absorption	Digestibility trials, AA appearance	(Mohammadrezaei et al., 2025)
Metabolomics-derived biomarker panels	Physiological stage, health status, diet composition	Strict control of sampling time; standardized analytics	System-level protein utilization and NUE	Multi-endpoint validation (nitrogen balance, urea, performance)	(Jiang et al., 2025; Wang et al., 2025)
Creatinine / SDMA	Muscle mass, hydration, renal health	Routine clinical sampling	Renal clearance (contextual interpretation)	Paired with urea or uric acid	(Sawmy et al., 2025)

Note: Abbreviations: (AA) amino acid(s); (AUC) area under the concentration-time curve; (BUN) blood urea nitrogen; (Cmax) maximum plasma amino acid concentration; (MUN) milk urea nitrogen; (NUE) nitrogen use efficiency; (PUN) plasma urea nitrogen; (SDMA) symmetric dimethylarginine; (SUN) serum urea nitrogen; (Tmax) time to maximum plasma amino acid concentration.

11. Future perspectives

Future research should move beyond single-analyte biomarkers toward integrated biomarker panels that combine indicators of digestion, amino acid utilization, and nitrogen excretion. Advances in untargeted metabolomics, together with other omics technologies, offer considerable potential for identifying novel biomarkers and improving mechanistic understanding of protein utilization (Nunes et al., 2024; Wishart, 2019). Greater emphasis should also be placed on validating biomarkers across species, physiological stages, and production systems to ensure robustness and practical applicability.

The integration of blood biomarkers with precision feeding technologies, sensor-based monitoring systems, genetic selection programs, and AI-based predictive analytics may further enhance the ability to optimize dietary protein supply while minimizing environmental nitrogen losses (Ojo et al., 2024; Pedlar et al., 2019). Machine-learning algorithms may facilitate the identification of complex biomarker patterns and improve prediction of nutrient utilization outcomes from multidimensional datasets generated through metabolomics and other omics technologies (Choudhary, 2026; Choudhary et al., 2025). In addition, standardized sampling protocols and large-scale validation studies are needed to facilitate comparisons among studies and support routine implementation of biomarker-based approaches in animal nutrition research and practice.

12. Conclusions

Although substantial progress has been made in identifying blood-based biomarkers of protein digestion, utilization, and nitrogen excretion, several limitations remain. Many biomarkers are influenced by factors such as species, physiological stage, feeding status, energy balance, and sampling protocols, which may complicate interpretation and comparison across studies. In addition, emerging metabolomics-derived biomarkers require further validation across diverse animal species and production systems before routine application.

Blood-based biomarkers provide valuable tools for assessing protein utilization and nitrogen excretion in animals and can offer indirect insight into protein digestion and absorption when interpreted appropriately. Among currently available indicators, blood urea nitrogen in mammals and plasma uric acid in birds remain the most practical and robust biomarkers of nitrogen surplus and excretion potential, whereas plasma amino acid kinetics and emerging peptide-derived amino acid biomarkers provide information more closely related to digestion and absorption processes.

Overall, no single biomarker can fully capture the complexity of protein metabolism. A complementary panel-based approach integrating amino acid profiles, nitrogen-related biomarkers, and metabolomics-derived signatures is likely to provide the most comprehensive assessment of protein utilization, NUE, and nitrogen excretion risk in animal production systems.

Ethics, consent to participate, and consent to publish

This study did not involve human participants, animals, or identifiable personal data. Therefore, ethics approval, consent to participate, and consent to publish were not applicable.

Funding

This work was supported by the National Key R & D Program of China (NO. 2022YFE0113400).

CRediT authorship contribution statement

Shahrukh Khan: Writing – original draft, Writing – review & editing, Validation, Software, Formal analysis, Data curation. Shakal Khan Korai: Writing – review & editing, Writing – original draft, Software, Data curation, Formal analysis, Supervision, Conceptualization. Shengnan Li: Investigation, Formal analysis, Data curation, Writing – review & editing. Liting Yang: Investigation, Formal analysis, Writing – review & editing. Gayrat Mengliyev: Writing – review & editing. Xusniddin Donayev: Writing – review & editing. Leylabeyim Seyidova: Writing – review & editing. Mohd Asif Shah: Writing – review & editing, Funding acquisition. Xiaoshan Wang: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The current study did not report any new data.