The need for such an approach arises because animals sharing a diagnosis or receiving the same recommendation may respond differently. A dog enrolled in a weight-management program, for example, may lose weight at a different rate from another dog given a controlled diet. Such variation can reflect genetics, age, body composition, activity, health status, feeding behavior, gut microbes and owner adherence. Population-level nutrient recommendations remain necessary for formulating complete and balanced diets, but they cannot capture every difference in energy requirements, nutrient use, metabolic phenotype or dietary tolerance. Precision nutrition is therefore presented as an extension of clinical nutrition, not a new diet category, and its defining feature is a measurable feedback loop linking initial characterization, intervention and subsequent reassessment.

Clinical phenotyping remains the core of that loop. Body weight, body-condition and muscle-condition scores, fecal quality, urinary signs, disease history and previous dietary responses are established, repeatable measures that veterinarians can connect directly to feeding decisions. A detailed dietary history adds information about food composition, portion size, moisture, treats, water intake, feeding frequency and owner adherence, although recall and record accuracy can limit precision. Laboratory data can characterize metabolic and organ status, but their value depends on the clinical question: serum phosphate, for example, directly informs dietary management in chronic kidney disease. Host characteristics such as species, breed, body size, life stage, reproductive status and activity add biological context, yet none is sufficient on its own to prescribe a diet.

Emerging technologies broaden the possible evidence base but face substantial barriers to routine use. Genomes, transcriptomes, proteomes, metabolomes, lipidomes and microbiome profiles can illuminate appetite regulation, inherited risk, host–microbiome interactions and diet-responsive metabolic changes. However, most molecular candidates have not been prospectively validated as predictors of dietary response. Microbiome results are especially sensitive to sampling method, recent diet, medication exposure, sequencing platform, bioinformatic pipeline and reference database, making a dysbiosis index or microbial signature unsuitable as a stand-alone prescription. Likewise, machine-learning models face small samples, heterogeneous study designs, limited longitudinal data and inadequate external validation. Smart feeders, connected scales and activity monitors may improve home monitoring, but device accuracy, data completeness and owner engagement determine whether their signals are dependable.

Obesity demonstrates how established measures can drive a response-guided cycle. Weight-management plans begin with controlled energy restriction, accurate accounting for treats, increased physical activity where appropriate and long-term follow-up. Body weight, body condition, muscle condition, food intake, activity and the weight-loss trajectory then determine whether the current allowance or satiety strategy should change. High-protein and high-fiber formulations have reduced voluntary food intake and improved satiety in dogs, while fermentable fibers may support satiety and microbial fermentation. Genetic findings involving POMC, MC4R and DENND1B indicate variation in appetite and energy-homeostasis pathways, but genotype-specific dietary effects have not been demonstrated. Probiotic and targeted microbial strategies remain investigational, including findings from mouse models that have not established efficacy in obese dogs or cats.

Chronic enteropathy shows why response classification can be more informative than a single diet label. In clinically stable dogs with chronic inflammatory enteropathy, dietary treatment trials are recommended first, and reported food responsiveness ranges from 38% to 89%. Hydrolyzed or novel-protein diets are considered when adverse reactions to dietary proteins are suspected; fiber-enriched diets are more relevant to large-bowel or fiber-responsive phenotypes; and low-fat diets may suit selected dogs with protein-losing enteropathy or intestinal lymphangiectasia. During a trial, stool quality, appetite, body weight, adherence and disease activity—using the CIBDAI or CCECAI in dogs—determine whether the intervention is working. Microbial dysbiosis, bile acids, short-chain fatty acids and candidate barrier markers may illuminate mechanisms or change during remission, but they remain supportive rather than prescriptive.

Kidney disease offers a similarly direct example through biochemical feedback. As renal function declines, phosphate regulation becomes increasingly difficult, and dietary phosphorus restriction is adjusted according to serum phosphate, concurrent calcium and CKD stage. Cats that develop ionized hypercalcemia during marked restriction can have that restriction attenuated while phosphorus control is maintained, demonstrating how a measured biochemical response can determine the next dietary step. Protein and energy intake should likewise be individualized to preserve appetite, body weight and lean mass. Experimental observations involving phosphorus source, calcium-to-phosphorus ratio, microbial metabolites and multi-omics responses identify plausible ways to characterize heterogeneity, but these findings do not yet replace stage-based recommendations or direct clinical monitoring.

Urolithiasis illustrates why one “urinary diet” cannot fit every urinary problem. Management depends on stone composition, urine pH, urine specific gravity, culture results, relative supersaturation where available, imaging and recurrence history. Sterile struvite stones in cats may be dissolved with magnesium-restricted, moderately acidifying diets that promote urine dilution, whereas infection-associated struvite requires control of the causative bacteria because diet alone is unlikely to dissolve the stones. Calcium oxalate stones cannot be dissolved nutritionally and are managed mainly through recurrence prevention, including increased urine volume and avoidance of excessive acidification. High-moisture feeding can increase feline urine volume and lower specific gravity and relative supersaturation for calcium oxalate and struvite. Microbiome and metabolomic studies may eventually refine risk assessment, but microbial interventions targeting crystal formation remain experimental.

Translation into practice will depend on evidence that new tools add value beyond conventional assessment. Diagnostic biomarkers classify disease or phenotype, predictive biomarkers estimate who is likely to respond, and monitoring biomarkers track change, relapse and the need for adjustment. Companion-animal research is closest to implementation through monitoring measures rather than molecular prediction: weight and condition scores for obesity, clinical activity and fecal outcomes for canine enteropathy, and urinary chemistry and stone characteristics for urolithiasis. Future studies will require standardized collections of host, dietary, clinical, sampling and longitudinal-response data, as well as controlled feeding trials, larger cohorts, independent validation and interpretable models. Canine and feline organoids may help screen mechanisms and prioritize candidates, but they cannot replace in-vivo dietary trials. In the near term, smart feeders and activity monitors may add useful longitudinal data, while multi-omics and AI remain decision-support technologies awaiting prospective proof of clinical benefit.

Subject of Research: Response-guided precision nutrition for dietary management in dogs and cats

Article Title: Precision nutrition in companion animals: a response-based framework linking dietary strategies, metabolic function, and measurable clinical outcomes

Article References: Du, X., Zhang, H., Tian, W., Chen, J., Wen, M., & Fan, W. (2026). Precision nutrition in companion animals: a response-based framework linking dietary strategies, metabolic function, and measurable clinical outcomes. Frontiers in Veterinary Science, 13, Article 1914947. https://doi.org/10.3389/fvets.2026.1914947

Image Credits: AI Generated

DOI: 10.3389/fvets.2026.1914947

Keywords: precision nutrition, dogs, cats, dietary response, clinical nutrition, gut microbiome, metabolomics, chronic kidney disease, urolithiasis, obesity, artificial intelligence, Precision