Postprandial glycemia in dogs: A review of dietary determinants, mechanisms and evidence

Introduction

Postprandial glycemia refers to the concentration of glucose in the blood measured after a meal, conventionally at one to two hours after ingestion in human clinical practice (American Diabetes Association Professional Practice Committee, 2024) [2] . That convention does not transfer cleanly to the dog. Canine postprandial glucose typically reaches its zenith between two and four hours after ingestion, and the interval to peak varies with the composition of the meal (Rucinsky et al., 2010) [47] . A measurement protocol imported from human medicine without adjustment therefore risks sampling the canine curve at the wrong moment. This is not a pedantic observation; as will become clear, the sampling schedules adopted in the canine feeding trials reviewed here vary from four postprandial points to sixteen, and the sparser schedules cannot in principle resolve a peak whose timing is itself diet-dependent. The case for treating postprandial glycemia as an endpoint worth managing rests on several distinct arguments, which are worth separating. It reflects metabolic flexibility and the integrity of glucose regulation (Dörner et al., 2025) [13] . It functions as an early indicator of impaired glucose control, detectable before fasting values shift (Thomas & Elliott, 2009) [54] . Although, persistent elevation is associated with metabolic disorders in dogs (Nelson & Couto, 2019) [42] . It is strongly influenced by diet composition and feeding pattern. And, critically, it can be modulated through targeted nutritional strategies (Sidhu & Randhawa, 2019) [49] . The last of these is what distinguishes postprandial glycemia from most other International Journal of Advanced Biochemistry Research https://www.biochemjournal.com metabolic markers available to the small-animal clinician: it is not merely diagnostic but tractable, and tractable through a route the food bowl that the owner already controls. The clinical burden gives the question its weight. Diabetes mellitus in dogs has been estimated at a prevalence of approximately one in three hundred (Federation of European Companion Animal Veterinary Associations, 2025) [16] , and epidemiological work drawn from primary-care veterinary practice indicates a measurable and non-trivial prevalence, with risk rising in association with obesity, advancing age and breed predisposition (Mattin et al., 2014) [35] . Onset peaks at seven to nine years, the condition is more common in middle-aged female dogs, and spayed females carry a reported four to ten-fold increased risk (American Veterinary Medical Association, n.d.). Against this, a substantial majority of affected dogs reported in the source material as seventy to eighty percent maintain quality of life under appropriate feeding management and exercise ( [15,23,43] . The denominator for that figure is not specified in the material reviewed, and it should be read as indicative rather than precise.

Physiological Regulation of Postprandial Glycemia in the Dog

Postprandial glycemia is regulated through coordinated interaction between intestinal absorption, hormonal signalling and peripheral tissue metabolism. It is conventionally decomposed into four sequential phases, and the decomposition is useful because different dietary interventions act at different points in the sequence.

Intestinal phase: Digestion and absorption

Dietary starch is hydrolysed to glucose by amylase and by brush-border enzymes of the small intestine. The liberated glucose crosses the enterocyte by way of two transporters: SGLT-1, which mediates sodium-coupled uptake at the apical membrane, and GLUT-2, which handles glucose transport in both intestine and pancreas. Absorbed glucose then enters the portal circulation (Vastolo et al., 2023) [56] . This phase is where the rate-limiting properties of the food matrix exert themselves: anything that slows enzymatic access to starch granules, or slows the delivery of digesta to the absorptive surface, acts here.

Pancreatic phase: Insulin secretion

Rising portal and systemic glucose is sensed by the pancreatic β-cell, which takes up glucose through GLUT-2. Intracellular metabolism raises the ATP:ADP ratio, closing ATP-sensitive potassium channels; the resulting membrane depolarisation opens voltage-gated calcium channels, and calcium influx triggers exocytosis of insulin-containing granules into the circulation (Holst et al., 2016) [27] . The sequence is canonical mammalian physiology, and the sources reviewed here do not establish dog-specific quantitative parameters for any step in it.

Circulatory phase

Blood glucose concentration rises and insulin circulates alongside it. This is the phase that the glucose curve actually measures, and it is worth keeping in view that a postprandial glucose value is a net quantity the balance of appearance from the gut and disappearance into tissue rather than a direct readout of either process.

Peripheral phase: Utilisation and storage

In muscle and adipose tissue, insulin signalling drives translocation of GLUT-4 to the plasma membrane and consequent glucose uptake, feeding glycolysis for energy production and, in adipose tissue, lipogenesis. In the liver, glycogenesis is upregulated and gluconeogenesis suppressed (Nelson & Couto, 2019) [42] . The three metabolic pathways engaged glycolysis, glycogenesis, and inhibition of gluconeogenesis together determine how rapidly the circulating glucose load is cleared, and therefore the descending limb of the curve. [10,26] . Higher starch intake raises postprandial glucose concentration; lower digestible carbohydrate lowers the glycaemic response (Hall et al., 2013) [24] . Quantity is only half the account. [56] . Meynier et al. (2015) [38,51] report that SDS, fat, fibre and RDS collectively explain approximately fiftythree per cent of the variability in glycaemic response, with food-matrix interactions exerting significant influence. That figure deserves more attention than it usually receives. If the four compositional variables routinely managed in formulation account for slightly over half the variance, then nearly half of the variability in glycaemic response is attributable to something else matrix architecture, particle size, processing history, individual animal variation, or measurement error. A formulation strategy built exclusively on the four measured variables is therefore working with an incomplete model, and the residual should temper confidence in predicting the glycaemic behaviour of a novel diet from its proximate analysis alone. [17] ; Foster-Powell et al. (2002) [18] ; Atkinson et al. (2021) [5] .

Protein

Protein influences the glucose response through three mechanisms operating in different phases. Amino acids stimulate insulin secretion, which reduces the glucose excursion; protein slows gastric emptying, which delays glucose absorption; and it enhances peripheral glucose uptake, improving clearance. When protein is co-ingested with carbohydrate, the composite glycaemic response is modified relative to carbohydrate alone, with improved post-meal glucose regulation (Banton et al., 2025; Holst et al., 2016) [7,27] . Protein is thus unusual among the macronutrients in acting on both the appearance and the disposal side of the balance simultaneously.

Fat

Fat slows gastric emptying, delaying delivery of nutrients to the small intestine and reducing the rate at which glucose appears in the circulation; the result is a delayed and blunted glycaemic response (Gentilcore et al., 2006) [21] . The acute effect is therefore favourable. The chronic effect is not: sustained high fat intake is associated with increased risk of insulin resistance. Fat consequently sits awkwardly in any glycaemic management strategy, because the same nutrient that improves the shape of today's postprandial curve may, at sustained high inclusion, degrade the insulin sensitivity on which tomorrow's curve depends. The evidence base compounds the difficulty: Gentilcore et al. (2006) [21] studied human subjects with type 2 diabetes, and the magnitude of the acute effect in dogs is not established by the sources reviewed here.

Dietary fibre

Fibre alters ingesta viscosity, modulates gastrointestinal motility, and influences nutrient digestibility and absorption. Five mechanisms are described in the material reviewed. Soluble fibre forms a gel in the intestine that slows glucose diffusion and delays carbohydrate digestion and absorption (Thorne et al., 2020) [55] . Increased viscosity reduces enzyme-substrate interaction and thereby the rate of glucose appearance in blood (Meynier et al., 2015) [38] . Delayed gastric emptying slows nutrient delivery to the intestine and blunts the postprandial peak. Fermentation yields shortchain fatty acids, principally acetate and propionate, which enhance peripheral glucose utilisation and improve insulin sensitivity (de Godoy et al., 2013) [12] . And fibre modulates the gut microbiota, promoting beneficial populations and their production of metabolites that regulate glucose metabolism (Moreno et al., 2022) [40] . The five mechanisms are not equally well evidenced in the dog, and they are not additive in any simple way. The first three are physical and operate acutely within a single meal; the last two are microbial and metabolic, operate over days to weeks, and depend on fermentability rather than viscosity. A diet formulated for acute curve-flattening and one formulated for chronic insulin sensitisation would therefore select different fibre sources, and conflating the two objectives under the single heading of "fibre" obscures a real formulation decision.

Dietary Strategies 4.1 Carbohydrate-based strategies

Six carbohydrate-directed strategies emerge from the mechanistic account. The first is ingredient selection: preferring low-glycaemic sources such as barley, oats and legumes, and limiting maize, rice and wheat flour, which are high-glycaemic when processed (Atkinson et al., 2021) [5] . The second is deliberate incorporation of slowly digestible starch from pulses and whole grains to promote gradual glucose release (Meynier et al., 2015) [38] . The third is limitation of rapidly digestible starch by avoiding highly processed and gelatinised starch and controlling the inclusion of refined cereals (Augustin et al., 2015) [6] . The fourth is optimisation of processing: coarse grinding in preference to fine, and avoidance of excessive extrusion and gelatinisation, since processing raises starch digestibility and with it the glycaemic response (Singh et al., 2010) [51] . The fifth is nutrient balancing combining carbohydrate with fibre, fat and protein to slow absorption and improve the insulin response (Gannon & Nuttall, 2010) [20] . The sixth is control of total carbohydrate inclusion, avoiding excessive carbohydrate density and adjusting to metabolic status (Elliott et al., 2012) [14] . These are formulation heuristics derived from mechanism, and it is worth being explicit that most of them have not been tested head-to-head in dogs. Of the six, only the first two receive reasonably direct support from the canine trials examined in Section 6, and the fourth, the processing recommendation rests substantially on food-science work conducted outside the species.

Fibre-based strategies

Five fibre-directed strategies are described. Soluble and viscous fibres such as psyllium, β-glucans from oats and barley and pectins are identified as most effective for glycaemic control (Jenkins et al., 2002) [29] . Moderately fermentable fibres such as beet pulp, inulin and fructooligosaccharides support metabolic regulation (Swanson et al., 2002) [53] . Fibre types should be balanced, combining soluble fibre for glycaemic control with insoluble fibre for gut motility (Reynolds et al., 2019; de Godoy et al., 2013) [20, 45] . Inclusion level should be optimised, since moderate levels give the best glycaemic response while excess fibre depresses nutrient digestibility (de Godoy et al., 2013) [12] . Finally, functional additives including prebiotics and resistant starch may be used to engage the gut-metabolic axis (Roberfroid et al., 2010) [46] . The fourth of these that the dose-response is non-monotonic, with an optimum rather than a ceiling is the practically important one, and it is also the least well quantified. No inclusion level is specified in the material reviewed, and the trade-off point between glycaemic benefit and digestibility loss will depend on fibre source, the rest of the diet, and the metabolic status of the animal.

Nutraceutical Interventions

Nutraceutical candidates are usefully organised by anatomical site of action, since this determines both the expected mechanism and the kind of evidence that would be required to demonstrate efficacy. Table 2 summarises the compounds described, grouped into intestinal modulators, agents acting on pancreatic insulin secretion, systemic agents acting on hepatic and whole-body metabolism, and peripheral agents acting on muscle and tissue uptake. [57] Basil Flavonoids Amylase inhibition; ↑ insulin ↓ glucose (~31%) Abdelrahman et al.

(2020) [1] Jamun Jamboline Delays starch digestion ↓ PPG Kochhar et al. (2005) [32]

Fenugreek Galactomannan Gel formation slows absorption ↓ glycemia Singletary (2017) [51]

Gymnema

Gymnemic acids ↓ intestinal glucose absorption ↓ glucose Mayyas et al. (2025) [36] Dietary fibre β-Glucan, pectin ↑ viscosity, ↓ absorption ↓ PPG spike de Godoy et al. (2013) [12] FOS Fructans SCFA production Indirect ↓ Makki et al. (2018) [34]

Flaxseed

Lignans, fibre Delays glucose absorption ↓ spikes Moreira et al. (2022) [39] Pancreatic (insulin response) Fenugreek 4-Hydroxyisoleucine ↑ insulin secretion ↓ glycemia Singletary (2017) [51] Basil Eugenol, flavonoids ↑ insulin levels ↓ glucose Abdelrahman et al.

Chromium

Trace mineral Enhances insulin signalling ↑ glucose tolerance Gupta et al. (2019) [23]

Zinc

Trace mineral Insulin storage and secretion Indirect ↓ Jansen et al. (2009) [28] Systemic (liver and whole-body metabolism) Cinnamon Procyanidins Improves insulin sensitivity Mild ↓ Gupta et al. (2019) [23] Turmeric Curcumin Anti-inflammatory, insulin sensitiser Indirect ↓ Suemanotham et al.

(2022) [52] Rosemary Rosmarinic and carnosic acid Antioxidant, metabolic regulation ↓ glucose Abdelrahman et al.

(2020) [1] Polyphenols (general) Flavonoids, tannins Multi-target metabolic effects ↓ glycemia Ruiz-Cano & Arnao (2024) [48] Peripheral (muscle and tissue uptake) Berberine Isoquinoline alkaloid AMPK activation ↑ glucose uptake Freitas et al. (2025) [19] Green tea Catechins ↑ glucose uptake Mild ↓ Li et al. (2020) [33] Magnesium Enzyme cofactor Improves insulin sensitivity Indirect ↓ Barbagallo & Dominguez (2015) [8] Vanadium Trace element Insulin-mimetic action ↓ glucose Kim et al. (2006) [31] PPG = postprandial glycemia; FOS = fructo-oligosaccharides; SCFA = short-chain fatty acids.

The table conceals a steep gradient in evidence quality, and reading across it uncritically would be a mistake. Only a minority of the entries rest on canine feeding studies. Abdelrahman et al. (2020) [1] worked in Rottweilers, Li et al. (2020) [33] in dogs with diet-induced obesity, Suemanotham et al. (2022) [52] in dogs with diabetes mellitus, and Wang et al. (2025) [57] in beagles. Kim et al. (2006) [31] studied dogs, but using alloxan-induced diabetes, a chemical model whose relationship to spontaneous canine diabetes is not straightforward. Moreira et al. (2022) [39] is a human crossover trial in type 2 diabetes. Mayyas et al. (2025) [36] is a computational and network-pharmacology study with no animal feeding component at all. Makki et al. (2018) [34] and Barbagallo and Dominguez (2015) [8] are reviews of general mechanism rather than canine trials. A compound's presence in Table 2 therefore indicates that a plausible mechanism has been proposed, not that efficacy has been demonstrated in the target species. The moringa data illustrate why the distinction matters quantitatively as well as categorically. Wang et al. (2025) [57] report that moringa leaf peptide inhibited α-amylase with a half-maximal inhibitory concentration of 2.29±0.10 mg/mL and α-glucosidase at 2.80±0.04 mg/mL, against acarbose values of 0.06 mg/mL and 1.02 × 10⁻³ mg/mL respectively. Expressed as a ratio, the peptide is roughly thirty-eight-fold less potent than acarbose against α-amylase and something on the order of three orders of magnitude less potent against α-glucosidase. Describing the two as showing "similar inhibition" with the pharmaceutical merely having "higher potency" understates the gap considerably. The in vivo effect that was reported at three per cent dietary inclusion is therefore unlikely to be attributable to pharmacological enzyme blockade at concentrations achievable in the gut lumen, and some other mechanism matrix effects, viscosity, or displacement of digestible starch should be considered.

Experimental Evidence

Four studies form the empirical core of this review. They differ in design, in the dietary contrast tested, and importantly in the density of postprandial sampling, which bears directly on what each can and cannot establish.

Grain-free versus cereal-based diets

Vastolo et al. (2023) [56] used a crossover design with repeated measures in fifteen healthy dogs, feeding each of three diets for fifty days and sampling blood at baseline and at 120, 240 and 360 minutes post-feeding for glycemia and insulin. The three diets were closely matched in energy are 3990, 3990 and 3997 kcal/kg metabolisable energy and more importantly, in starch content: 25 per cent in the first grain-free diet (sweet potato), 28 per cent in the second grain-free diet (pea starch), and 27.9 per cent in the cerealbased diet (oats and spelt). The protein and fat backbone was essentially identical across all three, based on boneless chicken, dehydrated chicken protein, chicken fat, dried eggs, herring and herring protein, fish oil and dried carrot. The first grain-free diet produced the lowest glucose area under the curve and the lowest glucose nadir, together with a low insulin area under the curve and the longest time to peak insulin. The second grain-free diet produced the strongest insulin response, with the highest insulin peak. The cereal-based diet produced the highest mean glucose and the highest glucose area under the curve, alongside high insulin values and the lowest insulin nadir; it represented the poorest glycaemic control of the three. Because starch content was held nearly constant, the differences recovered are attributable to starch source and structure rather than to starch quantity. This is the study's most valuable feature and it cuts against the framing under which it is often cited. The second grain-free diet was also grain-free, and it produced the strongest insulin response of the three; grain-free status per se therefore did not confer glycaemic advantage. What distinguished the bestperforming diet was its carbohydrate source sweet potato, not the absence of cereal. Formulators and clinicians reading the "grain-free versus cereal-based" headline should note that the operative variable was botanical origin and starch architecture. The design has one significant limitation. Postprandial sampling at 120-minute intervals cannot resolve a peak that the same literature places somewhere between two and four hours, and whose timing shifts with diet composition. The true zenith on any of the three diets may have fallen between sampling points, and the reported peak values are consequently lower bounds. The fifty-day feeding period, by contrast, is the longest of the four studies and offers reasonable confidence that adaptation had occurred.

High-protein, low-carbohydrate diets

Banton et al. (2025) [7] employed a Latin square design in nine adult dogs fed three diets over forty-two days: a highprotein, low-carbohydrate diet (52.1 per cent crude protein, 19 per cent crude fat, 10.7 per cent nitrogen-free extract), a moderate-protein, moderate-carbohydrate diet (28.7 per cent crude protein, 14.5 per cent fat, 40 per cent nitrogen-free extract), and a high-fibre metabolic diet (27.2 per cent crude protein, 12.5 per cent fat, 10.2 per cent crude fibre, 16 per cent total dietary fibre, 34.2 per cent nitrogen-free extract). Postprandial glucose, insulin, glucagon and amino acids were measured. The way these results are usually summarised does not survive contact with the P-values. It is commonly stated that the high-protein, low-carbohydrate diet delivered the best glycaemic control and the moderate-protein diet the poorest. Directionally the glucose data point that way the incremental area under the curve on the high-protein diet was roughly forty per cent lower than on the moderateprotein diet but neither the incremental nor the net glucose area reached conventional significance, at P = 0.103 and P = 0.057 respectively. Insulin showed no differences whatever (P = 0.82 and P = 0.844), and neither did HOMA-IR (P = 0.502 and P = 0.521). The only endpoints attaining significance were glucagon, which was roughly three-fold higher on the high-protein diet (p < 0.001), and the net glucose-to-insulin ratio (P = 0.039). The defensible reading is therefore narrower than the usual one. The robust finding is a large glucagon response to the high-protein diet, which is what one would expect from a substantial amino-acid load and is not in itself evidence of improved glycaemic control. The glucose trend is suggestive and, with nine animals, the study is plainly underpowered to detect effects of the size observed; a difference of that magnitude might well reach significance in a larger trial. But a review should report a non-significant trend as a nonsignificant trend rather than converting it into a conclusion. There is also an internal inconsistency in the reported fibre fractions worth flagging: for all three diets, insoluble fibre exceeds total dietary fibre (10.9 against 7.0; 8.4 against 7.9; 16.5 against 16.0), which cannot be correct as stated and suggests a transcription or units error somewhere in the chain. Those values should be checked against the primary publication before being used.

Pulse-based high-fibre diets

Quilliam et al. (2022) [44] used a randomised crossover design in eight adult beagles, feeding each of six diets for seven days with a three-day washout between periods. Blood was collected after an overnight fast and then at sixteen postprandial points were 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 and 300 minutes making this by a wide margin the best-resolved glucose curve among the four studies. The diets contrasted a rice-based control against lentil, smooth pea, fava bean and two wrinkled pea formulations. The rice diet produced the highest peak blood glucose, at approximately 5.0 mmol/L, and the highest glycaemic response by area under the curve, at approximately 810 mmol/L•min. The lentil diet produced the lowest peak, approximately 4.4 mmol/L, and the lowest area under the curve, approximately 726 mmol/L•min. Fasting blood glucose was lowest on the lentil diet at 3.5±0.1 mmol/L and on the wrinkled pea diet at 3.6±0.1 mmol/L. Two qualifications apply. The first is confounding: the six diets did not differ only in carbohydrate source. Flour inclusion ranged from 23.12 per cent in the rice diet to 58.65 per cent in one wrinkled pea diet, with chicken by-product meal falling correspondingly from 37.83 per cent to 8.85 per cent. The comparison therefore contrasts whole formulations differing simultaneously in carbohydrate source, carbohydrate load and protein source, and the pulse effect cannot be cleanly isolated from the accompanying reduction in animal protein. The second qualification concerns magnitude. The difference between the best and worst diets amounts to roughly 0.6 mmol/L in peak glucose and approximately ten per cent in area under the curve, in healthy beagles whose glucose remained within a narrow physiological band throughout. Whether an effect of this size is clinically meaningful in a dysglycemic animal is a separate question that the study, conducted in healthy dogs, cannot address.

Moringa oleifera leaf peptide

Wang et al. (2025) [57] combined in vitro enzyme inhibition and digestion work with an in vivo feeding trial. In vitro, moringa leaf peptide showed dose-dependent inhibition of α-amylase, reaching 90.98 per cent at 40 mg/mL, and of αglucosidase, reaching 98.44 per cent at 20 mg/mL, with the half-maximal inhibitory concentrations already discussed in Section 5. In simulated digestion, starch hydrolysis at 180 minutes was 79.58 per cent for the control snack against 65.82 per cent for the peptide-containing snack, and the estimated glycaemic index was significantly lower in the peptide group (p < 0.01). The in vivo component used sixteen beagles allocated to a control group (n = 8) and a peptide group (n = 8) fed snacks with three per cent inclusion, with blood glucose measured at 0, 15, 30, 60, 90, 120 and 180 minutes. Blood glucose was significantly lower at 30 and 60 minutes (p < 0.01), with a reduced peak, a longer time to peak indicating slower glucose absorption, reduced total and incremental area under the curve, and a significantly lower glycaemic index (p < 0.01). The in vivo design is the weakest of the four in one specific respect: it is a parallel-group comparison rather than a crossover, so between-animal variation in glucose handling is uncontrolled, and with eight dogs per arm the design has limited capacity to absorb it. The test matrix is also carbohydrate-dense rice flour at 27.93 per cent, corn flour at 9.31 per cent and potato starch at 8.38 per cent in the control snack, so what is being demonstrated is attenuation of a high-starch challenge rather than performance within a diet already formulated for glycaemic control. Finally, incorporating the peptide at three per cent displaced a proportional quantity of every other ingredient, so the two snacks are not strictly iso-nutrient; the observed effect includes a small reduction in digestible starch alongside whatever the peptide itself contributed.

Conclusions

Postprandial glycemia in the dog is diet-dependent and dynamic and it is modifiable by nutritional means. Carbohydrate is the central lever, but the useful formulation of that claim is more specific than the usual one: the source, structure and processing history of dietary starch appear to matter at least as much as its quantity and the single bestsupported strategy to emerge from this literature is substitution of rapidly digestible refined-cereal starch with slowly digestible starch from pulses or minimally processed whole grains. Protein, fat and dietary fibre modulate the response through mechanisms that are well described qualitatively and poorly quantified in this species. Nutraceutical interventions are mechanistically plausible and, with a small number of exceptions, not yet demonstrated in dogs at feeding-relevant doses.

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