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Empirical prediction of net portal appearance of volatile fatty acids, glucose, and their secondary metabolites (β-hydroxybutyrate, lactate) from dietary characteristics in ruminants: A meta-analysis approach¹

C. Loncke^*, I. Ortigues-Marty^*, J. Vernet^*, H. Lapierre, D. Sauvant and P. Nozière^*,2

^* Institut National de la Recherche Agronomique, UR 1213, Unité de Recherches sur les Herbivores, Theix, 63122 St Genès Champanelle, France and Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, Quebec JIMIZ3, Canada UMR 791, Physiologie de la Nutrition et Alimentation, Institut National de la Recherche Agronomique-AgroParisTech, 75231 Paris, France

	Abstract

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
The current trend in energy feeding systems for ruminants toward a nutrient-based system requires dietary energy supply to be determined in terms of amount and nature of absorbed energy-yielding nutrients. The objective of this study was to establish response equations on the net portal appearance (NPA) of VFA and glucose, and their secondary metabolites β-hydroxybutyrate (BHBA) and lactate, to changes in intake level and chemical dietary characteristics based on the Institut National de la Recherche Agronomique Feed Evaluation System for Ruminants. Meta-analyses were applied on published data compiled from the FLORA database, which pools the results on net splanchnic nutrient fluxes in multi-catheterized ruminants from international publications. For each nutrient, several prediction variables were tested. We obtained robust models for intakes up to 30 g of DM · d^–1 · kg of BW^–1 and diets containing less than 70 g of concentrate per 100 g of DM. These models were designed to predict the NPA (mmol · h^–1 · kg of BW^–1) of total VFA based on the amount of ruminally fermented OM (RfOM) intake [adjusted R² (R²_adj) = 0.95; residual means square errors (RMSE) = 0.24], to predict VFA profile (mol/100 mol of total VFA) based on type of RfOM intake (acetate: R²_adj = 0.85, RMSE = 2.2; propionate: R²_adj = 0.76, RMSE = 2.2; butyrate: R²_adj = 0.76, RMSE = 1.09), and to predict the NPA (mmol · h^–1 · kg of BW^–1) of glucose based on the starch digested in the small intestine independent of ruminant species, and while presenting no interfering factors on the residuals and individual slopes. The model predicting the NPA (mmol · h^–1 · kg of BW^–1) of BHBA based on the amount of RfOM intake (R²_adj = 0.91; RMSE = 0.036) was species-dependent, and the model predicting NPA (mmol · h^–1 · kg of BW^–1) of lactate based on starch digested in the rumen (R²_adj = 0.77; RMSE = 0.042) presented a wide dispersion. However, the NPA (mmol · h^–1 · kg of BW^–1) of BHBA was related to the NPA of both butyrate (R²_adj = 0.85; RMSE = 0.054) and acetate (R²_adj = 0.85; RMSE = 0.052), and the NPA (mmol · h^–1 · kg of BW ^–1) of lactate was related to the NPA of propionate (R²_adj = 0.51; RMSE = 0.096). This research showed that it is possible to accurately predict the amount and nature of absorbed nutrient fluxes based on dietary characteristics in both sheep and cattle. This work aims to quantify the consequences of digestion and portal-drained viscera metabolism on nutrient availability. These results can provide deeper insight into biological processes and help develop improved tools for dietary formulation.

Key Words: diet composition • energy • intake level • meta-analyses • portal-drained viscera • ruminant

	INTRODUCTION

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
The feeding systems developed for ruminants were designed to adjust feed allowances to fit the production potential of the animals (Vermorel and Coulon, 1998). Major progress has been made in feed characterization for predicting ME intake, but energy is still evaluated as an aggregated unit. The new challenges of ruminant nutrition require that feed evaluation systems evolve toward more precise and mechanistic nutrient-based systems (Reynolds, 2000).

Improvements to diet formulation depend on our capacity to predict the amount and the nature of the nutrients absorbed by the portal-drained viscera (PDV; Dijkstra et al., 2007). Bermingham et al. (2008) used meta-analyses to quantify the incremental responses of the net portal appearance (NPA) of energy-yielding nutrients to increases in DMI and digestible OM (dOM) intake at fixed diet composition, but the prediction of nutrient absorption after a change in diet composition was not addressed.

The objectives of the present work were to compare several descriptors of ruminant diets for their ability to predict the NPA responses of nutrients derived from digestion (i.e., VFA and glucose) or from gut metabolism [i.e., β-hydroxybutyrate (BHBA) and lactate]. The response equations to changes in both intake and diet composition were established by meta-analyses run on published results using the best descriptors. It was assumed that the relevant predictors differed depending on the nature of the energy substrate, its digestibility, and site of digestion. The newly revised Institut National de la Recherche Agronomique (INRA) Feed Tables (INRA, 2007) were used for detailed feed and diet descriptions. They include all recent advances in feed characterization for a wide range of forages and concentrates.

	MATERIALS AND METHODS

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Animal Care and Use Committee approval was not obtained for this study because the data were obtained from an existing database (FLORA; Vernet and Ortigues-Marty, 2006).

Selection of Publications from the FLORA Database

The FLORA database was used. It is an exhaustive base from approximately 200 international publications reporting results of net splanchnic nutrient fluxes in multi-catheterized ruminants. The Scopus and Current Contents Connect online bibliographic databases were queried. The structure of FLORA has been described in detail by Vernet and Ortigues-Marty (2006). Animals were identified according to species (sheep, cattle) and physiological status (nonproductive adult, growing, gestating, or lactating animals). Chemical composition and calculated nutritional value of feeds and diets, plus catheter location, blood sampling, metabolite analyses methods, and results for blood or plasma flows, metabolite concentrations, and fluxes were entered into the FLORA database after systematic verification steps.

In the present study, only a limited number of publications reported results for each target nutrient after a dietary treatment. There were 29, 55, 38, and 29 publications for total VFA, glucose, lactate, and BHBA, respectively. These publications were selected as being relevant to studying the response of nutrient NPA to changes in dietary intake and composition.

Description and Calculation of the Nutritional Values of Diets

Publications featuring net splanchnic nutrient fluxes in ruminants do not systematically report the chemical composition and nutritional value of feeds and diets, making it necessary to produce a consistent description of the chemical composition and nutritional values of the feeds and diets used in all relevant published reports. We chose to describe all feeds according to the INRA Feed Tables (INRA, 2007), which give detailed chemical composition and nutritional values for about 160 concentrates and 1,260 forages. After selection of the most representative feed ingredients, the estimated chemical composition and nutritional values of the diets were calculated assuming additivity. The estimated values were validated by comparison with the values reported by the authors. Comparisons focused primarily on OM digestibility, ME and CP concentrations, and subsequently on crude fiber concentrations in DM, which were reported in 18, 45, 60, and 15% of the selected publications, respectively. If a publication did not report at least one of these criteria, it was excluded from the models. Differences between the estimated values and those reported by the authors were analyzed by GLM analyses including a publication fixed effect. If the difference was greater than the measurement uncertainties for OM digestibility (±2.5 g/100 g), ME concentration (±1.0 MJ/kg or DM), or CP concentration (±12.5 g/kg of DM), then the publication was excluded from the models. If the difference was greater than the measurement uncertainty for crude fiber concentration (±17.5 g/kg of DM), then the publication was only excluded from the models if it was subsequently identified as an outlier.

Choice of Explanatory Variables to Predict Variations in Nutrient NPA

Considering the importance of digestibility in the nutritive value of ruminant diets, dOM intake level was considered as a predictor of the variations in NPA for all studied nutrients (VFA, BHBA, glucose, and L-lactate). The proportion of concentrate in the diet (DM basis), albeit partly linked to dOM intake, was also factored in. Other potential predictors were identified for each individual nutrient, based on current knowledge of digestion and PDV metabolism, as described below.

The first criterion for predicting the NPA of total VFA [i.e., acetate (C2) plus propionate (C3) plus butyrate (C4)] was the amount of ruminally fermentable OM (RfOM) intake. Ruminally digestible NDF (RdNDF) intake and ruminally digestible starch (RdS) intake were also tested as predictors of total VFA NPA. Variations in molar proportions of individual VFA and the C2:C3 ratio in the net portal flux were predicted from the dietary concentration of RdNDF and RdS, with their respective contribution to RfOM (RdNDF:RfOM, RdS:RfOM) used as indexes of the type of fermentable energy because of the absence of link between the molar percentage of VFA and DMI (Bermingham et al., 2008). Soluble sugars in feeds could not be taken into account, because they are highly variable and insufficiently reported (INRA, 2007). Ruminally fermentable OM concentrations were calculated according to INRA (2007; i.e., RfOM = dOM – fat – undegradable CP – undegradable starch – fermentation products, where undegradable CP and undegradable starch were estimated by the in sacco method). Ruminally digested starch values were calculated from starch concentrations and in sacco degradability (INRA, 2007) and the empirical model of ruminal starch digestion (Offner and Sauvant, 2004). This model takes into account the differences between in sacco and in vivo ruminal degradation of starch and the effects of intake level. For RdNDF, we assumed that 90% of digestible NDF was digested in the rumen, and thus we calculated RdNDF as digestible NDF x 0.90. Because the INRA Feed Tables (INRA, 2007) report the NDF digestibility for forages only, the digestible NDF of concentrates was estimated as: digestible NDF = [NDF – (undigestible OM – undigestible CP – undigestible fat – undigestible starch)]. Values for undigestible OM, undigestible CP, undigestible fat, and undigestible starch were based on the INRA Feed System (INRA, 2007), assuming a constant fat digestibility of 80% (Doreau and Ferlay, 1994). These calculations were validated by comparing estimated with measured data (Sauvant et al., 2008a). Concentrates accounted for less than 20% of dietary digestible NDF. Dietary digestible NDF was then calculated by additivity.

Variations in BHBA NPA were predicted from the same explanatory variables as for VFA (i.e., RfOM intake, RdNDF intake, and RdS intake). Furthermore, the responses of the C4:BHBA ratio to the percentage of concentrate and RdS, RdNDF, RdNDF:RfOM, and RdS:RfOM contents were tested.

Variations in glucose NPA were predicted by calculating the amount of starch digested in the small intestine (SIdS). Because the meta-analyses approach requires a minimum variation in the explanatory variable, the model based on SIdS only included the experiments with at least 1 starch-containing diet. The amount of SIdS was predicted from in sacco starch degradation (INRA, 2007) and the empirical model of ruminal and intestinal starch digestion (Offner and Sauvant, 2004). For diets containing no starch, potential predictors of glucose NPA were RdNDF intake, RfOM intake, and DMI.

Given that lactate NPA results from both ruminal fermentation and net production (from C3, valerate, or glucose) by the PDV (Leng et al., 1967; Weeks and Webster, 1975; Weigand et al., 1975), we used the same explanatory variables as for the NPA of VFA and glucose (i.e., RfOM intake, RdNDF intake, RdS intake, and SIdS).

Relationships between the NPA of lactate (Y) and the NPA of glucose or C3 (X) were tested on all the publications reporting results on the effects of diet on the relevant NPA. Similarly, relationships between the NPA of BHBA (Y) and the NPA of C4 and C2 (X) were also tested with a variance-covariance model.

Meta-Analyses

Data Coding To select relevant experiments for subsequent meta-analyses, all publications within FLORA were coded in several steps (Sauvant et al., 2005). A first coding step dealt with the experiments and the type of experimental factors within each publication. Then, within an experiment, experimental groups were defined as the groups of treatments that changed due to the variable of interest and were coded accordingly (Sauvant et al., 2005; Vernet et al., 2005).

Data Expression and Minimum Between-Treatment Variation on the Explanatory Variables Nutrient intake and NPA were expressed as a function of BW^1.0. Indeed, although across-species metabolic processes are best described as a function of BW^0.75 (Brody, 1964), intake as well as nutrient digestion and absorption have been shown to be similar between sheep and cattle when expressed as a function of BW^1.0 (Vernet et al., 2005; Sauvant et al., 2006a).

Calculation of reliable within-experiment responses requires a minimum variation on each explanatory variable (X) within that experiment (Sauvant et al., 2005). Therefore, the minimum acceptable variation of X ( X_min) was determined from all the selected publications for each target nutrient, as follows:

Thus, variations of X were considered relevant for the subsequent statistical analyses when the variation between within-experiment control and experimental treatments was greater than 10 g of concentrate DM/100 g of total DM, 30 g of RdNDF/100 g of DM, 25 g of RdS/100 g of DM, 1.2 g of dOM intake · d^–1 · kg of BW^–1, 1 g of RfOM intake · d^–1 · kg of BW^–1, 0.8 g of RdNDF intake · d^–1 · kg of BW^–1, 0.5 g of RdS intake · d^–1 · kg of BW^–1, 0.2 g of SIdS · d^–1 · kg of BW ^–1, 0.1 g of RdNDF/g of RfOM, and 0.1 g of RdS/g of RfOM. This selection led to the elimination of only 3 to 15% of the treatments depending on the explanatory variable.

Meta-Design, Variance-Covariance Models, and Post-Optimization Analyses Descriptive statistics (µ, SD, range of values) were generated for each variable in the selected data sets as well as matrix of correlation between all the nutritional parameters. Normal distribution of data and homogeneity of variances were tested by Shapiro-Wilk and Levene tests, respectively. Relationships between Y (nutrient NPA, molar percentage of VFA, C2:C3, or C4:BHBA in net portal flux) and the explanatory variable X were studied with a variance-covariance model:

where = the overall intercept and i = the effect of the experimental group i on the intercept , nested within species, and β is the slope of the relationship. If appropriate, quadratic models were also fitted to the data and compared with the linear model. The fit of each relationship was then examined by studying the Studentized residuals of the model and normality of the residuals. Outliers were identified first on the basis of residuals and then by hi leverage [hi = 1/n + (X_i – mean)² (X_i – mean)²], Cook’s distance, and DFITS (difference between the predicted values calculated with and without the ith observation; Sauvant et al., 2005).

Considering that several qualitative factors in the available publications differed between experiments, such as animal species (cattle vs. sheep), physiological stages, and the blood or plasma matrix used for nutrient analyses, it appeared appropriate to consider experiment as a fixed factor in the models and to test the influence of these qualitative factors on the parameters of the models. Moreover, because the statistical effect of the covariate (i.e., the main objective of the present study) is not dependent on whether the experiment factor is fixed or random (Sauvant et al., 2008b), all statistical analyses were consequently carried out using the GLM model (Minitab, Version 14, State College, PA).

Once models were generated, sensitivity analyses were carried out to test whether the models could be improved by a heuristic process (Sauvant et al., 2008b). The rationale was that between-study differences in experimental conditions can lead to differences in response among studies. Individual within-experiment slopes were regressed on interfering factors to check that the model did not need an additional X variable. The residuals and the least squares means (LSMeans) of the model, representing for each experimental group_i the predicted Y_i for the covariable X averaged across all experimental groups (X_ij), were also regressed on the major potentially quantitative interfering factors. The interfering factors tested corresponded to variables describing level of intake and diet composition, including DMI, dietary concentration, and intake level of dOM, RfOM, starch, RdS, NDF, RdNDF, the percentage of concentrate, and the dietary concentrations of ADF, crude fiber, CP, digestible CP, fermentable CP, ME, and proteins digestible in the intestine. When potential interfering factors were qualitative, LSMeans subclasses were compared to clarify the nature of the interaction. Thus, ANOVA was run on LSMeans to test for the influence of species, physiological stage, and matrix (blood or plasma) effects. If a significant interfering factor was detected, it was tested to establish whether its inclusion in the model improved the fit.

Parameters and correlations were considered significant at P < 0.05, whereas P < 0.10 indicated a trend. Thorough graphical examinations were carried out at each stage of the meta-analyses process.

For each nutrient, models obtained with the different predictors were compared. The criteria used to identify the best predictor were absence of interactions with species and intercept, residual means square errors (RMSE), adjusted R² (R²_adj), and absence of interfering factors on slopes, residuals, and LSMeans.

	RESULTS

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Evaluation of the Estimated Chemical Composition and Nutritional Values of the Diets

The GLM analyses showed that the estimated and actual chemical composition of the diets were closely correlated with slopes not significantly different from 1 (0.913 ± 0.057, P = 0.54 for OM digestibility; 0.930 ± 0.021, P = 0.24 for ME; 0.954 ± 0.078, P = 0.83 for crude fiber; and 0.817 ± 0.062, P = 0.44 for CP concentration). The coefficients of correlation were 0.935, 0.996, 0.965, and 0.949, and the RMSE were 2.0 g/100 g, 0.30 MJ/kg of DM, 16 g/kg of DM, and 7.4 g/kg of DM for OM digestibility, ME, crude fiber, and CP contents, respectively. Moreover, the intercept was not different from 0 for OM digestibility (4.94 ± 3.8 g/100 g, P = 0.20), crude fiber (8 ± 21 g/kg of DM, P = 0.72), and CP content (27 ± 26 g/kg of DM, P = 0.45), but was different from 0 for ME concentration (0.615 ± 0.22 MJ/kg of DM, P = 0.05).

Description of the Selected Data and Meta-Design

Table 1 presents numbers of treatments, means, SD, and ranges of all parameters considered, for all preselected data. Although DMI (g · d^–1 · kg of BW^nus;1) was similar between species (P = 0.23), diets fed to sheep had decreased proportions of concentrate (P < 0.001), dOM (P < 0.001), digestible CP (P < 0.001), starch (P < 0.001), and ME concentrations (P < 0.001), and greater cell wall concentrations (P < 0.001) than diets fed to cattle. In contrast, there were no significant differences in dietary RfOM (P = 0.57) and rumen-fermentable CP (P = 0.66) contents between diets offered to both species.

For each nutrient, the number of treatments was similar between cattle and sheep and between females and males (data not shown). The major physiological stages represented were growing animals, followed by nonproductive adult animals for sheep and lactation for cattle. Considering the data used in the models, net fluxes were mainly determined on blood for VFA (n blood = 55, n plasma = 0) and BHBA (n blood = 56, n plasma = 4), whereas the number of treatments determined on blood and plasma were similar for glucose (n blood = 21, n plasma = 29) and lactate (n blood = 65, n plasma = 44). Consequently, the matrix effect on the LSMeans was only tested for glucose and lactate and proved not significant (P = 0.58 and P = 0.31, respectively). Normal distribution of data was shown in the different data sets as well as homogeneity of variances.

Adjusted Models of Nutrient NPA

Tables 2 and 3 present final adjusted models, R²_adj, and RMSE after elimination of the outliers and indicate the number of experimental groups and number of treatments used. The number of outliers was small, representing between 5 and 15% of the initial number of treatments, depending on the models. The models were compared for each dependent variable (NPA of each given nutrient) as reported further below. Criteria for comparison were absence of significant species effect and species x X interaction. Moreover, models having no interfering factors on the slope, residuals, or LSMeans were considered as being the most stable. Figures 1 and 2 present the best adjusted model for each nutrient, with the associated individual within-experiment relationships and meta-design (links between DMI and the percentage of concentrate).

View larger version (43K): [in this window] [in a new window]   Figure 1. Meta-design according to DMI and proportion of concentrate in the diet in the data set used for total VFA model (1A) and proportion of acetate (C2; 2A), propionate (C3; 3A), and butyrate (C4; 4A) models. Within-experiment relationships between the rumen-fermentable OM (RfOM) intake and the net portal appearance (NPA; mmol · h–1 · kg of BW–1) of total VFA (1B) and between the proportion of rumen-digestible NDF in the RfOM (RdNDF/RfOM) and the proportion of C2 (2B), C3 (3B), and C4 (4B). Values within a same experiment are linked, and a dotted line indicates outliers of the model. Open symbols represent the sheep and shaded symbols the cattle. Adjusted models of the NPA of VFA (1C), and proportion of C2 (2C), C3 (3C), and C4 (4C). The lines represent the predicted values, and the points represent the values corresponding to the sum of predicted values and residuals.

View larger version (38K): [in this window] [in a new window]   Figure 2. Meta-design according to DMI and proportion of concentrate in the diet in the data set used for β-hydroxybutyrate (BHBA; 1A), glucose (2A), and lactate (3A) models. Within-experiment relationships between the rumen-fermentable OM (RfOM) intake and the net portal appearance (NPA; mmol · h–1 · kg of BW–1) of BHBA (1B), between intake of starch digested in the small intestine (SIdS) and the NPA of glucose (2B), and between intake of rumen digestible starch (RdS) and the NPA of lactate (3B). Values within a same experiment are linked, and a dotted line indicates outliers of the model. Open symbols represent the sheep and shaded symbols the cattle. Adjusted models of the NPA of BHBA (1C), glucose (2C), and lactate (3C). The lines represent the predicted values, and the points represent the values corresponding to the sum of predicted values and residuals.

The percentage of concentrate was linearly (P < 0.01) related to the molar proportion of C2 and C4 but not C3 (P = 0.62; Table 2). This explanatory variable highlighted a species effect (cattle greater than sheep, P < 0.01) for C4. Both the RdNDF:RfOM model and the RdS:RfOM model explained 76 to 93% of the variance (P < 0.01) in the molar proportions of the 3 major VFA. These linear models were independent of animal species (P > 0.15) and presented no interfering factors on LSMeans. However, with RdS:RfOM (Table 2), the individual VFA slopes and the residuals for C4 were positively influenced by intake level (P < 0.05) and tended to be influenced by diet composition (P < 0.1).

The C2:C3 ratio in NPA was significantly and linearly related to percentage of concentrate (P < 0.1), RdNDF:RfOM (P < 0.001), and RdS:RfOM (P < 0.001), with RdNDF:RfOM (P < 0.001) providing the best adjustment. The RdNDF:RfOM model was affected by species (cattle greater than sheep; P < 0.001), and the individual slopes of all models tended to be affected (P < 0.1) by diet composition (except for percentage of concentrate).

Models based on dietary concentrations of RdNDF or RdS were not clearly significant for C4 (P = 0.076 and P = 0.085, respectively) and were less adjusted (greater RMSE and less R²_adj) than models obtained with RdNDF:RfOM or RdS:RfOM for C2, C3, and C2:C3 (data not shown).

Between-model comparisons based on interactions with species, RMSE, R²_adj, interfering factors on the slope, and on the residuals clearly showed that RdNDF:RfOM was the best predictor of variations in molar proportion of individual major VFA in NPA (Figure 2). An increment of 0.1 RdNDF:RfOM increased the molar proportion of C2 (25.1 percentage units) at the expense of C3 (18.9 percentage units) and C4 (–7.3 percentage units).

Net Portal Flux of BHBA and C4:BHBA Ratio The RdS intake and percentage of concentrate were not related to BHBA NPA (P = 0.17 and P = 0.53, respectively). Models generated from dOM intake, RfOM intake, and RdNDF intake (P < 0.01) presented a species effect (cattle greater than sheep; P < 0.01; Table 3). The model obtained with RdNDF intake showed a quadratic response (P < 0.001), even when experimental groups with fasting treatments [number of experimental groups = 2, n_t (n_treatments) = 3] were removed from the data set. The LSMeans were greater for cattle than for sheep (P < 0.001) and were greater (P < 0.05) for growing animals and dairy cattle than nonproductive adult animals, with all 3 models, whereas no interfering factors were detected on the residuals. Based on the lack of interfering factors on individual slopes, RfOM intake was considered the best predictor of variations in BHBA NPA (Figure 2).

No significant within-experiment relationships could be obtained between the C4:BHBA NPA ratio and the qualitative components of the diet (data not shown).

β-Hydroxybutyrate NPA was related to the C4 NPA (Figure 3A) and C2 NPA (Figure 3B) in within-experiment linear relationships.

View larger version (11K): [in this window] [in a new window]   Figure 3. Relationships between (A) the net portal appearance (NPA; mmol · h–1 · kg of BW–1) of butyrate (C4) and the NPA of β-hydroxybutyrate (BHBA): BHBA NPA = 0.138 ± 0.0364 + 0.839 ± 0.321 x C4 NPA [residual means square error (RMSE) = 0.0540; adjusted R2 (R2adj) = 0.846]; (B) the NPA of acetate (C2) and the NPA of BHBA: BHBA NPA = 0.101 ± 0.0461 + 0.0631 ± 0.0222 x C2 NPA (RMSE = 0.0520; R2adj = 0.853); (C) the NPA of propionate (C3) and the NPA of BHBA: lactate NPA = 0.130 ± 0.0406 + 0.155 ± 0.0519 x C3 NPA (RMSE = 0.0960; R2adj = 0.514). The adjusted models are given along. The lines represent the predicted values, and the points represent the values corresponding to the sum of predicted values and residuals.

Experiments that did not use any starch-containing diets showed no significant within-experiment relationships between glucose NPA and any dietary characteristic. Only significant interexperiment relationships could be obtained with dOM intake, RfOM intake, and RdNDF intake, but with low explained variance (R²_adj < 0.07).

Net Portal Flux of Lactate Significant linear models were obtained with dOM intake (P < 0.001), RfOM intake (P < 0.001), RdNDF intake (P < 0.01), RdS intake (P < 0.001), SIdS intake (P < 0.01), and percentage of concentrate (P < 0.001; Table 3). However, only the model based on RdS intake was species-independent (P = 0.39). It presented a good adjustment (R²_adj = 0.77 and RMSE = 0.042 mmol · h^–1 · kg of BW^–1), although less than the other models, and no interfering factors on residuals and LSMeans. The model predicted that an increment of 10 g of RdS intake · d^–1 · kg of BW^–1 would lead to a 0.136 mmol · h^–1 · kg of BW^–1 increase in lactate NPA. This incremental response was, however, positively influenced by dietary N concentration (CP, fermentable CP, digestible CP, and proteins digestible in the intestine). Only models obtained with RfOM intake and percentage of concentrate presented no interfering factors on the individual slopes, but the RfOM model showed a significant species effect on the slope (P < 0.001; cattle greater than sheep). Thus, RdS intake was considered the best predictor of variations in lactate NPA (Figure 2). Lactate NPA was not significantly related to glucose NPA (P = 0.87), but was linearly and positively related to propionate NPA (P < 0.01) in a within-experiment relationship (Figure 3C).

	DISCUSSION

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Over the years, several mechanistic models have been developed to describe ruminal digestion (Baldwin and Koong, 1977; Danfaer, 1990; Dijkstra et al., 1992; Lescoat and Sauvant, 1995), PDV metabolism of AA (Hanigan et al., 2004), and hepatic metabolism (Waghorn, 1982; Freetly et al., 1993; Danfaer, 1994), most focusing on dairy cows. To our knowledge, no models have yet been developed to predict the NPA of energy-yielding nutrients, prompting the present work. Indeed, the major progress made in feed characterization for ruminants has now made it possible to predict the NPA of energy-yielding nutrients from dietary characteristics (Chesson, 2000; France et al., 2000). Rather than using a mechanistic approach, we opted for an empirical method employing a limited number of variables. This decreases the uncertainty of prediction, resulting in well-adjusted quantitative prediction models representing state-of-the art knowledge. The limit to this approach lies in the range of validity of the models, which is highly dependent on the source data available. This empirical approach also tests the quantitative consistency between all data and it will improve our understanding of biological processes (Lindsay, 1993) and help develop tools for dietary formulation.

Diet Characterization

The empirical models that have been developed in the present work were based on a full and homogenous description of diets according to INRA (2007) to have a common reference system for all data and all models. The diversity of feeds present in the INRA Feed Tables made it possible to characterize about 95% of the experimental diets. Some forages or concentrates happened to be missing from these tables or were not sufficiently diversified, notably tropical forages and wheat hay. In such cases, feeds that were equivalent in terms of ME, CP, and dOM concentrations were used to obtain the same nutritional values as those given by the authors. The overall approach was considered valid, because there was close agreement between estimated and reported (when existing) chemical composition and nutritional values.

In the INRA (2007) feeding system, digestive interactions and effects of intake level are taken into account in the NE allowances rather than in the dietary energy value. In the present study, therefore, we assumed additivity for dOM, RfOM, and dNDF. We tested the influence of this assumption on the results. Based on the model of Sauvant (2003), the influence of digestive interactions on diet dOM intake in the present data set would account for 0.7 ± 0.8 g of dOM intake · d^–1 · kg of BW^–1 for cattle and 0.4 ± 1.2 g of dOM intake · d^–1 · kg of BW^–1 for sheep, representing 4% of average dOM intake. Considering this fairly low level of digestive interactions, the models obtained can be considered as unbiased within the range of validity of the models.

Representativity of the Meta-Design

The data published on NPA of energy-yielding nutrients in ruminants proved relevant to establishing prediction models in both sheep and cattle at different physiological stages. It should be pointed out that there are only limited data from lactating and gestating animals and from goats. The range of validity of the models needs to be defined as accurately as possible in terms of intake level and diet composition. Most of the source data available for building the models was obtained in animals fed at intakes less than 30 g of DMI · d^–1kg of · BW^–1 and with concentrate proportions of below 70 g/100 g of DM. This reflects most real-world settings, except for high-producing dairy females receiving up to 40 g of DMI · d^–1 · kg of BW^–1, or for feeding management strategies based on high-concentrate dry feeds. It should also be stressed that models developed in the current study do not account for specific feed additive effects (buffers, essential oils, ionophores, etc.), which may affect the profile of digestion end products and absorbed nutrients.

Special attention, including thorough graphical examination, was paid to examining possible confounding effects, especially between species, levels of intake, and diet composition. Ruminally fermentable OM predicted variations in NPA of energy-yielding nutrients of rumen origin (total VFA and the derived BHBA) similarly for both species despite being mainly composed of cell walls (RdNDF) in sheep diets and starch (RdS) in cattle diets. Also, it was statistically independent of intake level. Similarly, models of the proportion of individual VFA, glucose, and lactate NPA were species-independent, suggesting that the known digestive differences between sheep and cattle had a small quantitative effect on the chosen predictors.

NPA and Profiles of VFA

Changes in total VFA NPA could be significantly predicted from changes in RfOM intake and dOM intake with a similar quality of adjustment, but RfOM intake appeared to be a more general predictor. Noteworthy, RfOM intake-driven variations in total VFA NPA appear to be due to concomitant variations in portal blood flow (probably due to intake) and arteriovenous differences (probably due to the nature of the diets). It should be underlined that RfOM is a calculated variable that includes several variables, whereas dOM is either measured or predicted from a decreased number of variables (INRA, 2007). Assuming that RfOM is transformed into 30% microbial matter, 18% gas, and 52% VFA (Sauvant et al., 2006b), ruminal production of total VFA is estimated at 7.8 mol/kg of RfOM. The slope obtained for total VFA NPA with RfOM intake (5.9 mol of total VFA NPA/kg of RfOM) is consistent with this estimate, assuming a net portal recovery of about 80% (Kristensen, 2005). Among all the variables tested, RfOM intake appeared to be the most accurate and integrative factor for predicting variations in total VFA NPA. Thus, despite a partly conventional definition, RfOM intake is closer to the actual absorbed flow of VFA than dOM intake.

Predictive models of the variations in molar proportions of individual VFA and of the C2:C3 ratio in the net portal flux were based on the nature of the fermentable OM (RdNDF:RfOM and RdS:RfOM) and the proportion of concentrate in the diet. Of these, RdNDF:RfOM was the best predictor of variations in molar proportion of individual major VFA. Moreover, RdNDF:RfOM can be applied even for starch-free diets and is consistent with the responses commonly observed in the rumen, with a positive slope for C2 and negative slopes for C3 and C4. Surprisingly, the present models predicted linear responses of the molar proportion of individual VFA, whereas empirical rumen models have classically described quadratic variations, as observed in response to proportion of concentrate in the diet (Lescoat and Sauvant, 1995; Archimède et al., 1997). The linear responses in the present work could be explained by the small number of publications with large fermentative deviations, the large treatment differences within these publications, or the lack of experiments specifically designed to test dose responses. The ability of PDV metabolism to limit variations in molar VFA proportions between diets cannot be ruled out, but there is little experimental evidence. Our results suggest a relative enrichment in C2 and C3 and loss of C4 between the rumen and the portal blood. This is classically observed in trials in which both rumen concentrations and NPA of VFA are reported (reviewed by Huntington, 1999). It is also related to the known differences between VFA in terms of extent of their PDV-driven metabolism [Kristensen, 2005; Nozière and Hoch, 2005; i.e., low first-pass extraction of C2 and C3 and large first-pass extraction of C4 (for oxidation and ketogenesis) by the rumen epithelium].

Variations in the NPA of individual VFA could be predicted by successively applying the above-mentioned models (total VFA NPA from RfOM intake and molar percentage of each VFA from RdNDF:RfOM), because RfOM and RdNDF:RfOM were not correlated. Comparative analyses of the NPA of each VFA measured by the authors (Y) vs. predicted by our models (X) using a GLM analyses for the 3 VFA showed intercepts not different from 0 (0.14 ± 0.29 for C2, 0.067 ± 0.099 for C3, and 0.017 ± 0.014 for C4) and slopes close to 1. Indeed, the slope was equal to 1.04 ± 0.16, 0.94 ± 0.16, and 0.93 ± 0.13, respectively, for C2, C3, and C4. The R²_adj were high (>0.83), and the RMSE values were rather low (0.35, 0.13, and 0.025 mmol · h^–1 · kg of BW^–1, for C2, C3, and C4, respectively).

NPA of BHBA

Although BHBA is not directly derived from digestion processes, variations in BHBA NPA could be significantly predicted from dOM intake, RfOM intake, and RdNDF intake. The best of these 3 significant predictors was RfOM intake, despite the fact that variations in BHBA NPA were significantly greater when RfOM intake changes were induced by variations in intake than by variations in diet composition (data not shown). This is consistent with the data of Reynolds and Huntington (1988a), who suggested that BHBA NPA depended primarily on intake rather than on nature of the diet.

The intercepts and LSMeans of the 3 significant models (dOM intake, RfOM intake, and RdNDF intake) presented significant effects of species, physiological stage, or both. A 1-way ANOVA carried out using data obtained in sheep and cattle fed at similar intakes with similar types of diet revealed that the species differences in BHBA NPA were related to significantly greater arteriovenous difference (0.194 vs. 0.0528 mM) and tendency toward decreased portal blood flows (1.81 vs. 2.38 L · h^–1 · kg of BW^–1, respectively) in cattle than in sheep, in agreement with Bermingham et al. (2008). The species differences in arteriovenous differences were not related to differences in arterial concentrations, which were also greater in cattle than in sheep (0.336 vs. 0.129 mM). Several hypotheses can be put forward to explain the greater NPA of BHBA in cattle: 1) a stronger expression of the enzyme converting C4 into BHBA in the rumen wall or the liver in cattle or 2) a decreased PDV utilization of arterial BHBA in cattle. There is still a lack of experimental data in support of these hypotheses.

There was a significant within-experiment relationship between C4 or C2 NPA and BHBA NPA. These relationships are partly related to blood flow, reflecting the global effect of intake level, although specific effects of C2 or C4 on ruminal ketogenesis may not be excluded. Ruminal ketogenesis from C2 was shown to be limited, whereas portal BHBA was primarily derived from the partial oxidation of C4 in the rumen wall (Kristensen et al., 1996b; Nozière et al., 2000a). The PDV uptake of arterial BHBA has been shown to account for 13% of arterial supply in sheep (Kristensen et al., 2000). Interestingly, the 2 studies that included fasting treatments, both in sheep (Katz and Bergman, 1969; Heitmann and Fernandez, 1986), recorded a similar net PDV uptake of BHBA in fasted animals, averaging 0.15 mmol · h^–1 · kg of BW^–1. This is consistent with a preferential PDV use of BHBA during fasting.

To conclude, variation in BHBA NPA is mainly affected by intake, whereas RfOM intake appears the most relevant predictor. However, we obtained no valid unique predictive model for both sheep and cattle, and the models were less adjusted than for VFA, reflecting the fact that BHBA is primarily derived not from digestion but from PDV metabolism, contrary to VFA or glucose.

NPA of Glucose

In the present work, dOM intake, SIdS intake, and percentage of concentrate were tested for their ability to predict variations in glucose NPA. No significant model was obtained with dOM intake, confirming preliminary results obtained by Bermingham et al. (2008) using a more restricted data set. The amount of starch digested in the small intestine, which takes into account between-diet differences in site and extent of starch digestion, was the best predictor of variations in glucose NPA. This confirms the relevance of the Offner and Sauvant (2004) model to predict the amount of starch apparently digested in vivo in the small intestine, 43% of which was recovered net in the portal vein, as indicated by the slope. This is consistent with the 25 to 51% net portal recovery of glucose after starch infusion into the abomasum or duodenum (Reynolds et al., 2001). This reduced level of recovery is probably due to increased utilization of arterial glucose subsequent to an increase in glycemia. However, even when arterial glucose utilization by PDV was taken into account, the starch apparently digested in the small intestine was still not fully recovered in the portal vein. This may be partly due to metabolism of glucose within the small intestine to supply glycerol for the absorption of long-chain fatty acids, as suggested by Reynolds et al. (1988a). It may also be partly due to residual microbial fermentation of glucose after hydrolysis of starch in the small intestine, as suggested by Larsen and Kristensen (2007).

Baseline PDV utilization of arterial glucose, which appeared in the negative intercept obtained with RdS intake (0.110 mmol · h^–1 · kg of BW^–1), was similar to the mean net portal uptake of glucose calculated with starch-free diets (µ = 0.123 mmol · h^–1 · kg of BW^–1; n_t = 87; SEM = 0.008) that nevertheless presented a high variability, ranging from 0.1 mmol · h^–1 · kg of BW^–1 to 0.3 mmol · h^–1 · kg of BW^–1. The only variable that was found to significantly explain this interstudy variability was intake level (particularly RdNDF intake). The negative slope between RdNDF intake and net portal flux of glucose may reflect an increase in glucose utilization by the PDV induced by an increase in digesta mass as discussed in a previous study (Loncke et al., 2007).

NPA of Lactate

All of the 6 explanatory variables tested (i.e., dOM intake, RfOM intake, RdS intake, RdNDF intake, SIdS intake, and percentage of concentrate) were significantly related to lactate NPA. In absence of any species effect on overall intercept and slope, RdS intake appeared to be a useful predictor of variations in lactate NPA in animals fed diets containing starch. Interestingly, the NPA (and arteriovenous differences) of C3 appeared significantly and positively related to NPA (and arteriovenous differences) of lactate, which was not the case for glucose. Although this does not demonstrate an actual contribution of C3 to lactate NPA, this result is consistent with the fact that the net portal release of lactate is mainly driven by stomach tissues (Reynolds et al., 1988a; Rémond et al., 2003).

The factors of variation in lactate NPA remain unclear. Although Bermingham et al. (2008) found no clear effect of DMI, all of the predictors identified in the present work were related to intake, strongly suggesting an interaction between intake and nature of the diet. However, the models obtained are less adjusted than for VFA or glucose, again reflecting the fact that lactate is not directly derived from digestion but from PDV metabolism.

Conclusion and Perspectives

The objective of the present study was to establish response equations for the NPA of energy-yielding nutrients in relation to combined changes in intake and diet composition in ruminants. We showed that with detailed knowledge of the chemical composition of the diets and the site and extent of digestion, although estimated from INRA Feed Tables (INRA, 2007), we were able to generate generic models for both cattle and sheep, with few interfering factors and few outliers. Even though all the models included a significant experiment effect, the lack of interfering factors on residuals, individual slopes, and LSMeans indicated that they could not be improved by including additional factors, suggesting that the best models were built using the most generic driving variables on X. The accuracy of these empirical models was clearly similar to the variability inherent to NPA measurements. Given that certain experimental groups presented a significantly different individual intercept from the overall intercept (i 0), and that LSMeans highlighted some species and physiological stage effects, the predictive ability of these models remains to be fully demonstrated. This is a key issue for further work.

Robust, consistent models were obtained for the NPA of total VFA and glucose and for the molar proportions of individual VFA in the net portal flux in absence of large fermentative deviations, or in absence of incorporation of additives in the rations. Although the source data for establishing these models presented a wide range of intake levels and diet compositions, additional data are needed at high intake levels for lactating ruminants and for high-concentrate diets.

Improvements in the empirical prediction of absorbed VFA will be obtained by comparing production rates and concentrations of VFA in the rumen and their NPA. Models predicting BHBA and lactate NPA will be improved by further exploring relationships with their tissue precursors and with species differences. The subsequent challenge will be to establish predictive models of the exchange (and use) of these nutrients between (and by) tissues.

This research improves our understanding of biological processes and represents the first step toward developing new tools for dietary formulation.

	Footnotes

 
¹ We thank INZO and LIMAGRAIN as well as the Association Nationale de la Recherche Technique for funding this project.

² Corresponding author: noziere{at}clermont.inra.fr

Received for publication February 8, 2008. Accepted for publication August 22, 2008.

	LITERATURE CITED

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 


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