Integration of Gut Microbiome and Dietary Chemical Composition Improves Digestible Energy Prediction in High-fiber Swine Diets

Traditional feed evaluation systems assign negative energy values to dietary fiber, yet recent observations demonstrate maintained energy digestibility despite substantial increases in dietary fiber content. This study aimed to develop a microbiome-informed approach for predicting digestible energy (DE) in high-fiber swine diets through integration of gut microbiome composition and dietary chemical composition. Growing barrows (n = 18; initial body weight = 50.6 ± 4.9 kg) were individually housed in metabolism crates and assigned to one of four experimental diets varying in fiber content from 75 to 287 g NDF/kg dry matter (DM). Diets included corn-soybean meal control, 29.65% corn dried distillers grains with solubles, 36.65% full-fat rice bran, or 24.59% wheat middlings, all formulated to equivalent net energy levels. Fecal samples (n = 49) were collected for DE determination and microbiome profiling. Deoxyribonucleic acid was extracted from fecal samples and the V4 region of the 16S rRNA bacterial gene was amplified and sequenced. Sparse canonical correlation analysis integrated microbiome (X1) and dietary chemical composition (X2) datasets, generating latent variables u = X1α and v = X2β. The combined latent space Z = [u, v] served as predictors for DE through ordinary least squares regression. Through composition of linear transformations, where Z = X2B + ε and DE = wTZ + w0, the model was reduced to DE = w0 + ΣiBiw × X2, embedding microbiome contributions within diet composition coefficients. Nine published digestibility studies were selected for independent validation. The microbiome and diet composition latent variables demonstrated strong correlation (r = 0.94). Network analysis revealed negative correlations between Bacteroides spp., Oscillospiraceae spp., Emergencia timonensis, Butyricicoccus pullicaecorum, and Lientehia spp. with dietary NDF, suggesting these taxa are adapted to metabolize soluble carbohydrates rather than recalcitrant fiber fractions. The microbiome latent variable explained differential amounts of DE variance depending on dietary fiber content. When combining all diets (n = 49), the microbiome accounted for 15% of DE variance, whereas analysis of high-fiber diets exclusively (n = 35) revealed 47% of DE variance was explained by the microbiome. Mathematical projection yielded: DE (kcal/kg) = 3863.21 – 0.2499 × crude protein – 1.336 × NDF – 0.8756 × ether extract + 1.4922 × ash (g/kg DM). Independent validation demonstrated superior accuracy compared with NRC (2012) equation, with root mean square error of 178.4 versus 208.0 kcal/kg, respectively, representing 14.2% improvement. The NRC equation increasingly underestimated DE as fiber content increased, with divergence exceeding 150 kcal/kg at NDF above 200 g/kg DM, while the microbiome-informed model-maintained accuracy across the entire range tested. These findings suggest that gut microbiome fermentation contributes substantially to energy extraction from high-fiber ingredients and can be quantified through computational modeling.