Technical note: optimizing sample size for broiler chicken and pig intestinal histomorphometry and prediction equations
- PMID: 40392651
- PMCID: PMC12202138
- DOI: 10.1093/jas/skaf160
Technical note: optimizing sample size for broiler chicken and pig intestinal histomorphometry and prediction equations
Abstract
Intestinal villi are subject to natural variation within the same sample, and there is no established standard for the number of unique measurements needed for reliable histomorphometry in broiler chickens and pigs. Furthermore, intestinal morphology assessment in monogastric animals typically focuses on villus height, crypt depth, and the villus height to crypt depth ratio. However, limited attention has been given to additional morphology indices, such as villus perimeter and area, largely due to the time and resources required for consistent estimates. Therefore, the objectives of this experiment were: 1) to estimate the minimum number of villi required to minimize the intra-sample coefficient of variation for each intestinal morphology index, and 2) to determine whether more complex metrics, such as villus perimeter and area, can be accurately predicted from linear measurements. A set of 1,283 intestinal morphology data points from 68 intestinal tissues of 4 animal experiments were used for the analysis in broiler chickens. For pigs, a set of 1,368 intestinal morphology data points was used from a single experiment involving 98 nursery-age animals. The NLIN, CORR, and REG procedures of SAS were used for broken-line analysis, correlation coefficients, and prediction equations, respectively. The analysis indicates that measurements from 11 villi per sample in broiler chickens and 10 villi per sample in pigs are necessary to minimize intra-sample variation. In jejunum and ileum, respectively, the villus height was highly correlated (P < 0.05) with villus perimeter (r = 0.95 and 0.87) and villus area (r = 0.77 and 0.63). Additionally, the coefficient of determination (R2) for predicting villus area in the jejunum and ileum using villus height, villus base width, and villus mid-width values were significant for broiler chickens (P < 0.01). The best-fit equation generated for predicting villus area in the jejunum was villus area = -0.136 + 0.195 × villus height + 0.123 × villus base width + 0.532 × villus mid-width (R2 = 0.92; RMSE = 0.01), while in the ileum, it was villus area = -0.069 + 0.136 × villus height + 0.122 × villus base width + 0.337 × villus mid-width (R2 = 0.86; RMSE = 0.02). In conclusion, this study provides the minimum number of intestinal villi required for consistent morphological assessment, relationships among intestinal histomorphometric indices, and equations for predicting villus perimeter and area from villi linear measurements.
Keywords: intestinal morphology; prediction; villus area; villus height; villus perimeter.
Plain language summary
Understanding the structure of the intestine in broiler chickens and pigs is important for assessing their digestive health and efficiency. Traditionally, intestinal absorptive capacity is estimated using morphometric parameters such as villus height and crypt depth. However, other important measurements, such as the villus perimeter and area are often overlooked because they take more time and resources to analyze. Additionally, there is no established standard for the number of villi needed to obtain accurate intestinal morphology measurements in broiler chickens and pigs. This study evaluated the minimum number of villi required to reduce variability in intestinal morphology measurements for both broiler chickens and pigs. Furthermore, we explored whether more complex morphometric indices, such as villus perimeter and area, could be accurately predicted from simpler linear measurements, such as villus height and mid-width. Our results revealed that the villus height, villus mid-width, and base width were reliable predictors for both villus perimeter and area. These results offer a more efficient approach to intestinal morphology assessment in monogastric animals, reducing the time and resources required for more complex measurements while maintaining accuracy.
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