Payan-Carreira Rita, Martins Luis, Miranda Sónia, Olivério Pedro, Silva Severiano R
Zootecnia Department, CECAV-Centro de Ciência Animal e Veterinária, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal.
EUVG-Escola Universitária Vasco da Gama, Campus Universitário, Bloco B, Lordemão, 3020-210, Coimbra, Portugal.
Acta Vet Scand. 2016 Oct 20;58(Suppl 1):58. doi: 10.1186/s13028-016-0239-y.
Systems for estimating body condition score (BCS) are currently used in canine practice to monitor fatness levels. These tools are cheap and easy to use but lack the necessary precision to monitor small changes in body fat, particularly during weight control treatments or in research. The present work aims to study the application of real-time ultrasonography (RTU) together with image analysis in the assessment of subcutaneous fat depots in dogs. Ultrasound images were collected from five anatomical locations (chest, flank, abdomen, thigh and lumbar) from 28 healthy dogs of different breeds and with a body weight (BW) ranging from 5.2 to 33.0 kg. BCS was collected by visual appraisal using a 5-point scale. Subcutaneous fat thickness (SFT) was estimated from RTU images, using the average of three measurements taken in fat deposits located above the muscles represented in each image. Correlations were established between SFT and BW or BCS as well as a classification of BCS-based fatness [overweight (BCS = 4), ideal (BCS = 3) and lean (BCS = 2)].
SFT was found to differ between the five regions considered (P < 0.001). Abdomen and thigh were the areas displaying the widest variation for the different dogs included in the study and also those correlating most with BW, in contrast to the chest, which showed the least variation. Overall, a strong correlation was found between BCS and SFT. The highest correlations were established for the flank, abdomen and lumbar areas. In every anatomical area, a decrease in SFT was observed across all three BCS classes, ranging from 48 to 65 % among overweight and ideal dogs, and from 46 to 83 % among ideal and lean dogs.
Preliminary data showed that within this population there was a strong correlation between BCS and SFT estimated from RTU images. It was also observed that RTU measurements for fat thickness differed among the anatomical points surveyed suggesting differences in their sensitivity to a change in BCS. The images displaying the best prediction value for fatness variations were those collected at the lumbar and abdomen areas.
目前在犬类临床实践中使用身体状况评分（BCS）系统来监测肥胖水平。这些工具价格便宜且易于使用，但缺乏监测体脂微小变化的必要精度，特别是在体重控制治疗期间或研究中。本研究旨在探讨实时超声检查（RTU）结合图像分析在评估犬皮下脂肪库中的应用。从28只不同品种、体重（BW）在5.2至33.0千克之间的健康犬的五个解剖部位（胸部、胁腹、腹部、大腿和腰部）采集超声图像。使用5分制通过视觉评估收集BCS。从RTU图像估计皮下脂肪厚度（SFT），采用在每个图像中肌肉上方脂肪沉积物处进行的三次测量的平均值。建立了SFT与BW或BCS之间的相关性，以及基于BCS的肥胖分类[超重（BCS = 4）、理想（BCS = 3）和消瘦（BCS = 2）]。
在所考虑的五个区域中，SFT存在差异（P < 0.001）。腹部和大腿是研究中不同犬只显示出最大变化的区域，也是与BW相关性最高的区域，而胸部显示的变化最小。总体而言，BCS与SFT之间存在很强的相关性。胁腹、腹部和腰部区域的相关性最高。在每个解剖区域，所有三个BCS类别中的SFT均有所下降，超重和理想犬之间下降幅度为48%至65%，理想和消瘦犬之间下降幅度为46%至83%。
初步数据表明，在该群体中，BCS与从RTU图像估计的SFT之间存在很强的相关性。还观察到，所调查解剖部位的脂肪厚度RTU测量值存在差异，表明它们对BCS变化的敏感性不同。对肥胖变化显示最佳预测价值的图像是在腰部和腹部区域采集的图像。
