Field‐based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning. Issue 12 (13th December 2022)

• Record Type: Journal Article
• Authors: Kophamel, Sara; Ward, Leigh C.; Konovalov, Dmitry A.; Mendez, Diana; Ariel, Ellen; Cassidy, Nathan; Bell, Ian; Balastegui Martínez, María T.; Munns, Suzanne L.
• Is Part Of: Ecology and evolution. Volume 12:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-13
• Subjects: adipose tissue -- Bland–Altman -- body condition -- body fat -- nutritional status -- sea turtle; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.9610
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass − (−0.03 [intercept] − 0.29 * length 2 /resistance at 50 kHz + 1.07 * body mass − 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%–0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level. Abstract : Loss of body fat (i.e., adipose tissue) in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Here, we calibrate bioelectrical impedance spectroscopy (BIS) for assessing the nutritional status of sea turtles. Using a form of artificial intelligence, we trained a computer model to identify and quantify body fat from computed tomography scans, which enabled estimating body fat from the BIS measurements. Our standardized BIS protocol can be applied to other species and taxa and improves on conventional body composition assessment methods (e.g., body condition indices) by accurately estimating body fat.