UNLABELLED: Cortisol is the most commonly used biomarker to compare physiological stress between individuals. Its use, however, is frequently inappropriate. Basal cortisol production varies markedly between individuals. Yet, in naturalistic studies that variation is often ignored, potentially leading to important biases. OBJECTIVES: Identify appropriate analytical tools to compare cortisol across individuals and outline simple simulation procedures for determining the number of measurements required to apply those methods. METHODS: We evaluate and compare three alternative methods (raw values, Z-scores, and sample percentiles) to rank individuals according to their cortisol levels. We apply each of these methods to first morning urinary cortisol data collected thrice weekly from 14 cycling Mayan Kaqchiquel women. We also outline a simple simulation to estimate appropriate sample sizes. RESULTS: Cortisol values varied substantially across women (ranges: means: 1.9-2.7; medians: 1.9-2.8; SD: 0.26-0.49) as did their individual distributions. Cortisol values within women were uncorrelated. The accuracy of the rankings obtained using the Z-scores and sample percentiles was similar, and both were superior to those obtained using the cross-sectional cortisol values. Given the interindividual variation observed in our population, 10-15 cortisol measurements per participant provide an acceptable degree of accuracy for across-women comparisons. CONCLUSIONS: The use of single raw cortisol values is inadequate to compare physiological stress levels across individuals. If the distributions of individuals' cortisol values are approximately normal, then the standardized ranking method is most appropriate; otherwise, the sample percentile method is advised. These methods may be applied to compare stress levels across individuals in other populations and species.
UNLABELLED: Cortisol is the most commonly used biomarker to compare physiological stress between individuals. Its use, however, is frequently inappropriate. Basal cortisol production varies markedly between individuals. Yet, in naturalistic studies that variation is often ignored, potentially leading to important biases. OBJECTIVES: Identify appropriate analytical tools to compare cortisol across individuals and outline simple simulation procedures for determining the number of measurements required to apply those methods. METHODS: We evaluate and compare three alternative methods (raw values, Z-scores, and sample percentiles) to rank individuals according to their cortisol levels. We apply each of these methods to first morning urinary cortisol data collected thrice weekly from 14 cycling Mayan Kaqchiquel women. We also outline a simple simulation to estimate appropriate sample sizes. RESULTS:Cortisol values varied substantially across women (ranges: means: 1.9-2.7; medians: 1.9-2.8; SD: 0.26-0.49) as did their individual distributions. Cortisol values within women were uncorrelated. The accuracy of the rankings obtained using the Z-scores and sample percentiles was similar, and both were superior to those obtained using the cross-sectional cortisol values. Given the interindividual variation observed in our population, 10-15 cortisol measurements per participant provide an acceptable degree of accuracy for across-women comparisons. CONCLUSIONS: The use of single raw cortisol values is inadequate to compare physiological stress levels across individuals. If the distributions of individuals' cortisol values are approximately normal, then the standardized ranking method is most appropriate; otherwise, the sample percentile method is advised. These methods may be applied to compare stress levels across individuals in other populations and species.
Authors: Tejaswi Ogirala; Ashley Eapen; Katrina G Salvante; Tomas Rapaport; Pablo A Nepomnaschy; Ash M Parameswaran Journal: Med Biol Eng Comput Date: 2017-01-12 Impact factor: 2.602
Authors: Anne Ac van Tetering; Jacqueline Lp Wijsman; Sophie Em Truijens; Annemarie F Fransen; M Beatrijs van der Hout-van der Jagt; S Guid Oei Journal: BMJ Simul Technol Enhanc Learn Date: 2018-04-28
Authors: Dario Maestripieri; Amanda C E Klimczuk; Marianne Seneczko; Daniel M Traficonte; M Claire Wilson Journal: PLoS One Date: 2013-12-16 Impact factor: 3.240
Authors: Cindy K Barha; Katrina G Salvante; Courtney W Hanna; Samantha L Wilson; Wendy P Robinson; Rachel M Altman; Pablo A Nepomnaschy Journal: PLoS One Date: 2017-05-25 Impact factor: 3.240
Authors: Christopher R von Rueden; Benjamin C Trumble; Melissa Emery Thompson; Jonathan Stieglitz; Paul L Hooper; Aaron D Blackwell; Hillard S Kaplan; Michael Gurven Journal: Evol Med Public Health Date: 2014-09-11