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. 2018 Dec 30;32(24):2122-2128.
doi: 10.1002/rcm.8291.

Implications of the variation in biological 18 O natural abundance in body water to inform use of Bayesian methods for modelling total energy expenditure when using doubly labelled water

Priya A Singh  1 , Elise R Orford  1 , Kevin Donkers  1 , Leslie J C Bluck  1 , Michelle C Venables  1
Affiliations

Implications of the variation in biological 18 O natural abundance in body water to inform use of Bayesian methods for modelling total energy expenditure when using doubly labelled water

Priya A Singh et al. Rapid Commun Mass Spectrom. .

Abstract

Rationale: Variation in 18 O natural abundance can lead to errors in the calculation of total energy expenditure (TEE) when using the doubly labelled water (DLW) method. The use of Bayesian statistics allows a distribution to be assigned to 18 O natural abundance, thus allowing a best-fit value to be used in the calculation. The aim of this study was to calculate within-subject variation in 18 O natural abundance and apply this to our original working model for TEE calculation.

Methods: Urine samples from a cohort of 99 women, dosed with 50 g of 20% 2 H2 O, undertaking a 14-day breast milk intake protocol, were analysed for 18 O. The within-subject variance was calculated and applied to a Bayesian model for the calculation of TEE in a separate cohort of 36 women. This cohort of 36 women had taken part in a DLW study and had been dosed with 80 mg/kg body weight 2 H2 O and 150 mg/kg body weight H2 18 O.

Results: The average change in the δ18 O value from the 99 women was 1.14‰ (0.77) [0.99, 1.29], with the average within-subject 18 O natural abundance variance being 0.13‰2 (0.25) [0.08, 0.18]. There were no significant differences in TEE (9745 (1414), 9804 (1460) and 9789 (1455) kJ/day, non-Bayesian, Bluck Bayesian and modified Bayesian models, respectively) between methods.

Conclusions: Our findings demonstrate that using a reduced natural variation in 18 O as calculated from a population does not impact significantly on the calculation of TEE in our model. It may therefore be more conservative to allow a larger variance to account for individual extremes.

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Figures

Figure 1
Figure 1
Urine 18O natural abundance across 14 days in three representative women from the DNSIYC cohort (A, B, C) with D displaying the individual with the greatest range
Figure 2
Figure 2
Pearson's correlation (A) and Bland–Altman (B) comparisons between Bluck and modified Bayesian methods for estimating total energy expenditure. For the Bland–Altman comparison, the solid line represents the mean difference between the two methods and the dashed lines the 95% limits of agreement (−103, 78 kJ/day) [Color figure can be viewed at http://wileyonlinelibrary.com]
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