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ORIGINAL ARTICLE

Inter- and intraindividual correlations of backgroundabundances of 2H, 18O and 17O in human urine andimplications for DLW measurementsESF Berman1, EL Melanson2,3, T Swibas3, SP Snaith1 and JR Speakman4,5

BACKGROUND/OBJECTIVES: The method of choice for measuring total energy expenditure in free-living individuals is thedoubly labeled water (DLW) method. This experiment examined the behavior of natural background isotope abundancefluctuations within and between individuals over time to assess possible methods of accounting for variations in the backgroundisotope abundances to potentially improve the precision of the DLW measurement.SUBJECTS/METHODS: In this work, we measured natural background variations in 2H, 18O and 17O in water from urine samplescollected from 40 human subjects who resided in the same geographical area. Each subject provided a urine sample for 30consecutive days. Isotopic abundances in the samples were measured using Off-Axis Integrated Cavity Output Spectroscopy.RESULTS: Autocorrelation analyses demonstrated that the background isotopes in a given individual were not temporallycorrelated over the time scales of typical DLW studies. Using samples obtained from different individuals on the same calendar day,cross-correlation analyses demonstrated that the background variations of different individuals were not correlated in time.However, the measured ratios of the three isotopes 2H, 18O and 17O were highly correlated (R2 = 0.89–0.96).CONCLUSIONS: Although neither specific timing of DLW water studies nor intraindividual comparisons were found to be avenuesfor reducing the impact of background isotope abundance fluctuations on DLW studies, strong inter-isotope correlations within anindividual confirm that use of a dosing ratio of 8‰:1‰ (0.6 p.p.m.:1 p.p.m.) optimizes DLW precision. Theoretical implicationsfor the possible use of 17O measurements within a DLW study require further study.

European Journal of Clinical Nutrition advance online publication, 25 March 2015; doi:10.1038/ejcn.2015.10

INTRODUCTIONThe high prevalence of obesity in the United States1 andworldwide is a major public health concern.2–6 Obesity stemsfrom an imbalance between total caloric consumption and totalenergy expenditure (TEE), although the causes of this imbalanceremain debated.7 Accurate and precise measurements of TEEtherefore have a pivotal role in understanding and ultimatelyreversing this epidemic. The method of choice for measuring TEEin free-living individuals is the doubly labeled water (DLW)method,8 which is based on the principle that the oxygenin body water is in complete isotopic equilibrium with the oxygenin dissolved carbon dioxide (CO2). Consequently, a stable isotopiclabel of oxygen introduced into body water is eliminated by thecombined flux of body water and exhaled CO2. Because hydrogenis eliminated only in water and not in CO2,

9 the difference in therates of elimination of simultaneously administered oxygen andhydrogen labels is a measure of CO2 production.Despite its widespread use,7,10–15 the DLW method has some

limitations. First, individual measurements with low analyticalerrors are only precise to ± 5% at best,16,17 and hence the methodis currently most suitable for studies of populations. Thislow precision contributes to the second problem, which is thatthe DLW method is expensive because of the need for relatively

large sample sizes to achieve sufficient statistical power, the largequantities of H2

18O needed for dosing18 and the costs of isotopeanalysis. This precision problem is in large part caused by the factthat the stable isotopes used to dose subjects are naturally occurringand their abundances are not stable over time. Hence, althoughindividuals provide an initial background sample before dosing, thissample does not provide a good estimate of the backgroundabundance at the time the final sample is taken.19 It has beensuggested that utilizing the covariance between 2H and 18O innatural abundance variations when dosing would minimize theerrors caused by natural abundance variations.20 However, evenwith these factors incorporated, the precision of individual DLWestimates of CO2 production was estimated to be 4.90±2.14%(coefficient of variation), of which 3.23±1.20% (coefficient ofvariation) arose from natural abundance variation.21

Three proposals have been considered for minimizing back-ground variation effects. First, if the isotope abundances in a givenindividual are correlated in time, then timing a DLW study to takeadvantage of these temporal correlations would increase theprobability that the initial background measurement was repre-sentative of the final background abundance. Second, if back-ground abundances are correlated across individuals, it would befeasible to use the background variations in undosed individuals

1Los Gatos Research, Mountain View, CA, USA; 2Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 3Divisionof Geriatric Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 4Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen,Scotland, UK and 5State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.Correspondence: Dr ESF Berman, Los Gatos Research, 67 E. Evelyn Avenue Suite 3, Mountain View, 94041 CA, USA or Professor JR Speakman, Institute of Biological andEnvironmental Science, University of Aberdeen, Tillydrone Avenue, Aberdeen AB24 2TZ, UK.E-mail: [email protected] or [email protected] 24 June 2014; revised 21 December 2014; accepted 23 December 2014

European Journal of Clinical Nutrition (2015), 1–8© 2015 Macmillan Publishers Limited All rights reserved 0954-3007/15

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to reconstruct the background variation in dosed subjects. Finally,it is well known that the background isotope abundances of2H and 18O are correlated.19,20,22 In theory, background variationsof 17O, another naturally occurring rare stable isotope of oxygen,are correlated with those of 18O and 2H. Hence, measurements ofbackground 17O made in parallel to measures of dose eliminationshould allow reconstruction of the underlying pattern of back-ground variation in 2H and 18O.18 However, until very recently,measurements of 17O were made by fluorination coupled toisotope ratio mass spectrometry and were prohibitively expensivefor application to DLW studies.23 An additional obstacle to theapplication of this last proposal is that the 17O isotope is generallyenriched whenever water is artificially enriched in 18O.In this work, we measured natural background variations of

2H, 18O and 17O in human urine samples from 40 subjects usingOff-Axis Integrated Cavity Output Spectroscopy (OA-ICOS)to determine the feasibility of using correlations within anindividual with time, parallel measures of undosed individuals orthe variations in 17O to correct for background variations of 18Oand 2H in DLW experiments. 17O measurements of enriched dosewater were also conducted. Both intra- and interindividualcorrelations were examined to explore potential means toimprove the precision (and thus cost) of the DLW technique.The sensitivity and robustness of OA-ICOS for measurements ofwater isotopomers H2O,

1H2HO and H218O in liquid water24–29 and

human urine30 have been previously published. Recently, sensitivemeasurements of the H2

17O isotopomer in liquid water have beenreported.31

MATERIALS AND METHODSInstitutional approvalProcedures followed were in accordance with the ethical standards of theHelsinki Declaration of 1975 as revised in 1983. The study was approved bythe Colorado Multiple Institutional Review Board.

ParticipantsForty (40) healthy adults (20 males and 20 females; age= 37 ± 10 years,range= 23–62 years; BMI = 23.5 ± 3.8 kgm−2, range= 16.8–32.6 kgm−2)were recruited from the local Denver area. Exclusion criteria were self-reported acute or chronic disease (diabetes, heart diseases, thyroiddiseases); recent (2 weeks) or scheduled IV transfusions; use of diureticmedications and recent (2 weeks) or scheduled travel 4200 miles fromDenver.

Sample collection and preparationSubjects were instructed to maintain their normal lifestyle during thecourse of the sample collection. No isotope dose was ingested; the urinesamples obtained provided a measure of the natural isotopic abundancesover the course of the study. Urine samples were self-collected from thefirst void in the morning for 30 consecutive days in each individual andfrozen in 4ml glass vials. Previously frozen urine samples were prepared bycentrifugation only as described by Berman et al.30 No distillation ordecolorizing steps were undertaken, reducing the probability of sample-handling-induced errors. Duplicates were prepared from each sample, onefor analysis on each of the two OA-ICOS instruments.

OA-ICOS instrumentationWe utilized two OA-ICOS laser absorption spectrometers (Los GatosResearch (LGR), Mountain View, CA, USA), one for simultaneous directmeasurement of 2H/1H and 18O/16O (LWIA-V30d) and one for directmeasurement of 17O/16O stable isotopes in liquid water (prototypeinstrument). Briefly, in OA-ICOS, laser radiation is coupled to an opticalcavity in an off-axis manner and is continuously measured similar to astandard laser absorption experiment.32 The cavity provides an extra-ordinarily long effective optical pathlength (typically 2–10 km), and the off-axis configuration provides robustness, allowing for the accuratequantification of water isotopomers with very high precision.24–31

Centrifuged urine samples were introduced into the OA-ICOS instrument

via a PAL HTC-xt autoinjector (CTC Analytics, Zwingen, Switzerland)equipped with a Hamilton 1.2 μl, zero dead-volume syringe (P/N: 203185-/01) and a heated (≈85 °C) injector block (LGR), where the water wasevaporated for isotope analysis directly on the water vapor.

OA-ICOS analysis of urine samplesSamples and isotope reference waters were interleaved throughout eachOA-ICOS analysis to ensure high accuracy by frequent intra-run calibration.Calibration was completed using deionized secondary isotope referencewaters that had previously been calibrated by OA-ICOS against the primaryinternational reference waters VSMOW2 and SLAP2.33 An internal controlwater of known isotopic composition was measured periodically through-out each analysis.30 Sample-to-sample memory is well known in waterisotope analysis,8 including analysis by laser absorption spectroscopy.25,34

Analyses of urine have increased memory effects, which worsen during arun because of the accumulation of urine solutes in the injector block.Urine analyses were completed as described in Berman et al.30 to addressboth the instrumental and the additional solute memory betweensuccessive urine samples. Data were analyzed using commercially availablePost Analysis Software (LGR, version 2.2.0.12). Data were checked for thepresence of any organic contamination using the Spectral ContaminationIdentifier (LGR, version 1.0.0.69).35 Approximately 4% of the urine samplesshowed small but detectable contamination, likely from ethanol; the size ofthe contamination was sufficiently low to have no noticeable effect onthe data.

Statistical analysisTo determine whether temporal patterns exist in the measured back-ground variations of each isotope within each subject, autocorrelation andpartial autocorrelation analyses were performed on the time seriesdata for each individual (R v3.0.1). Autocorrelation analysis examines thesimilarity between a data set and a time-lagged version of itself; partialautocorrelation analysis displays the similarity with the effects of shorterlag times removed. Autocorrelation analysis would thus highlightreproducible changes associated with temporal patterns such as week-end/weekday changes. Any autocorrelation outside of the 95% confidenceinterval (−0.36o ro0.36 for 30 measurements) is deemed significant.Cross-correlation analysis is used to detect common patterns of variationbetween subjects on the same calendar day. Cross-correlation analyses forPearson’s correlation coefficients were performed using Origin Pro (v 9.0)for each pair of subjects with at least five overlapping sample collectiondays. Throughout the paper, for data that are normally distributed, themean and standard deviation are presented; otherwise, the median andmedian absolute deviation are used.

ModelingTo model the effects of background isotope variation on DLW measure-ments, we simulated the isotope washout curves for a typical DLWexperiment fixing the following parameters: 18O dose enrichment = 100000 p.p.m.; 2H dose enrichment = 65 000 p.p.m.; dose volume 90.15ml;starting (measured) background 18O abundance 2000 p.p.m.; starting(measured) background 2H abundance 155 p.p.m.; initial post-dose18O abundance 2150 p.p.m. and initial post-dose 2H abundance =251 p.p.m. These initial enrichment ratios, with the δ2H (‰) enrichmentapproximately eight times the δ18O (‰) enrichment, have been suggestedto minimize the impact of background variations in isotope ratio.19 Theseparameters yield a dilution space ratio of 1.034. We then fixed the18O washout rate (ko) to 0.089 per day and allowed the washout ratio(ko/kd) to have values between 1.1 and 1.4. Given these parameters, wesimulated the decline in isotope enrichment for both 18O and 2H from days7 to 21 of a typical experiment under the assumptions that there was noanalytical (that is, measurement) uncertainty, no temporal variation in thebackground isotope levels (that is, the final background isotope level wasequal to the starting level), and the metabolic rate remained constant overthe entire measurement period. We used equation A6 from Schoelleret al.36 to calculate the CO2 production as 38.41 moles per day.We then explored the consequences of introducing uncertainty into

isotope determinations. First, we simulated analytical variation in theisotope measurements using the empirical uncertainty in measurementsmade by the OA-ICOS analyzers. To each isotope value (initial and finaloxygen, and initial and final hydrogen), we added a random value,generated from a normal distribution centered on zero with a standarddeviation defined from the known analytical uncertainty of the instrument.

Correlations of background isotopes in human urineESF Berman et al

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We then generated 1000 estimates for each day between days 7 and 21,and for each washout ratio (1.1, 1.15, 1.2, 1.3 and 1.4), for a total of14 × 5× 1000= 70 000 simulations, including simulated analytical variation.Second, we modeled the impact of variation in the final backgroundisotope abundances using the magnitudes of the background abundancefluctuations measured in this study. Because our data indicate nocorrelation between the background isotope abundance at day 0 andthe background abundances 7–21 days later, we added a random errorterm to the final oxygen abundance on a given day. To simulate thevariation in the hydrogen isotope, we used the fact that background 18Oand 2H are correlated and set the deviation of the final hydrogenbackground estimate equal to 0.47 times the oxygen deviation (0.47 is thefitted gradient between 18O and 2H measurements observed in the presentstudy). We then introduced a level of stochasticity to this estimate toreflect the fact that the correlation between the two isotopes was notperfect. Having derived the simulated 18O and 2H values, we were able tocombine these estimates to recalculate 1000 estimates of the CO2production for each day and washout combination, including the effectsof both analytical uncertainty and background isotope abundancevariation. The individual estimates allowed us to evaluate the % error inthe CO2 calculation (equivalent to % error in the TEE estimate) thatstemmed from uncertainty in the isotope abundances over days 7–21 ofan experiment. To evaluate the impact of using the 17O values, we reducedthe standard deviation of the distribution from which the simulated errorvalues were drawn to reflect the correlation between 17O, 18O and 2Habundances. The % errors in the resultant CO2 production (equal to TEE)were then calculated for all the day and washout combinations as above.

RESULTSWithin-subject variationThe measured background abundances of 2H/1H, 18O/16O and17O/16O vary in both magnitude and direction between subjectsand over the course of the study period (Figure 1). The average

(standard deviation) span of isotope ratios (max—min) acrossall 40 subjects is 7.80‰ (2.36‰) for δ2H, 1.16‰ (0.43‰) forδ18O and 0.76‰ (0.22‰) for δ17O. Figure 1 also demonstrates theclose correspondence in the variations of 2H, 18O and 17O.Figure 2 shows the median values across all 40 subjects of the

three autocorrelations (top) and three partial autocorrelations(bottom; one for each isotope). For all three isotopes, the highestautocorrelations occurred at a lag of 1 day; correlationsprogressively declined as the lag increased. Beyond 2 days, thecorrelations were no longer significant. The partial autocorrela-tions show that, on average, only the 1-day lag was significant(95% confidence interval calculated for the 30-day time seriesfor a single subject). Hence, the significance of the 2-day lags inthe autocorrelation plots were the result of the effects of two1-day lags.

Between-subject variationThe measured background variations of each subject were alsocompared with background variations of other subjects whoseurine was collected on the same calendar day to examine whetherthere are significant correlations in isotope fluctuation betweenpeople at a given time. This type of correlation would occur if, forexample, natural abundance variations were primarily the result ofchanges in the drinking water source in a local area. Figure 3shows the results of cross-correlation analyses for δ2H (a), δ18O (b)and δ17O (c). Each pixel represents the magnitude of the Pearsoncorrelation coefficient for the two different subjects for all samplescollected on overlapping calendar days, resulting in 375 cross-correlations for each isotope. The insets show histogramsof the magnitudes of the correlation coefficients with the best-fit Gaussian distribution to the histogram data. The correlation

Figure 1. Representative time traces of δ2H (pink circles, left axis), δ18O (cyan triangles, right axis) and δ17O (brown diamonds, right axis) inhuman urine over a 30-day measurement period. The background variations vary in both magnitude and direction between subjects and overthe course of the study period.


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© 2015 Macmillan Publishers Limited European Journal of Clinical Nutrition (2015) 1 – 8

coefficients were normally distributed and centered at zero (fittedcenter correlation values of 0.05 (σ= 0.43) for δ2H, 0.09 (σ= 0.33)for δ18O and 0.02 (σ= 0.32) for δ17O).Figure 3 clearly shows that most subject pairs were uncorre-

lated. The occasional high-correlation coefficient was equally likelyto be positive or negative and generally occurred when thecalendar overlap between individuals was small (⩽7 days).

Between isotope variationAlthough the background variations of an individual isotope werenot found to be correlated either within an individual for lagsexceeding a few days or between individuals, the measured ratiosof the three water isotopes within a sample were highly correlatedwith each other. The correlations between the measured isotopeabundances for each subject for each pair of isotopes werecalculated using standard regression analysis. These analysesresulted in an average linear correlation (R2) of 0.76 ± 0.14 (1σ)between δ18O and δ17O, 0.71 ± 0.16 (1σ) between δ2H and δ17Oand 0.88 ± 0.08 (1σ) between δ2H and δ18O.The measured urine isotope values for δ2H, δ18O and δ17O for

all 40 subjects and over the entire study period were plottedpairwise for the same sample and the correlations between theisotope ratios calculated using standard regression analysis.

Figure 4 shows the excellent linear correlations, across all studyparticipants, between δ18O and δ17O (R2 = 0.96), δ2H and δ18O(R2 = 0.92) and δ2H and δ17O (R2 = 0.89). In the latter two plots, theexpected effects of differential fractionation of the isotopes of twodifferent elements are evident in several individuals. The increasein the correlation from a single subject to all subjects takentogether is attributed to the much larger range of values and theincreased sample size. Regression analysis across all studyparticipants of the relation between δ2H and δ18O resulted in aslope (standard error) of 6.53 (0.06). The average (standarddeviation) of the individual slopes for the 40 participants was6.1 (1.0).The data also show remarkable agreement with the global

meteoric water line for 18O and 17O (δ'17O = 0.528 × δ'18O+0.000033).37 Luz and Barkan have measured natural waters fromaround the globe, determining the global relationship, the globalmeteoric water line, between 18O and 17O (note that they use amodified isotope unit, δ' = ln(δ+1)). Regression analysis of thestudy data gives a linear fit of δ'17O = 0.526 × δ'18O—0.0564(R2 = 0.96), the slope of which agrees with the global meteoricwater line within experimental error. The intercept of theregression analysis shows that, on average, human urine has alower 17O-excess than natural waters, an expected result

Figure 2. Median autocorrelation (top) and partial autocorrelation (bottom) coefficients for δ2H (left), δ18O (middle) and δ17O (right) in humanurine from 40 subjects over a 30-day measurement period. Error bars show the median absolute deviations. Partial autocorrelations show thatonly a 1-day lag is at a significant level (dashed horizontal lines show 95% confidence limits).


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as the human body water pool is fractionated22 becauseof the evaporative loss of water. 37

Modeling of the precision implications to DLW calculationsThe impact of uncertainty in the isotope measurements and thefinal background abundances on the precision of the final CO2(and thus TEE) measurement was dependent on the washoutratios (ko/kd) and on the experimental duration (Figure 5). The bestprecision was achieved with the highest washout ratio (1.4)corresponding to the highest TEE. A high washout ratio createsrapid divergence of the two isotope enrichments, which are hencemore easily distinguished from one another. The effect ofexperiment duration on precision showed a U-shaped curve; thisreflects a well-known trade-off within DLW studies. At shortdurations, the precision is governed by a lower ability todistinguish the washout curves, and at longer durations by thecloser approach to an unknown background. This modelingsuggests that the optimal duration for DLW studies is around3 half-lives of the deuterium isotope (about 2 weeks for mostsubjects) for the modeled washout rates using the backgroundisotope abundance fluctuations measured in this study.The impact of using 17O to improve predictability of the

background isotope abundance was to reduce imprecisionbetween one-third and one-half for all five modeled washoutratios at a constant isotope dose. Figure 5a shows the results withstandard DLW analysis and with 17O to improve backgroundisotope prediction. The error curve for the triple-isotope DLW

model was also markedly flatter for all washout ratios reflectingthe improved precision in background abundances, suggestingthat the optimal experimental durations might encompass a widerwindow using the proposed approach.An alternate use of the improved predictability of the

background isotope abundance would be to lower the initialisotope dose. Figure 5b shows the results with standard DLWanalysis (that is, full dose, no background isotope prediction) andwith half the initial isotope dose and 17O to improve backgroundisotope prediction. The error curve for the half-dose, triple-isotopeDLW model shows only a small increase in uncertainty (halvingthe dose normally produces nearly a doubling of the uncertainty).Table 1 summarizes the results of the modeling both with andwithout the triple-isotope DLW method at the duration thatproduced the minimum errors, 14 days. To avoid any confusion,we should highlight that this triple-isotope method is not thesame as the suggested triply labeled method advocated byHaggerty et al., which involves dosing with 17O at the same timeas 18O with the purpose of trying to directly measure evaporativewater loss.38

17O Enrichments of 18O-enriched watersThe enrichment of 17O concurrent with 18O enrichment will bean important factor in the ability to utilize measurements of17O background abundance fluctuations to correct for backgroundabundance fluctuations of 18O and 2H. Enriched 18O water (IconIsotopes, Mt Marion, NY, USA) of 10 atom percent excess (APE), as

Figure 3. Pearson correlation coefficients for pairs of subjects for δ2H (a), δ18O (b) and δ17O (c) in human urine. Each pixel represents themagnitude of the Pearson correlation coefficient for the two different subjects (x,y) for all samples collected on overlapping calendar days.White space indicates that the two subjects did not have urine collected on at least five overlapping calendar days. The insets showhistograms of the magnitudes of the correlation coefficients and the best-fit Gaussian distribution to the histogram data.


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is commonly used in DLW experiments, was measured by OA-ICOSto have a 17O content of 0.5%. This level of enrichment wouldproduce an initial enrichment of about 7.2 p.p.m. excess 17O at thestandard dosing protocol for 18O enrichment. The relativeenrichment of 17O to 18O can be reduced by purchasing waterhighly enriched in 18O, although this option is more expensive perdose than the standard 10 APE 18O water. 95 APE 18O water waspurchased (CortecNet, Mill Valley, CA, USA) and the 17Oenrichment was measured by OA-ICOS to be 1.1%. This level ofenrichment would produce a small but measureable initialenrichment of about 1.5 p.p.m. excess 17O at the standard dosingprotocol for 18O enrichment.

DISCUSSIONAutocorrelation analyses show that the background isotopevariations of an individual were only correlated over periods ofup to a few days. Thus, background isotope variations areespecially significant for human (and large animal) DLW studieswhere the study period is significantly longer than the period overwhich the background levels are significantly correlated. Despitethe possibility that differences in behavior between weekday andweekend activities might cause a weekly cycle in isotopeabundance, the data showed no such weekly cycle for any ofthe 40 participants. Timing DLW studies to be exact multiples of7 days, therefore, does not improve the precision of thebackground estimate (although other advantages in terms ofweekend vs weekday energy expenditures might be

observed).39,40 The only theoretical way to take advantage ofthe observed autocorrelation in isotope abundances over timewould be to shorten the duration of the measurement to less than3 days. However, at this short duration, any advantage frompredicting the background isotope level is completely offset bythe imprecision in establishing the divergence of the labels.Cross-correlation analyses showed that the background varia-

tions in the isotope abundances of different individuals, even inthe same city, were not correlated in time, and thus thatmeasurement of one individual’s background fluctuation couldnot be used to estimate the background fluctuation of a differentindividual. It remains possible that if a control subject were pairedwith an experimental subject, and maintained the same patternsof eating and activity, some correlation between individuals mightbe found. However, although this may theoretically be possible, itis not a practical solution in most circumstances, because it wouldnecessitate every individual having his own control, which wouldeffectively double the costs of isotope analysis. The exceptionswhere this might prove a useful approach are where there is aknown directional change in background during the study. Thismight, for example, occur if individuals moved geographicallocation or were involved in controlled dietary interventions suchas parenteral nutrition.The measured ratios of the three isotopes 2H, 18O and 17O were

highly correlated with each other. The measured linear correlationfor all 40 subjects and over the entire study period in δ2H andδ18O (R2 = 0.92) is higher than that found in previous studies(Schoeller, R2 = 0.8320 and Horvitz and Schoeller, R2 = 0.7919), likely

Figure 4. Correlation between δ18O and δ17O (a), δ2H and δ17O (b), δ2H and δ18O (c) and δ’17O and δ’18O (d) for 30-daily urine samples fromeach of 40 human subjects. Each color represents a single individual; the red line is the best linear regression fit to the data in each case;regression lines and correlations are shown. The black line in d is the global meteoric water line for 17O.37


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because of the larger sample size in the present study.Importantly, there was not only an overall correlation includingdata across individuals but within each individual thecorrelations were highly significant. The regression slopesmeasured for δ2H and δ18O agree well with those measuredby Horvitz and Schoeller19 for non-travelers and corroboratetheir suggestion of using a initial enrichment of 8‰:1‰(0.6 p.p.m.:1 p.p.m.) to reduce the errors caused by naturalabundance variations.The theoretical improvements in precision afforded by using

17O measurements to estimate background fluctuations in 2H and18O were modeled, indicating that the proposed triple-isotopeDLW method would reduce by one-third to one-half the averageprecision error on the TEE measurements regardless of washoutratio, thereby significantly reducing the number of subjectsneeded to achieve sufficient statistical power and thus the costof the study. For example, consider a hypothetical study to find aneffect size of 5% between two independent groups with a desiredpower of 80% on a two-sample t-test. Calculations show that witha precision of 6.66% (modeled at 14 days and ko/kd = 1.3) aresearcher would need a total of 58 participants. In contrast, with

the improved, triple-isotope precision of 4.36%, detecting thesame effect size with the same power would require only 26participants. Similarly, with a precision of 10.0% (14 days and ko/kd= 1.2), a researcher would need 130 participants, whereas theimproved, triple-isotope precision of 5.96% would require only 48participants. Using the triple-isotope DLW method would therebyreduce the subject cost (isotope dose and compensation) by afactor of 2.25–2.75.Although the background abundance correlations indicate that

measurements of 17O could in theory be used to estimatethe background fluctuation of 2H and 18O, measurements of18O-enriched waters show that 17O is also enriched in dose waters.Hence, 17O in DLW subjects would not be at backgroundabundance. Using enriched 18O that is not also enriched in 17Owould be the simplest solution to this problem. In fact, 17O gas isused as a precursor to produce products for nuclear magneticresonance spin studies and is produced by extraction of 17Ofrom 5 APE 18O water (Suszczynski C. ISOTEC. PersonalCommunication)41. This process should in theory also result in 5APE 18O water with little to no excess 17O enrichment as a by-product. If the 17O enrichment in this material is indeed atbackground, it would provide a suitable dosing solution forthe types of study envisioned here. The issue would then behow costly this material would be compared with the standard10 APE material that is currently used, relative to the benefits interms of reduced doses or improved precision leading to reducedsample sizes.In this work, we measured natural background variations in 2H,

18O and 17O in water from urine samples collected from 40 humansubjects from the same geographical area using OA-ICOS.Autocorrelation analyses demonstrated that the backgroundisotopes in a given individual were not temporally correlatedover the time scales of typical DLW studies. Cross-correlationanalyses demonstrated that the background variations of differentindividuals were not correlated in time. However, the measuredratios of the three isotopes 2H, 18O and 17O were highly correlated(R2 = 0.89–0.96). The correlation between 2H and 18O within anindividual confirms that use of a dosing ratio of 8‰:1‰ (0.6 p.p.m.:1 p.p.m.) optimizes DLW precision. Theoretical implications forthe possible use of 17O measurements within a DLW study requirefurther study.

Table 1. Precision errors modeled to include both analytical andbackground isotope abundance uncertainty with a 14-daystudy duration for standard DLW analysis, analysis using 17O toimprove predictability of the background isotope abundance,the ‘triple-isotope DLW’ method, with the full isotope dose, andthe ‘triple-isotope DLW’ method, with half the isotope dose

ko/kd StandardDLW

Triple-isotopeDLW, full dose

Triple-isotopeDLW,

half-dose

1.1 20.45 12.45 22.91.15 12.68 8.37 15.41.2 10.05 5.96 11.131.3 6.66 4.36 7.861.4 5.6 3.23 6.055

Abbreviation: DLW, doubly labeled water.

Figure 5. DLW analysis simulations show the precision error of measured TEE versus study duration for standard analysis conditions (both aand b, open symbols) using the measured precision of the analytical instrument to include error due to measurement uncertainty and themeasured background variations to include the error due to background variation. Use of the 17O measurements within the model toestimate and correct the background variations in 2H and 18O, the theoretical triple-isotope DLW method reduces the precision error by one-third to one-half for all washout ratios when the isotope dose remains constant (a, filled symbols). Alternatively, use of the 17O measurementswithin the model produces only a small increase in uncertainty with half of the isotope dose (b, filled symbols).


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CONFLICT OF INTERESTESFB discloses that she is employed by Los Gatos Research, the manufacturer of theOA-ICOS laser absorption spectrometers. SPS discloses that he was formerlyemployed by Los Gatos Research.

ACKNOWLEDGEMENTSWe thank Ruixin Guo for statistical expertise, Susan Fortson for laboratory assistanceand Thomas Owano for helpful discussions. We are particularly grateful to ProfessorBill Wong for providing us with some enriched isotopic standards. This work wassupported by NIH SBIR 1R43DK093362–01.

AUTHOR CONTRIBUTIONSESFB is the principal investigator and involved in the study design, datacollection, sample analysis, data analysis, statistical analysis and manuscriptpreparation. ELM involved in the study design, sample collection, data collectionand manuscript preparation. TS involved in the sample collection and datacollection. SPS involved in the sample analysis and data analysis. JRS involved inthe study design, modeling, data interpretation and manuscript preparation.

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Inter- and intraindividual correlations of background abundances of 2H, 18O and 17O in human urine and implications for DLW measurementsIntroductionMaterials and MethodsInstitutional approvalParticipantsSample collection and preparationOA-ICOS instrumentationOA-ICOS analysis of urine samplesStatistical analysisModeling

ResultsWithin-subject variationBetween-subject variation

Figure 1 Representative time traces of δ2H (pink circles, left axis), δ18O (cyan triangles, right axis) and δ17O (brown diamonds, right axis) in human urine over a 30-day measurement period.Between isotope variation

Figure 2 Median autocorrelation (top) and partial autocorrelation (bottom) coefficients for δ2H (left), δ18O (middle) and δ17O (right) in human urine from 40 subjects over a 30-day measurement period.Modeling of the precision implications to DLW calculations17O Enrichments of 18O-enriched waters

Figure 3 Pearson correlation coefficients for pairs of subjects for δ2H (a), δ18O (b) and δ17O (c) in human urine.DiscussionFigure 4 Correlation between δ18O and δ17O (a), δ2H and δ17O (b), δ2H and δ18O (c) and δ’17O and δ’18O (d) for 30-daily urine samples from each of 40 human subjects.Table 1 Precision errors modeled to include both analytical and background isotope abundance uncertainty with a 14-day study duration for standard DLW analysis, analysis using 17O to improve predictability of the background isotope abundance, the ‘Figure 5 DLW analysis simulations show the precision error of measured TEE versus study duration for standard analysis conditions (both a and b, open symbols) using the measured precision of the analytical instrument to include error due to measurement unA5A6ACKNOWLEDGEMENTSA7REFERENCES

inter- and intraindividual correlations of background ...€¦ · original article inter- and...

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