Environment International 156 (2021) 106656
Available online 29 May 2021
0160-4120/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Urinary specific gravity measures in the U.S. population: Implications for the adjustment of non-persistent chemical urinary biomarker data
Jordan R. Kuiper
a,*, Katie M. O ’ Brien
b, Kelly K. Ferguson
b, Jessie P. Buckley
a,caDepartment of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
bEpidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
cDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
A R T I C L E I N F O Handling Editor: Dr. Lesa Aylward Keywords:
Bias Creatinine Specific gravity Urinary dilution Non-persistent chemicals
A B S T R A C T
Background: Urinary biomarkers are often corrected for sample dilution using creatinine, which is influenced by sociodemographic factors and certain health conditions. It is unknown whether these factors similarly influence specific gravity.
Objectives: To identify predictors of specific gravity and creatinine and compare methods for correcting estimated chemical concentrations for sample dilution using these measures.
Methods: We assessed predictors of urinary specific gravity and creatinine among NHANES 2007–2008 partici- pants (n = 7257). We corrected concentrations of mono-n-butyl phthalate (MnBP) for dilution using two methods, each applied to both specific gravity and creatinine: correction using a sample mean of the dilution indicator (i.e., specific gravity or creatinine) and covariate-adjusted standardization. We compared distributions and assessed the agreement of uncorrected or corrected concentrations visually using Bland-Altman plots and statistically by Kendall’s τa. We stratified all analyses by age category (i.e., 6–19 or 20+years of age).
Results: Gender, race/ethnicity, body mass index, and height were associated with urinary specific gravity and creatinine. Distributions of corrected MnBP concentrations were comparable for both methods and dilution in- dicators, but agreement between methods was greater for specific gravity. Additionally, specific gravity- and creatinine-corrected MnBP concentrations had slightly greater agreement with each other when corrected using a covariate-adjusted standardization method.
Discussion: Specific gravity, like creatinine, is associated with sociodemographic and body composition variables.
Accounting for these factors as part of the dilution correction method may be important to minimize bias.
1. Introduction
Epidemiologic studies of the health effects of environmental chem- icals often estimate exposure by measuring urinary biomarker concen- trations. Urine samples are often inexpensive and easy to collect and they are appropriate media for measuring non-persistent chemicals with short half-lives (Barr et al., 2005; Calafat et al., 2015; Smolders et al., 2009). However, urinary biomarker concentrations are affected by hy- dration status (Barr et al., 2005; Boeniger et al., 1993; Elkins et al., 1974;
Levine 1945; Middleton et al., 2016; O’Brien et al., 2016). Two proxies of hydration status, creatinine and specific gravity, are routinely used by
researchers to account for differential urinary dilution across samples from study participants (Barr et al., 2005; Boeniger et al., 1993; Mac- Pherson et al., 2018; O’Brien et al., 2016). While creatinine is a waste product from skeletal muscle which is filtered and excreted by the kid- neys, specific gravity is a measure of the relative density of a substance (e.g., urine sample) compared to water (Boeniger et al., 1993; Elkins et al., 1974). Both relate to hydration status, yet how strongly they correlate with the true hydration of an individual may vary due to the influence of endogenous and exogenous factors (O’Brien et al., 2016).
For example, previous studies have shown time of sample collection (Gaines et al., 2010) as well as age (Barr et al., 2005; O’Brien et al., Abbreviations: BMI, body mass index; CDC, Centers for Disease Control and Prevention; CI, confidence interval; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; cm, centimeter; Cr, creatinine; dL, deciliter; E, exposure; eGFR, estimated glomerular filtration rate; g, grams; IU, international unit; Hg, mercury; kg, kilogram; L, liter; LOD, limit of detection; m, meter; mg, milligram; mL, milliliter; mm, millimeter; MnBP, mono-n-butyl phthalate; ng, nanogram; NHANES, National Health and Nutrition Examination Survey; SD, standard deviation; SG, specific gravity.
* Corresponding author at: 615 N Wolfe St, W7513 Suite, Baltimore, MD 21205, USA.
E-mail address: [email protected] (J.R. Kuiper).
Contents lists available at ScienceDirect
Environment International
journal homepage: www.elsevier.com/locate/envint
https://doi.org/10.1016/j.envint.2021.106656
Received 26 March 2021; Received in revised form 12 May 2021; Accepted 13 May 2021
2016), body mass index (BMI) (Barr et al., 2005), gender (Barr et al., 2005), race (Barr et al., 2005), and ethnicity (Barr et al., 2005) influence urinary creatinine concentrations. While specific gravity has been viewed as an attractive alternative to creatinine, few studies have assessed whether specific gravity systematically differs by factors known to affect urinary creatinine. Previous, limited studies have shown uri- nary specific gravity measures differ by age (Xia et al., 2014), body mass (Elkins et al., 1974; MacPherson et al., 2018), time of sample collection (Gaines et al., 2010), biological sex (Carrieri et al., 2001; Nermell et al., 2008), and gender (Xia et al., 2014), suggesting that these and poten- tially other factors are important when interpreting specific gravity as a measure of urinary dilution.
It is common to adjust urinary biomarkers for creatinine by dividing the biomarker concentration by the concentration of urinary creatinine (i.e., resulting units are weight of analyte to weight of creatinine) (O’Brien et al., 2017) or to include creatinine as a covariate in the regression model (Barr et al., 2005). A recent simulation study that compared several different methods for correcting urinary biomarkers using creatinine found that methods directly accounting for known predictors of creatinine yielded the least biased estimates in regression modeling (O’Brien et al., 2016).
A common approach to adjusting urinary biomarkers for specific gravity is based on the formula proposed by Levine and Fahy (Levine 1945) and popularized by Boeniger, Lowry, and Rosenberg (Boeniger et al., 1993). In this approach, biomarker concentrations are multiplied by the ratio of average specific gravity (in the population or in the study sample) to an individual’s specific gravity measure. This approach yields corrected concentrations on the original units but does not explicitly account for systematic differences by endogenous or exogenous factors (Boeniger et al., 1993). While some investigators have adjusted for specific gravity as a covariate in regression models (Gaines et al., 2010), as has been done with creatinine (Barr et al., 2005), the above Levine and Fahy/Boeniger method for specific gravity has not commonly been applied to creatinine.
As such, we proposed to address the following questions: (1) Is specific gravity affected by the same factors as urinary creatinine? and (2) If so, should methods used to adjust for urinary dilution using creatinine also be applied to specific gravity?
2. Methods 2.1. Study population
We used data collected during the 2007–2008 cycle of the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, population-based survey of the non-institutionalized U.S. population conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC). NHANES selects participants using a multi-stage, stratified-random sampling procedure; is nationally representative; and includes questionnaires as well as physical exami- nations (Centers for Disease Control and Prevention (CDC), 2009a). We used the 2007–2008 cycle as it is the only one to include measures of both urinary creatinine (n =7878) and specific gravity (n =7387). In analyses comparing biomarker correction methods, we further restricted analyses to individuals constituting the random one-third sample selected for measurement of phthalate metabolites (n =2718).
2.2. Urinary measures of hydration status
Urine samples were provided by all NHANES participants at least six years of age during the physical examination in the mobile examination center (MEC). Details of the urine collection process are provided else- where (Centers for Disease Control and Prevention (CDC), 2007).
Briefly, participants were randomly assigned to an examination session
as soon as possible during the examination if unable to provide it at the initiation of the exam. Methods for quantification of specific gravity (Xia et al., 2014) and creatinine (Centers for Disease Control and Prevention (CDC), 2009b) in NHANES have been described previously. Briefly, specific gravity of urine specimens was assessed using a digital hand- held refractometer (ATAGO PAL-10S) with automatic temperature compensation and calibrated to 1.000 with HPLC grade water, cali- brated after each batch, and quality control was determined by including a blank and two pooled samples in each batch. Specific gravity measures were available for participants six years of age or older with at least 0.3 mL of urine available (n =7387) (Xia et al., 2014). Urinary creatinine concentration (mg/dL) was assayed in a subsample of 7878 individuals six years of age or older using an enzymatic method (Roche/
Hitachi Modular P Chemistry Analyzer) described elsewhere (Centers for Disease Control and Prevention (CDC), 2009b). Laboratory results met the criteria for accuracy and precision determined by the assurance program of the Division of Laboratory Sciences, National Center for Environmental Health, CDC.
2.3. Predictors of creatinine and specific gravity
We identified potential predictors of specific gravity and creatinine based on prior literature (Barr et al., 2005; MacPherson et al., 2018; Xia et al., 2014). Sociodemographic variables were collected by self- reported questionnaires while height, weight, and urine sample collec- tion characteristics (i.e., time of day) were collected by trained staff as part of the assessment performed at the mobile examination center.
Sociodemographic variables included age (years), self-identified gender (male, female; no other options given), self-identified race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic/Mexican-American, and other/multi-racial), BMI (kg/m2), and height (cm). We included time of day of sample collection (morning, afternoon, evening) as a characteristic of urine sample collection. Health-related variables were determined based on measures taken at the mobile examination center and self-report of physician diagnosis. We classified individuals as having hypertension if they had an average diastolic pressure >90 mmHg, average systolic pressure >140 mmHg, or self-reported physi- cian diagnosis of hypertension on at least two occasions. We classified individuals as having hyperthyroidism if their serum thyroid stimulating hormone level was >5.00 μIU/mL. We classified diabetes based on self- reported physician diagnosis or a blood hemoglobin A1c ≥6.50%. We calculated eGFR based on Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulae (Levey et al., 2009; Schwartz et al., 2009; Stevens et al., 2011).
2.4. Urinary mono-n-butyl phthalate metabolite
Urinary metabolites of certain phthalates were measured in spot urine samples in a random one-third subset of NHANES participants. For this example, we chose to examine mono-n-butyl phthalate (MnBP) due to its high detection frequency (>99%) across all age groups. Details on urine sample collection and metabolite assays are reported elsewhere (Centers for Disease Control and Prevention (CDC), 2010). Briefly, urine samples were collected in mobile examination centers and stored at
− 20 ◦C until they were shipped to the National Center for Environ- mental Health, CDC, and analyzed for MnBP (and other chemicals) using HPLC-electrospray ionization-tandem mass spectrometry. Metabolite concentrations below the limit of detection (LOD) of 0.6 ng/mL were replaced with the LOD/ ̅̅̅
√2
(Hornung and Reed 1990).
2.5. Correction methods for urinary biomarkers
We compared two different approaches to account for hydration
method (based on Boeniger et al., 1993), has been used exclusively with specific gravity to obtain a dilution-corrected concentration for each participant. The estimated exposure, based on the biomarker concen- tration, is given by: Esg =EoxSGSGmediano−1−1, where Esg is the specific gravity- corrected exposure biomarker (i.e., MnBP) concentration, Eo is the observed exposure biomarker concentration, SGmedian is the median of specific gravity values in the study sample, and SGo is the observed specific gravity value. As the purpose of this transformation is simply to provide a correction factor based on how different the observed level is to the population median, we extended the concept to correct for urinary creatinine: Ecr=EoxCrCrmediano , where Ecr is the creatinine-corrected expo- sure biomarker concentration, Eois the observed exposure biomarker concentration, Crmedian is the median of creatinine concentrations in the study sample, and Cro is the observed urinary creatinine concentration.
The second approach, which we refer to as the “O’Brien” method (based on a covariate-adjusted standardization method proposed by O’Brien et al., (O’Brien et al., 2016)), has to-date been applied exclu- sively to urinary creatinine. This approach obtains a dilution-corrected exposure biomarker value for each participant that additionally ac- counts for relevant covariates (O’Brien et al., 2016). Briefly, as shown by the directed acyclic graph (Fig. S1), dilution indicators (i.e., creatinine and specific gravity) that are often used as proxies of hydration status may also be influenced by other covariates (e.g. age, sex, race/
ethnicity). These covariates, in turn, may be related to the exposure and outcome of interest. For creatinine, the formula given by O’Brien et al., is: Ecr = EoxCrCrpo, where Ecr is the creatinine-corrected exposure biomarker concentration, Eois the observed exposure biomarker con- centration, Crp is the predicted creatinine concentration for each observation based on a prediction model of urinary creatinine, and Cro is the observed urinary creatinine concentration for each observation. We modified the O’Brien et al., formulation for specific gravity as: Esg =
EoxSGSGpo−−11, where Esg is the specific gravity-corrected exposure biomarker concentration, Eois the observed exposure biomarker concentration, SGp
is the predicted specific gravity for each observation based on a pre- diction model of specific gravity, and SGo is the observed specific gravity for each observation.
2.6. Statistical analyses
2.6.1. Predictors of urinary dilution
Our first objective was to determine whether specific gravity was systematically affected by measurable factors. To address this, we used survey-weighted, multiple linear regression to estimate adjusted asso- ciations of potential predictors with specific gravity and creatinine. We hypothesized that predictors would differ by age and as such we strat- ified analyses by age categories: children and adolescents (6–19 years of age) and adults (20+years of age). We z-transformed specific gravity and creatinine within age categories, using all available measures after excluding one observation with a very large specific gravity measure (1.20) and log-transforming creatinine (given it was slightly right- skewed). We restricted regression analyses to observations with mea- sures of both specific gravity and urinary creatinine, z-scores within three standard deviations of the mean for both measures, and no missing data on included covariates (n =2170 and 5087 for children/adoles- cents and adults, respectively). We used robust estimation of variance for all models and calculated adjusted coefficients of determination (R2) for each model.
2.6.2. Correlation and agreement of urinary biomarker correction methods Our second objective was to correct urinary MnBP concentrations via the two above dilution correction methods, using urinary creatinine and
specific gravity, and compare corrected concentrations. For this set of analyses, we additionally excluded one outlying child/adolescent participant with an uncorrected MnBP concentration several orders of magnitude greater than the next highest concentration (101,013 ng/
mL). We estimated Spearman’s correlations between all possible pairs of specific gravity- or creatinine-corrected MnBP concentrations adjusted using the Boeniger or O’Brien methods, within age-group strata (n =741 children/adolescents; n =1704 adults). Given their log-normal distri- butions, we log2-transformed uncorrected and corrected concentrations, then visualized the linear associations between pairs via scatterplots.
While Spearman’s correlation provides a rank-based measure of as- sociation between two measures, it does not provide an indication of agreement between measures. As such, we constructed Bland-Altman plots (Altman and Bland 1983) based on the log2-transformed, uncor- rected and/or corrected urinary MnBP concentrations. Bland-Altman plots generally show the arithmetic mean of the two measures (x-axis) against the arithmetic difference of the two measures (y-axis). However, given the log-normal distribution of uncorrected and corrected urinary MnBP concentrations, we took the anti-log of measurement differences and means, resulting in Bland-Altman plots with the ratio of measure- ments on the y-axis and geometric means on the x-axis. For all plots, we included the line of perfect agreement (i.e., the anti-log of differences between measures =1.0), the line of central tendency/mean bias (i.e., the anti-log of the average difference in measures), and the 95% limits of agreement (i.e., the range in which the differences between measures are considered acceptable). Limits of agreement were calculated as:
xdiff±1.96(sddiff), where xdiff is the average of the log2-differences in measurements and sddiff is the associated standard deviation.
As a statistical measure of agreement, we calculated the Kendall’s τa correlation (Kendall 1938) for all pairwise combinations of uncorrected and corrected urinary MnBP concentrations. Kendall’s τa is interpreted as the difference in probabilities of concordance and discordance for all possible pairs of values resulting from two different measures, with a value of 1.0 indicating perfect agreement, 0.0 indicating no agreement (beyond chance), and − 1.0 indicating perfect disagreement.
Lastly, we applied the same methods to assess the agreement be- tween MnBP concentrations corrected by specific gravity and creatinine, using either the O’Brien method or Boeniger method. Given the O’Brien method is theoretically and demonstrably superior to other dilution adjustment methods when creatinine systematically differs by measur- able factors (O’Brien et al., 2016), we hypothesized that the agreement between specific gravity- and creatinine-corrected urinary MnBP con- centrations would be lower using the Boeniger method and higher using the O’Brien method.
2.6.3. Sensitivity analyses
Several health-related factors that influence hydration status, including hypertension, hyperthyroidism, diabetes, and estimated glomerular filtration rate (eGFR), are not routinely collected by all studies (Barr et al., 2005). As such, we included these variables in
“extended” regression models which also included the base socio- demographic and sample collection variables. To ensure the base and extended models included the same set of observations, we accounted for missing data in the additional predictors (i.e., hypertension, hyper- thyroidism, diabetes, and eGFR) using full information maximum like- lihood (FIML) (Enders, 2001). As an additional sensitivity analysis to investigate potential age-specific differences in the relations of cova- riates with urinary dilution, we estimated associations of the selected covariates from the base models within age-group strata of adults.
Specifically, we performed analyses within three strata consisting of individuals: 20–39 years, 40–59 years, or 60+ years of age. We per- formed all analyses using Stata v15.1 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC).
3. Results
3.1. Overview of study population
Survey-weighted, descriptive statistics of adult NHANES participant sociodemographic and health status characteristics are reported in Table 1. The median age was 46 years, approximately half of partici- pants identified as female, and nearly 69% of participants identified as non-Hispanic White (Table 1). Median (interquartile range [IQR]) spe- cific gravity value was 1.017 (1.011, 1.022) while median urinary creatinine concentration was 109 mg/dL (62, 164) (Table 1). As a comparison to the often reported “acceptable/normal adult range” of creatinine concentrations in urine (30 mg/dL–300 mg/dL) (Barr et al., 2005; World Health Organization, 1996; Yeh et al., 2015): 364 (7.2%) of adults included in the study sample (i.e., not survey-weighted) had a
creatinine concentration <30 mg/dL while 136 (2.7%) had a creatinine concentration >300 mg/dL. For those with a creatinine concentration <
30 mg/dL, specific gravity ranged from 1.001 to 1.036 (median = 1.005), while for those with a creatinine concentration >300 mg/dL, specific gravity ranged from 1.017 to 1.037 (median = 1.028).
Descriptive statistics for children/adolescents are provided in Supple- mental Material Table S1.
3.2. Predictors of specific gravity and creatinine
Among adults, female gender was associated with lower specific gravity (β = − 0.34, 95% CI: − 0.46, − 0.23) and urinary creatinine (β =
− 0.43, 95% CI: − 0.54, − 0.31) z-scores. Urinary specific gravity and creatinine z-scores were higher among non-Hispanic Blacks, Mexican- Americans, and other Hispanics, compared to non-Hispanic Whites.
Additionally, greater BMI and height were associated with higher spe- cific gravity and creatinine z-scores. There was also a tendency for creatinine z-scores to be lower in afternoon and evening collection (compared to morning) while specific gravity z-scores were higher for evening collection. Together, these covariates explained 12.9% and 18.7% of the variation in specific gravity and creatinine z-scores, respectively (Table 2).
In sensitivity analyses using the “extended model” (adults only), diabetes (n =836) was associated with higher specific gravity z-scores (β =0.27, 95% CI: 0.14, 0.39) while hyperthyroidism (n =194) was modestly associated with lower specific gravity (β = − 0.18, 95% CI:
− 0.42, 0.058) and creatinine (β = − 0.23, 95% CI: − 0.48, 0.015) z-scores (Supplemental Material Table S2). While not associated with specific gravity, each 5 mL/min/1.73 m2 increase in eGFR from the mean was associated with lower urinary creatinine z-scores (β = −0.077, 95% CI:
− 0.095, − 0.059). Including these additional covariates only marginally increased the proportion of variance explained to 13.6% and 20.2% for specific gravity and creatinine, respectively (Supplemental Material Table S2). In sensitivity analyses of age-stratified models (using selected covariates from the base models), we observed some modest differences in associations across age-group strata, with the 40–59 years age-group strata having the highest proportion of variance explained by the covariates for both specific gravity (14.9%) and creatinine (20.3%) (Supplemental Material Table S3).
Among children/adolescents, female gender was associated with lower specific gravity (β = − 0.30, 95% CI: − 0.41, − 0.19) and urinary creatinine z-scores (β = − 0.17, 95% CI: − 0.25, − 0.10). Non-Hispanic Black race/ethnicity (compared to non-Hispanic White), greater height, and afternoon or evening collection (compared to morning) were associated with higher z-scores (Supplemental Material Table S4). Each 1-year increase in age was associated with a 0.037 (95% CI: 0.009, 0.066) standard deviation (SD) higher creatinine z-score but a 0.026 (95% CI: − 0.057, 0.005) SD lower specific gravity z-score. These covariates explained 5.4% of the variation in specific gravity z-scores and 20% of variation in creatinine z-scores (Supplemental Material Table S4).
3.3. Comparison of urinary biomarker correction methods, distributions and correlations
Distributions of urinary MnBP concentrations uncorrected and cor- rected for dilution using both correction methods for specific gravity and creatinine are shown in Supplemental Material Table S5. The geometric means and geometric standard deviations of urinary MnBP concentra- tions were comparable for uncorrected and corrected concentrations.
However, concentrations corrected using creatinine and the O’Brien method had the lowest geometric mean and standard deviation, in both age groups (Supplemental Material Table S5).
Among adults, Spearman’s correlations between all pairwise com- Table 1
Survey-weighted, sociodemographic and health status characteristics of adult (20+years of age) NHANES 2007–2008 participants with available urinary specific gravity and creatinine measures (n =5162).
Characteristic Median (IQR)yor N (%)
Age (years) 46.0 (33.0, 58.0)y
Height (cm)a 168.4 (161.6, 176.3)y
Specific gravity 1.017 (1.011, 1.022)y
Urine creatinine (mg/dL)b 109.0 (62.0, 164.0)y
eGFR (mL/min/1.73 m2)c 97.2 (82.2, 111.5)y
Gender
Male 2556 (48.6)
Female 2606 (51.4)
Race/ethnicity
Non-Hispanic White 2363 (68.5)
Non-Hispanic Black 1104 (11.8)
Mexican-American 902 (8.6)
Other Hispanic 576 (4.9)
Other / multi-racial 217 (6.2)
BMI (kg/m2)
Underweight (<18.5) 86 (1.6)
Normal weight (18.5–<25) 1384 (30.4)
Overweight (25–<30) 1749 (34.1)
Obese (30+) 1887 (33.9)
Missing 56
Examination session
Morning 2514 (48.0)
Afternoon 1854 (34.0)
Evening 794 (18.0)
Diabetesd
No 4111 (88.5)
Yes 836 (11.5)
Missing 215
Hyperthyroidisme
No 4590 (95.7)
Yes 194 (4.3)
Missing 378
Hypertensionf
No 4461 (92.1)
Yes 511 (7.9)
Missing 190
Abbreviations: BMI = body mass index; dL = deciliter; eGFR = estimated glomerular filtration rate; g =gram; IQR =interquartile range; kg =kilogram; L
=liter; m =meter; mg =milligram; mL =milliliter; min =minute; NA =not available
Note: N’s reflect the study sample while percentages reflect the survey-weighted, source population
aMissing for n =49
b 1 mg/dL =0.01 g/L.
cMissing for n =294; estimated using the Chronic Kidney Disease Epidemi- ology Collaboration (CKD-EPI) formula.
dBased on self-report of physician diagnosis or HbA1c ≥6.50 at examination.
eBased on thyroid stimulating hormone level >5.00 at examination.
fBased on average diastolic pressure >90 mmHg or average systolic pressure
dilution indicator (Fig. 1A–C). Correlation was near perfect (ρ =0.98) between concentrations corrected using the Boeniger and O’Brien methods (Fig. 1C). Correlations between uncorrected concentrations and corrected concentrations (either Boeniger or O’Brien) were considerably lower when creatinine was used as the dilution indicator (Fig. 1D and E) and modestly lower between the Boeniger and O’Brien corrected concentrations (Fig. 1F). Similar correlations and trends were noted among children/adolescents (Supplemental Material Fig. S2A–F),
with the correlation between corrected concentrations (Supplemental Material Fig. S2C and S2F) being higher when specific gravity (ρ =0.99) was used instead of creatinine (ρ =0.92).
3.4. Urinary biomarker correction methods, agreement
Among adults, there was moderate agreement between corrected urinary MnBP concentrations and the uncorrected values (Fig. 2A, B, D,
Fig. 1.Scatter plots of urinary MnBP concentrations (ng/mL) uncorrected or corrected for urinary dilution using the O’Brien or Boeniger method, by dilution factor (specific gravity =blue, creatinine =red) among adults (n =1704): (A) specific gravity, uncorrected vs. Boeniger method, (B) specific gravity, uncorrected vs.
O’Brien method (C) specific gravity, Boeniger method vs. O’Brien method (D) creatinine, uncorrected vs. Boeniger method, (E) creatinine, uncorrected vs. O’Brien method, (F) creatinine, Boeniger vs. O’Brien method. Solid black line =line of agreement, dashed black line =line of best fit. Size of circle markers are proportional to NHANES 2007–2008 sampling weight for participant. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Table 2
Multivariable associations of selected predictors with urinary specific gravity and creatinine z-scores among NHANES participants aged 20+years (n =5087).
Characteristic Specific gravity Creatinine
Age (mean-centered; years) −0.010 (−0.012, −0.007) − 0.012 (− 0.014, − 0.010)
Gender
Male Ref Ref
Female −0.34 (− 0.46, − 0.23) − 0.43 (−0.54, −0.31)
Race/ethnicity
Non-Hispanic White Ref Ref
Non-Hispanic Black 0.37 (0.26, 0.48) 0.48 (0.41, 0.56)
Mexican-American 0.21 (0.090, 0.33) 0.076 (−0.022, 0.17)
Other Hispanic 0.25 (0.15, 0.35) 0.16 (0.051, 0.26)
Other/multi-racial 0.077 (− 0.086, 0.24) − 0.012 (− 0.16, 0.13)
BMI (mean-centered; kg/m2) 0.027 (0.021, 0.032) 0.025 (0.022, 0.029)
Height (mean-centered; scaled per five cm) 0.021 (− 0.002, 0.045) 0.037 (0.014, 0.060)
Examination session
Morning Ref Ref
Afternoon −0.026 (−0.11, 0.055) − 0.15 (−0.24, −0.060)
Evening 0.098 (− 0.015, 0.21) − 80.090 (−0.20, 0.026)
Coefficient of determination (R2) 0.129 0.187
Abbreviations: BMI =body mass index; dL =deciliters; kg =kilogram; m =meter; mg =milligram; min =minute.
Note: Difference in specific gravity and creatinine z-scores (95% confidence interval) estimated in survey-weighted linear regression models.
and E). Agreement between MnBP concentrations corrected using the O’Brien and Boeniger methods was high for specific gravity (τa =0.87) and moderate for creatinine (τa =0.72) (Fig. 2C and F). Similar patterns were noted in analyses among children/adolescents (Supplemental Material Fig. S3).
Lastly, we compared MnBP concentrations adjusted for specific
corrected MnBP concentrations was similar when using the Boeinger method (Fig. 3A; τa =0.71) or the O’Brien method (Fig. 3B; τa =0.74).
Among children/adolescents, the agreement between specific gravity- and creatinine-corrected concentrations was lower using the Boeniger method (τa =0.70, Supplemental Material Fig. S4A) than the O’Brien method (τa =0.77, Supplemental Material Fig. S4B).
Fig. 2. Bland-Altman plots of uncorrected or corrected urinary MnBP concentrations (ng/mL), by dilution factor (specific gravity =blue, creatinine =red) among adults (n =1704): (A) specific gravity, uncorrected vs. Boeniger method (B) specific gravity, uncorrected vs. O’Brien method, (C) specific gravity, O’Brien method vs.
Boeniger method, (D) creatinine, uncorrected vs. Boeniger method, (E) creatinine, uncorrected vs. O’Brien method, (F) creatinine, O’Brien method vs. Boeniger method. Solid black line =line of perfect agreement (i.e., concentrations from both methods are equivalent), dashed grey line =mean bias; the anti-log of the mean difference in concentrations under both methods, and dashed red lines =95% limits of agreement (i.e., the range in which differences between concentrations from both methods are considered acceptable). Kendall’s τa =strength of agreement between concentrations from both methods. Size of circle markers are proportional to NHANES 2007–2008 sampling weight for participant. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3. Bland-Altman plots of specific gravity- and creatinine-corrected urinary MnBP concentrations (ng/mL), adjusted for urinary dilution using the Boeniger or O’Brien method, among adults (n =1704): (A) Boeniger method (B) O’Brien method. Solid black line =line of perfect agreement (i.e., concentrations from both methods are equivalent), dashed grey line =mean bias; the anti-log of the mean difference in concentrations under both method, and dashed red lines =95% limits of agreement (i.e., the range in which differences between concentrations from both methods are considered acceptable). Kendall’s τa =strength of agreement between concentrations from both methods. Size of circle markers are proportional to NHANES 2007–2008 sampling weight for participant. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
4. Discussion
Urinary biomarkers of chemical exposures are subject to a high de- gree of measurement error since measured concentrations are depen- dent on sample dilution, which is itself dependent on urine flow and a person’s hydration status (Barr et al., 2005; O’Brien et al., 2016;
Smolders et al., 2009). In a nationally-representative sample, we showed that specific gravity z-scores differed by age, gender, race/ethnicity, and body composition. We also applied and compared two methods for correcting urinary biomarker concentrations for specific gravity and creatinine: the O’Brien method (O’Brien et al., 2016) and Boeniger method (Boeniger et al., 1993). We found that agreement between correction methods was greater when specific gravity was used as the dilution indicator instead of creatinine. Additionally, the agreement between dilution indicators was comparable when either the Boeniger or O’Brien correction methods was applied, though agreement was slightly higher for the O’Brien method.
In this study, both urinary specific gravity and creatinine were lower among females compared to males; greater among non-Hispanic Blacks, Mexican-Americans, and other Hispanics compared to non-Hispanic Whites; and positively associated with BMI and height. Previous studies have identified similar associations between urinary creatinine and gender (Barr et al., 2005; Carrieri et al., 2001; Xia et al., 2014), race/
ethnicity (Barr et al., 2005; Xia et al., 2014), and BMI (Barr et al., 2005;
MacPherson et al., 2018); as well as specific gravity and gender (Carrieri et al., 2001; Xia et al., 2014), race/ethnicity (Xia et al., 2014), and BMI (MacPherson et al., 2018). None of these studies assessed height as a predictor. These relationships may reflect differences in body compo- sition that are associated with creatinine excretion or urine density.
Creatinine is highly associated with muscle mass, as a result of increased creatine and creatine phosphate metabolism, both of which are stored in skeletal muscle (Boeniger et al., 1993). Urinary specific gravity is affected by the number of molecules in the sample (Boeniger et al., 1993;
Chadha et al., 2001), and as such any factors which increase the con- centration of creatinine in urine would be expected to increase the specific gravity of the sample, though not necessarily in a proportional manner. For example, glucosuria, high concentrations of glucose in urine, is a common consequence of diabetes and therefore the density of the urine sample will likely be increased irrespective of a person’s hy- dration status (Chadha et al., 2001). Accordingly, our sensitivity anal- ysis showed that having diabetes was positively associated with specific gravity, but not creatinine. Importantly, magnitudes of sociodemo- graphic and body composition associations were generally lower and explained less of the variability for specific gravity compared to creatinine.
We observed some marginal diurnal differences in urinary dilution indicators for both children/adolescents and adults. Among adults, specific gravity z-scores were highest in the evening examination session urine samples while creatinine z-scores were highest in the morning samples. Among children/adolescents, both specific gravity and creati- nine were lowest in the morning examination session urine samples.
Other studies have shown that both specific gravity (Carrieri et al., 2001;
Gaines et al., 2010; Pearson et al., 2009) and urinary creatinine con- centrations (Akerstrom et al., 2012; Barr et al., 2005; Boeniger et al., 1993; Carrieri et al., 2001; Gaines et al., 2010) change throughout the day. We lacked detailed information on other sample collection char- acteristics such as time of sample collection, time since last void, and season of sample collection. Further studies evaluating the daily intra- individual variability in urine dilution indicators are warranted.
In this study, we also compared different methods of correcting urinary biomarker concentrations for urinary dilution when using spe- cific gravity or creatinine as the dilution indicator. A prior study showed via simulation that failure to account for factors related to urinary
creatinine and the outcome of interest resulted in biased estimates of the association between the urinary biomarker and outcome of interest (O’Brien et al., 2016). The authors developed covariate-adjusted stan- dardization(i.e., the “O’Brien” method) to explicitly correct urinary biomarker concentrations for these factors and found this method resulted in estimates that were less biased compared with other common correction approaches (O’Brien et al., 2016). The method commonly used for correction with specific gravity (i.e., the “Boeniger” method) (Boeniger et al., 1993; Levine 1945) does not explicitly account for covariates during correction of biomarker concentrations. However, while the Boeniger method is often applied using a target specific gravity measure (e.g., 1.024) (Boeniger et al., 1993) or the mean/median of the study sample (MacPherson et al., 2018; Romano et al., 2017), it is important to note that other studies have addressed systematic differ- ences in specific gravity through modifications of the Boeniger method.
For example, we previously used timepoint-specific medians of specific gravity to correct metabolite concentrations measured at multiple times during pregnancy (Kuiper et al., 2020). By using strata-specific values of specific gravity (or creatinine) for key variables, the Boeniger and O’Brien methods become more similar. However, it is more straight- forward to accommodate multiple key variables (including continuous variables) in the O’Brien method. While we observed that several factors influenced urinary specific gravity values, the magnitudes of association were smaller compared to those with creatinine and explained less of the variability in specific gravity values. Therefore, the potential reduction in measurement error achieved from using a dilution adjustment method that explicitly accounts for the factors we measured (e.g., the O’Brien method) may be smaller for specific gravity compared with creatinine.
There were several notable observations from the agreement of un- corrected and specific gravity-corrected or creatinine-corrected urinary MnBP concentrations, adjusted using the Boeniger or O’Brien methods.
The agreement between methods was consistently higher across all pairwise comparisons in children/adolescents, as opposed to adults.
This is congruent with the observation that less variability in specific gravity and creatinine was explained by the selected predictors in the children/adolescent models, compared to the adult models. Further, the agreement between the Boeniger and O’Brien methods was high when specific gravity was the dilution indicator and consistently greater than when creatinine was used. Taken together, this implies that specific gravity is either a closer proxy for hydration status than creatinine, or there are additional, important predictors unaccounted for by the pre- dictor models (such as measures of metabolic processes and regulation).
Lastly, when we evaluated the agreement between specific gravity- and creatinine-corrected urinary MnBP concentrations, we observed a consistently greater agreement between concentrations when the O’Brien method was used over the Boeniger method, in both children/
adolescents and adults. This finding has important implications for studies in which chemical assay data from multiple sources (e.g., a pooled analysis of multiple cohorts) is to be harmonized, but differ in the dilution indicator used (e.g., some cohorts measured specific gravity while others measured creatinine).
A major strength of this study is the inclusion of individuals from a large, nationally-representative, and diverse sample of individuals across the lifespan. However, this study is not without limitations. First, the 2007–2008 cycle of NHANES does not have detailed information on certain urine sample characteristics which are likely related to exposure and both specific gravity and creatinine (e.g., exact time of sample collection, whether samples were first void, season of sample collec- tion). We did not account for diet as a predictor of urinary creatinine or specific gravity, though previous studies have shown that diet affects measures of urinary creatinine (Barr et al., 2005; Boeniger et al., 1993) as well as serving as a source of phthalate exposures (Serrano et al., 2014). We did not directly account for muscle mass, which is more
strongly related to urinary creatinine than BMI (Boeniger et al., 1993).
As a consequence, the estimated agreement between dilution-correction methods may be greater than expected had these additional variables been included in prediction models using the O’Brien method. Addi- tionally, while our sensitivity analysis showed modest differences in strengths of covariate relations within strata of age-groups among adults, we chose a more parsimonious approach for our correction of urinary MnBP concentrations. As such, our estimates of agreement be- tween dilution methods may be more conservative than would be ex- pected if all sources of effect measure modification were accounted for in the O’Brien method. Lastly, we were unable to make comparisons of specific gravity with other measures of urinary dilution, including osmolality and urine flow rate, which were shown to be more robust measures of urinary dilution (compared to creatinine) in a previous NHANES study (cycles 2009–2010 and 2011–2012) (Middleton et al., 2016). However, NHANES does not currently have a cycle which in- cludes urinary specific gravity, creatinine, and either osmolality or urine flow rate.
5. Conclusion
Urinary specific gravity, like creatinine, differs systematically based on an individual’s gender, race, ethnicity, and body composition. In addition, (1) there was substantial overlap in predictors of specific gravity and urinary creatinine, (2) the agreement between the O’Brien and Boeniger methods were lower when creatinine was used as the dilution indicator, and (3) the agreement between specific gravity- and creatinine-corrected urinary MnBP concentrations were greater when the O’Brien method was used over the Boeniger method. Taken together, we take this as evidence that specific gravity is likely preferable to creatinine as a measure of urinary dilution in studies of urinary chemical biomarkers. Second, a dilution adjustment method that explicitly ac- counts for predictors of creatinine or specific gravity (e.g. O’Brien method) should be considered for dilution adjustment of urinary biomarkers.
Funding
JRK and JPB were supported by NIH OD U24 OD023382 and NIEHS R01 ES030078. KMO and KKF were supported by the Intramural Research Program of the National Institute of Environmental Health Sciences, National Institutes of Health.
CRediT authorship contribution statement
Jordan R. Kuiper: Conceptualization, Methodology, Formal anal- ysis, Writing - original draft, Writing - review & editing. Katie M.
O’Brien: Conceptualization, Methodology, Writing - review & editing.
Kelly K. Ferguson: Conceptualization, Methodology, Writing - review
& editing. Jessie P. Buckley: Conceptualization, Methodology, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envint.2021.106656.
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