The Effect of Hydration on Urine Color Objectively Evaluated in CIE Lab Color Space

Abstract

Urine color has been shown to be a viable marker of hydration status in healthy adults. Traditionally, urine color has been measured using a subjective color scale. In recent years, tristimulus colorimetry developed by the International Commission on Illumination (CIE L*a*b*) has been widely adopted as the reference method for color analysis. In the L*a*b* color space, L* indicates lightness ranging from 100 (white) to 0 (black), while a* and b* indicate chromaticity. a* and b* are color directions: –a* is the green axis, +a* is the red axis, –b* is the blue axis, and +b* is the yellow axis. The L*a*b* color space model is only accurately represented in three-dimensional space. Considering the above, the purpose of the current study was to evaluate urine color during different hydration states, with the results expressed in CIE L*a*b* color space. The study included 28 healthy participants (22 males and 6 females) ranging between the age of 20 and 67 years (28.6 ± 11.3 years). One hundred and fifty-one urine samples were collected from the subjects in various stages of hydration, including morning samples after 7–15 h of water deprivation. Osmolality and CIE L*a*b* parameters were measured in each sample. As the urine osmolality increased, a significant linear increase in b* values was observed as the samples became more pronouncedly yellow (τb = 0.708). An increase in dehydration resulted in darker and significantly more yellow urine, as L* values decreased in lightness and b* values increased along the blue–yellow axis. However, as dehydration increased, a notable polynomial trend in color along the green–red axis was observed as a* values initially decreased, indicating a green hue in slightly dehydrated urine, and then increased as urine became more concentrated and thus more dehydrated. It was determined that 74% of the variance seen in urine osmolality was due to CIE L*a*b* variables. This newfound knowledge about urine color change along with the presented regression model for predicting urine osmolality provides a more detailed and objective perspective on the effect of hydration on urine color, which to our knowledge has not been previously researched.

Full text

ORIGINAL RESEARCH
published: 26 October 2020
doi: 10.3389/fnut.2020.576974
Frontiers in Nutrition | www.frontiersin.org 1October 2020 | Volume 7 | Article 576974
Edited by:
Stavros A. Kavouras,
Arizona State University, United States
Reviewed by:
Juan Del Coso,
Rey Juan Carlos University, Spain
Colleen X. Munoz,
University of Hartford, United States
*Correspondence:
Rebekah Belasco
rbelasco6457@sdsu.edu
Michael J. Buono
mbuono@sdsu.edu
Specialty section:
This article was submitted to
Sport and Exercise Nutrition,
a section of the journal
Frontiers in Nutrition
Received: 30 June 2020
Accepted: 15 September 2020
Published: 26 October 2020
Citation:
Belasco R, Edwards T, Munoz AJ,
Rayo V and Buono MJ (2020) The
Effect of Hydration on Urine Color
Objectively Evaluated in CIE L∗a∗b∗
Color Space. Front. Nutr. 7:576974.
doi: 10.3389/fnut.2020.576974
The Effect of Hydration on Urine
Color Objectively Evaluated in CIE
L∗a∗b∗Color Space
Rebekah Belasco*, Tory Edwards, A. J. Munoz, Vernon Rayo and Michael J. Buono*
School of Exercise and Nutritional Sciences, San Diego State University, San Diego, CA, United States
Urine color has been shown to be a viable marker of hydration status in healthy adults.
Traditionally, urine color has been measured using a subjective color scale. In recent
years, tristimulus colorimetry developed by the International Commission on Illumination
(CIE L∗a∗b∗) has been widely adopted as the reference method for color analysis. In the
L∗a∗b∗color space, L∗indicates lightness ranging from 100 (white) to 0 (black), while a∗
and b∗indicate chromaticity. a∗and b∗are color directions: –a∗is the green axis, +a∗
is the red axis, –b∗is the blue axis, and +b∗is the yellow axis. The L∗a∗b∗color space
model is only accurately represented in three-dimensional space. Considering the above,
the purpose of the current study was to evaluate urine color during different hydration
states, with the results expressed in CIE L∗a∗b∗color space. The study included 28
healthy participants (22 males and 6 females) ranging between the age of 20 and 67
years (28.6 ±11.3 years). One hundred and fifty-one urine samples were collected
from the subjects in various stages of hydration, including morning samples after 7–15 h
of water deprivation. Osmolality and CIE L∗a∗b∗parameters were measured in each
sample. As the urine osmolality increased, a significant linear increase in b∗values was
observed as the samples became more pronouncedly yellow (τb=0.708). An increase in
dehydration resulted in darker and significantly more yellow urine, as L∗values decreased
in lightness and b∗values increased along the blue–yellow axis. However, as dehydration
increased, a notable polynomial trend in color along the green–red axis was observed
as a∗values initially decreased, indicating a green hue in slightly dehydrated urine, and
then increased as urine became more concentrated and thus more dehydrated. It was
determined that 74% of the variance seen in urine osmolality was due to CIE L∗a∗b∗
variables. This newfound knowledge about urine color change along with the presented
regression model for predicting urine osmolality provides a more detailed and objective
perspective on the effect of hydration on urine color, which to our knowledge has not
been previously researched.
Keywords: urine color, urine osmolality, dehydration, color space, CIE L∗a∗b∗

Belasco et al. Objective Urine Color During Dehydration
INTRODUCTION
Urine color has been shown to be a viable marker of hydration
status in healthy adults (1,2). This is evidenced by the significant
correlations (r>0.70) of urine color with several established
physiological measures of whole-body hydration, including
urine osmolality and urine-specific gravity (1,3–5). Urine
color is dictated by the concentration of urochrome, a yellow-
pigmented waste by-product of hemoglobin catabolism (1,6,
7). During whole-body dehydration, urochrome concentration
increases as antidiuresis is stimulated via increased arginine
vasopressin production, thus increasing water reabsorption from
the collecting duct of the nephron. This physiological response to
dehydration results in darker and more concentrated urine (3,4).
Traditionally, the assessment of hydration through urine color
analysis has been performed with a subjective eight-point color
scale (1,3–5). This technique involves visual color matching
of a collected urine sample to a stepwise color chart, resulting
in an assigned value ranging from 1 (pale yellow) to 8 (dark
greenish brown) (3,4). Color assessments based on visual
comparisons to a set of stepwise color samples, also referred to as
color-order systems, are known to have several methodological
limitations. These include the following: (1) variability of color
vision in different individuals, (2) change of color vision with
age, (3) potential color blindness in individuals, (4) difficulty in
standardization of illumination conditions, and (5) difficulty in
standardization of reference color charts (8–10). Furthermore,
evidence of the eight-point color scale’s success in determining
hydration status in older populations is inconclusive (11,12).
Although the eight-point color scale has proven to be a
practical tool for convenient assessment of hydration status
in healthy adults (1,3–5), it lacks the capability to be used
as a universally accurate tool for objective quantification of
urine color.
CIE L∗a∗b∗is a system of objective tristimulus colorimetry
developed by the International Commission on Illumination (i.e.,
Commission Internationale de l’Eclairage, CIE) in which three
different characteristics constitute a color’s position within a
three-dimensional color space. CIE L∗a∗b∗analysis describes all
colors visible to the human eye and was created to serve as a
device-independent reference model. This system has been used
to perform objective analysis of color and color movement in
several different scientific fields, ranging from food science to
medicine. Previous research has used CIE L∗a∗b∗to identify
photosynthetic pigmentation in olive oils, to create ranges of
human gingival color, and to visualize body movement captured
by remote photoplethysmography (13–15). Furthermore, the
CIE L∗a∗b∗color space has been widely adopted as a standard
for color assessment and quantification since its conception
in 1976 (10,14,16).
In the L∗a∗b∗color space, L∗indicates lightness ranging from
100 (white) to 0 (black), while a∗and b∗indicate chromaticity
or the quality of a color independent of its luminance. a∗
and b∗are color directions: –a∗is the green axis, +a∗is the
red axis, –b∗is the blue axis, and +b∗is the yellow axis. As
the L∗a∗b∗color space is a three-dimensional model, it can
only be accurately represented in a three-dimensional space
(Figure 1). These three parameters are measured via spectral
analysis of each wavelength within the visible color spectrum by
a spectrophotometer.
Considering the above, the purpose of the current study was
to evaluate urine color during dehydration, with the results
expressed in CIE L∗a∗b∗color space parameters. As such, this
study was conducted not to replace any current methods of
urine analysis, but rather to provide a more objective perspective
on the effect of hydration on urine color. Such data, which
to our knowledge has not been previously published, would
both advance our understanding about objective measurement
of urine color and aid in the quantification of urine color during
various stages of hydration in humans.
MATERIALS AND METHODS
A cross-sectional study was conducted to examine the
relationship between hydration status and urine color when
visualized in the CIE L∗a∗b∗color space. The subjects for this
study were 28 healthy volunteers (22 males and 6 females)
ranging between the age of 20 and 67 years (28.6 ±11.3 years).
Prior to data collection, all subjects read and signed an informed
consent approved by the San Diego State University Institutional
Review Board. One hundred and fifty-one spot urine samples
were collected from the subjects in various stages of hydration,
including morning samples after 7–15 h of water deprivation.
Diet and vitamin intake were not controlled. Menstrual cycle
status of the female participants was not determined.
Participants arrived at the laboratory in the morning following
an overnight period of water deprivation and provided a urine
sample. Participants were then instructed to rehydrate with 1 to
3 L of water over a period of 4 h. Urine samples were collected
approximately once per hour during the rehydration period,
thus providing samples at various stages of hydration. Urine
samples were collected in plastic containers by the subject and
then immediately analyzed upon retrieval. Subjective urine color
was determined by two investigators using the eight-point urine
color chart (3,4). Urine osmolality was measured in duplicate
using a Wescor Model 5500 vapor pressure osmometer (Logan,
UT). Urine color was measured using a Hunter Lab Vista
spectrophotometer (Reston, VA), and the results were expressed
in CIE L∗a∗b∗color space parameters.
Statistical analyses were performed using IBM SPSS Statistics,
version 26.0. Two-tailed Kendall’s Tau-b correlation analyses
were performed to determine the strength of the relationship
between urine osmolality and the individual CIE L∗a∗b∗values.
Standardized beta coefficient values were also calculated for the
individual CIE L∗a∗b∗components to determine the predictor
variable that was most strongly related to urine osmolality. A
hierarchal multiple regression model was then constructed to
determine the strength of the relationship between the predictor
variables of urine color, as expressed in CIE L∗a∗b∗color space,
and the criterion variable of urine osmolality. The hierarchy of
predictor variables when used in the construction of the multiple
regression model was based on the individual beta coefficient
values. The alpha level for significance was set to α=0.05 a priori.
Frontiers in Nutrition | www.frontiersin.org 2October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
FIGURE 1 | Model illustration of three-dimensional CIE L*a*b* color space. The CIE L*a*b* color space illustrates a color’s objective lightness and chromaticity. The L*
value measures lightness from black to white. The a* and b* values measure chromaticity on the green–red spectrum and blue–yellow spectrum, respectively.
FIGURE 2 | An increase in urine osmolality is significantly correlated with an increase in yellow urine color. A more dehydrated sample, as determined by urine
osmolality readings, was measured as having a higher b* value. This indicates that an increase in dehydration is significantly correlated with increased yellow color
in urine.
RESULTS
A greater urine osmolality reading indicated higher
concentration of urine solutes per kilogram of water and,
thus, represented a more dehydrated urine sample. The urine
osmolality was ∼50–200 mmol/kg following water consumption
and over 1,000 mmol/kg following overnight water deprivation.
As the urine osmolality of the collected samples increased,
a significant linear increase in b∗values was observed as
the samples became more pronouncedly yellow (τb=0.708)
(Figure 2). An increase in urine osmolality was also accompanied
by a significant linear decrease in L∗values as the samples became
Frontiers in Nutrition | www.frontiersin.org 3October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
FIGURE 3 | An increase in urine osmolality is significantly correlated with a decrease in urine sample lightness. A more dehydrated sample, as determined by urine
osmolality readings, was measured as having a lower L* value. This indicates that an increase in dehydration is significantly correlated with darker urine color.
FIGURE 4 | An increase in urine osmolality is significantly correlated with the movement of urine color on the green–red axis. An initial increase in dehydration, as
determined by urine osmolality readings, was accompanied by a decrease in a* value. At a urine osmolality reading of ∼600 mmol/kg, the a* value began to increase.
This indicates a parabolic relationship between hydration status and the value of green–red chromaticity in urine.
darker (τb= −0.567) (Figure 3). The relationship between urine
osmolality and a∗values was determined to be parabolic, with
an initial decrease followed by an increase in a∗values as urine
osmolality increased (τb= −0.375) (Figure 4). Notably, a
significant relationship existed between the b∗value of the CIE
L∗a∗b∗color space and the eight-point urine color score as an
increasing color chart score was associated with a higher b∗value
(τb=0.805) (Figure 5). The mean, standard deviation, and
two-tailed Kendall’s Tau-b correlations for CIE L∗a∗b∗, urine
osmolality, and urine color data are presented in Table 1.
The relationship between urine color and hydration status
was visualized in the three-dimensional CIE L∗a∗b∗color space
(Figure 6). An increase in dehydration resulted in darker and
significantly more yellow urine, as L∗values decreased in
lightness and b∗values increased positively along the blue–yellow
axis (Figure 7). However, as dehydration increased, a notable
polynomial trend in color along the green–red axis was observed
as a∗values initially decreased negatively, indicating the presence
of a green hue in slightly dehydrated urine, and then increased
positively as urine became more concentrated and thus more
dehydrated (Figure 8). Means and standard deviations for CIE
L∗a∗b∗data are presented in Table 1.
Standardized beta coefficient values were then calculated for
each of the three CIE L∗a∗b∗color parameters. This statistical
Frontiers in Nutrition | www.frontiersin.org 4October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
FIGURE 5 | An increase in urine color score as determined by an eight-point color scale is significantly correlated with the movement of urine color on the blue–yellow
axis.
TABLE 1 | Means, standard deviations, and two-tailed Kendall’s Tau-b correlation coefficients for CIE L*a*b*, urine osmolality, and urine color data.
Variable MSD U†Osm (mmol/kg) U‡
Col L* a* b*
U†
Osm (mmol/kg) 538 291 –
U‡
Col 4.62 1.94 0.704* –
L* 95.8 3.4 −0.567* −0.622* –
a* −2.2 1.5 −0.375* −0.440* 0.262* –
b* 18.5 12.2 0.708* 0.805* −0.696* −0.506* –
*p<0.05.
†Urine osmolality abbreviated to UOsm.
‡Urine color abbreviated to UCol.
analysis illustrated the relationship between each CIE L∗a∗b∗
element and urine osmolality. It was determined that b∗was
a significant predictor of urine osmolality (Beta =0.836,
p<0.0001) and that a∗was a significant predictor of urine
osmolality (Beta = −0.104, p<0.029), whereas L∗was not found
to be a significant predictor of urine osmolality (Beta =0.006,
p>0.935) (Table 2). A hierarchal multiple regression model then
was constructed to determine the predictive ability of all three
CIE L∗a∗b∗variables for the criterion of urine osmolality. Based
on the significance of each individual element’s beta coefficient
value, b∗was determined to be the most significant predictor of
urine osmolality, followed by a∗and L∗, respectively.
The hierarchal multiple regression model was determined
to be significant in its prediction of urine osmolality
(F(3,147) =139.574, p<0.0001, adjusted R2=0.735) (Tables 3,
4). Furthermore, it was determined that 74% of the variance seen
in urine osmolality is due to the three predictor variables of CIE
L∗a∗b∗color. The adjusted R2value was used to determine this
percentage of variance, as this value is based upon sample size
and number of predictor values contributing to the regression
model. The coefficients for each predictor value that contributed
to the construction of the hierarchal multiple regression model
have been reported (Table 2).
DISCUSSION
Urine color analysis is an inexpensive and convenient method
of identifying whole-body hydration status. This is important
as monitoring of hydration is essential for the maintenance
of essential physiological function and performance. Acute
dehydration during physical activity can lead to impairments
in cognitive and motor performance and increase feelings of
tension, fatigue, and anxiety, and urine color analysis allows
for a quick field assessment of an athlete’s hydration status to
Frontiers in Nutrition | www.frontiersin.org 5October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
FIGURE 6 | Hydration status significantly determines urine color in the three-dimensional CIE L*a*b* color space. An increase in dehydration results in an increase in
b* and a decrease in L*. The relationship between hydration status and a* is parabolic. A notable shift in a* trend occurs at ∼600 mmol/kg, which has previously been
identified as being in the cutoff range for distinguishing between whole-body euhydration and dehydration (4,17–19).
FIGURE 7 | Hydration status significantly determines the lightness and yellowness of urine color in CIE L*a*b* color space.
estimate performance capacity (20,21). To our knowledge, the
only validated method of assessing urine color in a healthy
adult population is with an established eight-point color chart
that assigns a numerical value of hydration to an identified
color (3,4). Numerous studies using the subjective eight-point
color scale have reported that an increase in subject dehydration
Frontiers in Nutrition | www.frontiersin.org 6October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
FIGURE 8 | Hydration status significantly determines green–red and blue–yellow chromaticity of urine color in CIE L*a*b* color space.
TABLE 2 | Table of coefficients for a constructed hierarchal multiple regression
model illustrating the predictive ability of CIE L*a*b* for urine osmolality.
Model Unstandardized
coefficients
Standardized
coefficients
Significance
Beta Standard error Beta
Constant 74.702 634.485 – 0.906
L* 0.517 6.402 0.006 0.936
a* −19.872 8.943 −0.104 0.028*
b* 19.947 1.834 0.836 0.000*
*p<0.05.
results in darker urine color. For example, Armstrong et al.
(4) reported that whole-body dehydration of −5.2% of body
mass resulted in a significant increase in urine color from a
mean value of 1 to a mean value of 7 on the eight-point scale.
Simultaneously, urine osmolality significantly increased from 110
to 1,080 mmol/kg. However, this technique of color assessment
is methodologically limited and, thus, subject to individual bias.
The present study sought to identify the relationship between
urine color and hydration status within CIE L∗a∗b∗parameters,
a three-dimensional color space that incorporates three different
color characteristics to present an objective visualization of color.
In this study, hydration status was determined by urine
osmolality (1,17). Kendall’s Tau-b correlations revealed a
significant relationship between urine osmolality and each
individual CIE L∗a∗b∗color characteristic, with the strongest
relationship seen between urine osmolality and b∗(Table 1).
The b∗value is a chromaticity coordinate indicating a color’s
position along the blue–yellow axis. As dehydration increases,
the concentration of yellow urochrome in urine increases as
less water is being voided. This physiological response to
dehydration results in both increased urine osmolality and
yellow pigmentation of a sample, which supports the observed
TABLE 3 | Table of the analysis of variance for a constructed hierarchal multiple
regression model illustrating the predictive ability of CIE L*a*b* for urine osmolality.
Model Sum of
squares
df Mean square F-statistic Significance
Regressiona9379312.37 3 3126437.46 139.574 0.000*
Residual 3292784.42 147 22399.894 – –
Total 12672096.8 150 – – –
*p<0.05.
aPredictors: constant, b*, a*, L*.
TABLE 4 | Model summary for a constructed hierarchal multiple regression model
illustrating the predictive ability of CIE L*a*b* for urine osmolality.
Model R R2Adjusted R2Standard error of
the estimate
Regressiona0.860 0.740 0.735 149.666
aPredictors: constant, b*, a*, L*.
relationship between urine osmolality and b∗. The significant
relationship observed between urine osmolality and L∗is also
expected. The L∗value is a measure of a color’s lightness
from white (100) to black (0). Urine samples following water
consumption are dilute in color and will have higher L∗readings
than samples collected following water deprivation which are
more concentrated with urochrome solute.
The parabolic visual relationship and significant correlation
between urine osmolality and a∗were not expected. The a∗value
is a chromaticity coordinate indicating a color’s position along the
green–red axis. An increase in dehydration resulted in an initial
decrease in a∗, suggesting that the urine samples became greener.
However, at a urine osmolality reading of ∼600 mmol/kg, there
is an observed increase in a∗values, suggesting that the urine
samples became less green. Previous studies have proposed a
Frontiers in Nutrition | www.frontiersin.org 7October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
urine osmolality threshold of <700 mmol/kg for indicating
sufficient whole-body hydration (4,17–19). The unexplained
change in a∗occurring at this approximate threshold might be
a result of physiological changes occurring in renal function
as the body is preventing water loss. Alternately, it has been
suggested that pH of the surrounding medium may alter the
absorptive spectra of linear tetrapyrroles like urochrome (22).
Thus, the possibility exists that urine pH and urine osmolality
may also exhibit a parabolic relationship. Certainly, further work
is warranted in the area.
The successful findings in the correlation analysis prompted
the construction of a regression model that suggests predictive
ability of CIE L∗a∗b∗for urine osmolality. This model
determined that 74% of the variance seen in the urine osmolality
of the collected samples was due to the predictor variables of
CIE L∗a∗b∗color (Table 4). This is a powerful finding, as it
further supports the strong relationship observed between these
two measures of urinalysis.
The relationship between urine color and hydration status in
CIE L∗a∗b∗color space was visualized in a three-dimensional
model (Figure 6). CIE L∗a∗b∗provided an objective analysis
of urine color, in which a quantitative change in any or all of
the three-color characteristics resulted in the same change in
visual color perception. Therefore, assessment of urine color
through CIE L∗a∗b∗provides a more complete understanding
of the relationship between urine color and hydration status,
such as the unexpected movement of a∗along the green–red
axis. Such nuances and shifts in tone cannot be identified with
a subjective color chart. The color chart has also shown to
be methodologically limited (8–10) as well as inconclusive for
measuring hydration status in older populations (11,12).
Limitations of the current study include that urine osmolality
was used as the sole marker of hydration status. Currently,
there is great debate in the literature as to what is the best
marker of hydration status, including laboratory measures such
as plasma osmolality, urine-specific gravity, and urine osmolality.
However, past studies have successfully used urine osmolality as
a surrogate of hydration status (1,17). Furthermore, diet and
vitamin intake were not controlled for in the current study, and
these factors have the potential to artificially alter urine color and
urine osmolality. It should also be noted that this dataset was
a time series, as each subject contributed multiple samples over
the course of the collection period, and thus, multiple data points
were obtained from each subject. This violated the assumption of
independent data and was recognized during statistical analysis.
This present study sought to understand and quantify the
relationship between urine color and hydration status when
evaluated in CIE L∗a∗b∗parameters. It was discovered that
significant relationships exist with urine osmolality and each
CIE L∗a∗b∗color characteristic. A hierarchal multiple regression
model for predicting urine osmolality from a color’s position
in the CIE L∗a∗b∗color space was also presented. This
suggests a potential method of determining one’s hydration
status using objective color assessment. To our knowledge,
this study is one of the first to evaluate the use of objective
color measurement to assess hydration. Further research should
examine the relationships between objective color assessment
and other methods of urinalysis that are used to dictate
hydration status.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by San Diego State University Human Research
Protection Program. The patients/participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
MB conceived and designed the research study. MB, RB, TE, AM,
and VR performed the measurements and collected all necessary
data. MB and RB performed the statistical analyses and wrote
the manuscript in consultation with TE, AM, and VR. The full
dataset is available upon request to RB. All authors agree to be
accountable for the contents of the manuscript.
ACKNOWLEDGMENTS
We wish to acknowledge and thank the School of Exercise and
Nutritional Sciences at San Diego State University for supporting
the execution of this research study.
REFERENCES
1. Kavouras SA, Johnson EC, Bougatsas D, Arnaoutis G, Panagiotakos
DB, Perrier E, et al. Validation of a urine color scale for assessment
of urine osmolality in healthy children. Eur J Nutr. (2016) 55:907–15.
doi: 10.1007/s00394-015-0905-2
2. Mckenzie AL, Munoz CX, Armstrong LE. Accuracy of urine color to detect
equal to or greater than 2% body mass loss in men. J Athletic Train. (2015)
50:1306–9. doi: 10.4085/1062-6050-51.1.03
3. Armstrong LE, Maresh CM, Castellani JW, Bergeron MF, Kenefick RW,
LaGasse KE, et al. Urinary indices of hydration status. Int J Spt Nutr. (1994)
4:265–79. doi: 10.1123/ijsn.4.3.265
4. Armstrong LE, Soto JA, Hacker FT Jr, Casa DJ, Kavouras SA, Maresh CM.
Urinary indices during dehydration, exercise, and rehydration. Int J Sport
Nutr. (1998) 8:345–55. doi: 10.1123/ijsn.8.4.345
5. Hahn RG, Waldreus N. An aggregate urine analysis tool to detect
acute dehydration. Int J Sport Nutr Exerc Metabol. (2013) 23:303–11.
doi: 10.1123/ijsnem.23.4.303
6. Ehrig F, Waller S, Misra M, Twardowski ZJ. A case of green urine. Nephrol
Dial Transplant. (1999) 14:190–2. doi: 10.1093/ndt/14.1.190
7. Foot CL, Fraser JF. Uroscopic rainbow: modern matula medicine. Postgrad
Med J. (2006) 82:126–9. doi: 10.1136/pgmj.2005.037598
8. Choudhury AKR. Color order systems. Rev Prog Coloration. (1996) 26:54–62.
doi: 10.1111/j.1478-4408.1996.tb00110.x
Frontiers in Nutrition | www.frontiersin.org 8October 2020 | Volume 7 | Article 576974

Belasco et al. Objective Urine Color During Dehydration
9. Vienot F. Relations between inter-and intra-individual variability
of color matching function. J Opt Soc Am. (1980) 70:1476–83.
doi: 10.1364/JOSA.70.001476
10. Weatherall IL, Coombs BD. Skin color measurements in terms of
CIELAB color space values. J Invest Dermatol. (1992) 99:468–73.
doi: 10.1111/1523-1747.ep12616156
11. Fortes MB, Owen JA, Raymond-Barker P, Bishop C, Elghenzai S, Oliver SJ,
et al. Is this elderly patient dehydrated? Diagnostic accuracy of hydration
assessment using physical signs, urine, and saliva markers. J Am Med Dir
Assoc. (2015) 16:221–8. doi: 10.1016/j.jamda.2014.09.012
12. Hooper L, Bunn DK, Abdelhamid A, Gillings R, Jennings A, Maas K, et al.
Water-loss (intracellular) dehydration assessed using urinary tests: how well
do they work? Diagnostic accuracy in older people. Am J Clin Nutr. (2016)
104, 121–131. doi: 10.3945/ajcn.115.119925
13. Huang J, Chen W, Huang T, Fu P, Lai P, Tsai C, et al. Using a
spectrophotometric study of human gingival colour distribution to develop
a shade guide. J Dent. (2011) 39:e11–e16. doi: 10.1016/j.jdent.2011.10.001
14. Moyano MJ, Melendez-Martinez AJ, Alba J, Heredia FJ. A comprehensive
study on the colour of virgin olive oils and its relationship with
their chlorophylls and carotenoids indexes (II): CIELUV and
CIELAB uniform colour spaces. Food Res Int. (2008) 41, 513–521.
doi: 10.1016/j.foodres.2008.03.006
15. Yang Y, Liu C, Yu H, Shao D, Tsow F, Tao N. Motion robust remote
photoplethysmography in CIELab color space. J Biomed Opt. (2016)
21:117001. doi: 10.1117/1.JBO.21.11.117001
16. Kuehni RG. Industrial color difference: progress and problems. Color Res
Appl. (1990) 15:261–5. doi: 10.1002/col.5080150506
17. Shirreffs S, Maughan R. Urine osmolality and conductivity as indices of
hydration status in athletes in the heat. Med Sci Sports Exerc. (1998) 30:1598–
602. doi: 10.1097/00005768-199811000-00007
18. Perrier ET, Bottin JH, Vecchio M, Lemetais G. Criterion values for
urine-specific gravity and urine color representing adequate water intake
in healthy adults. Eur J Clin Nutr. (2017) 71:561–3. doi: 10.1038/ejcn.
2016.269
19. Popowski LA, Oppliger RA, Lambert GP, Johnson RF, Johnson AK,
Gisolf CV. Blood and urinary measures of hydration status during
progressive acute dehydration. Med Sci Sports Exerc. (2001) 33:747–53.
doi: 10.1097/00005768-200105000-00011
20. Armstrong LE, Ganio M, Casa D, Lee E, McDermott B, Klau J, et al. Mild
dehydration affects mood in healthy young women. Nutr J. (2012) 142:382–8.
doi: 10.3945/jn.111.142000
21. Ganio M, Armstrong LE, Casa D, McDermott B, Lee E, Yamamoto
L, et al. Mild dehydration impairs cognitive performance and mood
of men. Br J Nutr. (2011) 106:1535–43. doi: 10.1017/S00071145110
02005
22. Cole WJ, Heocha CO, Moscowitz A, Krueger WR. The optical activity of
urobilins derived from phycoerythrobilin. European J Biochem. (1967) 8:202–
7. doi: 10.1111/j.1432-1033.1967.tb19516.x
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Belasco, Edwards, Munoz, Rayo and Buono. This is an open-access
article distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with these
terms.
Frontiers in Nutrition | www.frontiersin.org 9October 2020 | Volume 7 | Article 576974