Urine Hydration Science and Guide
Urinary biomarkers are some of the most commonly used markers of hydration status. That being said, the science of hydration is complex. A variety of hydration markers exist with their own respective pros and cons. This is because each marker is measuring a direct or indirect impact of a different physiological mechanism involved with fluid balance. And in fact, there isn't even a universally accepted clinical definition of dehydration, further complicating quantitative measurements.
This guide will provide an overview of the dynamic and still-changing field of hydration science and the biomarkers used to measure hydration with an emphasis on urine-based biomarkers and applications primarily related to hydration status in healthy adults involved in strenuous physical activity like athletes, laborers, and warfighters.
Urine and Hydration
Daily Fluctuations in Urine Hydration Biomarkers
One reason measuring hydration status is difficult is because it is dynamic. Hydration status fluctuates throughout the day.
You can see this fluctuation visually in the chart below created from a 2013 study by Erica Perrier, et al. in which 52 adults were broken into two groups - “high” water drinkers and “low” water drinkers (Perrier, 2013). After establishing a baseline at their respective drinking levels, they switched groups, all the while collecting biomarkers of hydration status including urine osmolality, urine specific gravity, urine color, and salivary osmolality. You’ll notice the sharp switch in line plots at the start of the intervention, signaling the change in drinking quantity and a parallel shift in each urine biomarker.
What you’ll also notice is that at steady state for each group, there is significant variability within each day, signaling the substantial circadian rhythm associated with hydration status and biomarkers of hydration status.
This fluctuation in hydration status is due to a number of factors, including some obvious ones like what, and when, you eat, drink, and exercise. After all, fluid consumption results in changes to biomarkers of hydration status, including urinary biomarkers (Armstrong, 2010; Armstrong, 2012; Perrier, 2013).
But even if you control for these factors, there is still variation in hydration status that has largely been contributed to variation in hormones, like arginine vasopressin, produced by the body and suppresses urine output overnight (George, 1975). In fact, these changes in hormone levels overnight are part of the explanation for why first morning voids overshoot hydration status.
Interestingly, Perrier, et al. found no significant difference in saliva osmolality between high and low water drinkers.
The First Morning Void - Fact vs. Myth
Given that hydration indices of all kinds fluctuate throughout the day, it is not surprising that test timing is an important factor for any spot test.
The "First Morning Void" has become the de facto standard for spot urine assessment (McDermott, 2017). The reasons for doing so are chiefly centered around one important aspect - consistency (Cheuvront, 2015). The consistency of always using a test result from the first urination of the day helps to control for much of the within-day fluctuations of hydration indices, and the fact that most individuals are asleep for many hours prior to this test further helps control for variability in recent foods consumed, fluids ingested, and activity levels.
Therefore, given a lack of personalized trend data, a first morning void will likely provide the highest level of consistency in providing an assessment of hydration status.
Or will it?
One problem with first morning void urine samples is that they will overshoot your hydration status. That is, your urine will appear more concentrated and have higher USG values relative to your true hydration status.
This is exemplified in Figure 1. As "high" and "low" water drinkers exhibit fluctuations around higher and lower hydration indices demonstrates the prominent use of "24-hour pooled" urine samples as a means of smoothing out these fluctuations in an attempt to get a closer value of one's homeostatic hydration status. In Figure 1, noticeable spikes near the start of each day represent higher-than-average urine hydration values.
Morning urine indices have also been investigated against 24-hour pooled samples, which again, provide a 24-hour average that eliminates circadian rhythm variability, among 59 men over 12 days of testing and again demonstrated morning samples are significantly higher (Perrier, 2013) and does not correlate significantly to total water intake in habitual low and high water drinkers (Perrier, 2013).
The tendency for first morning urine voids to be biased higher than your true hydration status is in part explained by the earlier point that the production of the hormone arginine vasopressin is increased overnight and results in fluid retention and thus higher urine concentration in the morning. In other words, your morning urine sample is more likely to make you appear more dehydrated than you actually are.
In an attempt to find a more appropriate spot sample indicative of true hydration status, a 2016 study by Bottin, et al. investigated spot urine sample values among 41 men and 41 women and compared them against 24-hour pooled averages (Bottin, 2016). They found that urine samples collected in mid to late afternoons were equivalent to 24-hour pooled sample values for USG and osmolality and echo the conclusions of Perrier, et al. Similarly, a 2020 study by Suh, et al. investigated 541 healthy children and also demonstrated that first morning samples were high and afternoon spot samples were equivalent to 24-hour pooled samples for urine osmolality (Suh, 2020).
This all tells us that urinary biomarkers of hydration status fluctuate throughout the day, and that first morning void values for USG and osmolality are higher than the daily average.
How about urine color?
Dehydrated Urine Color
Regarding the discrepancy between first morning voids and 24-hour collections, Figure 1 clearly demonstrates a similar pattern for urine color. Yet, as analyzed by Armstrong, et al. (Armstrong, 2010), this discrepancy did not reach the level of statistical significance.
Although the lack of statistical significance with urine color may represent an interesting fundamental difference between these urine indices, this may also be attributable to the imprecise nature of color data.
The urine color data collection method is a simple visual categorization against a relatively “imprecise” 1-7 integer scale. In 2022, Wardenaar, et al. experimentally tested the environmental lighting conditions on the accuracy of urine color scoring and demonstrated a darker lighting environment could bias urine color shades by 2 color units (Wardenaar, 2022).
Wardenaar, et al. also demonstrated in 2021 and 2022 that the skill associated with classifying urine colors visually carries some degree of error even in consistent lighting conditions (Wardenaar, 2021; Wardenaar, 2022).
Needless to say, other factors that could affect urine color chart classification could include variations in urine color charts, print paper or screen color variability, and urine collection cup material and size which all could influence color perception and correct classification (Wardenaar, 2021).
This all helps explain the conclusions of a 2021 systematic review that urine color is an acceptable urinary biomarker for hydration monitoring and diagnostic for dehydrated status, yet more evaluations should be done regarding the ability to properly classify and use this biomarker in real-life settings (Kostelnik, 2021).
This lack of precision and standardization of urine color as a hydration biomarker has likely held back the progression of research. Standardized, controlled urine color evaluations, when compared against USG and osmolality, actually reveal interesting insights that are not fully appreciated by practitioners measuring hydration status likely because of this lack of standardization.
For example, a 1998 study by Armstrong, et al. investigated urinary biomarkers of hydration status during dehydrating exercise and rehydration among 9 individuals over a 42-hour period (Armstrong, 1998). When measuring against the usual gold standard for total body water turnover, body mass change, the authors noted that “urine color tracked body water loss as effectively as, or more effectively than, urine osmolality and USG (Figure 3).”
In another study investigating urinary biomarkers’ diagnostic capability for detecting dehydration as defined as 2% loss in body mass, McKenzie et al. demonstrated in a study on 22 healthy, active men that urine color had a higher Area Under the Curve (AUC) in Receiver Operating Characteristic (ROC) analysis, a typical diagnostic analysis used among medical diagnostic device testing for assessing diagnostic quality using sensitivity and specificity, compared to USG (McKenzie, 2015).
These studies would seem to suggest that urine color could be superior to USG and osmolality when it comes to tracking the gold standard for hydration assessment (body mass change).
However, as all things related to the dynamic and complicated task of measuring hydration status, it is not so straightforward. The cause of dehydration, along with environmental conditions and multiple variables associated with the individual’s physiology at the time of testing, all contribute to the variety of research findings that seem to, at times, contradict each other.
Passive vs. Active Dehydration
One source of variability among USG and osmolality when compared to urine color is that these biomarkers represent slightly different physiological responses.
Regarding the physical measurement itself, USG and osmolality are often very closely related. Osmolality is a measure of all solutes in solution, while USG similarly measures solute concentration but will be biased towards the relative weight of solutes (i.e. larger molecules will push USG higher than smaller solutes).
Color is largely interpreted by the concentration of urochrome, which gives urine its yellowish color.
Fluid and solute concentration are controlled by a variety of different mechanisms the body has adapted to maintain fluid balance and keep blood plasma osmolality within a tight window. Arginine vasopressin is a hormone that largely controls fluid balance, while aldosterone largely controls sodium balance (Armstrong, 1998).
Because the number and interplay of various organ systems involved in maintaining blood plasma volume under different kinds of stress, the various physiological markers of hydration vary in their relevance. In other words, the accuracy and appropriateness of the biomarker you choose to assess hydration status depends on the circumstances within which you are testing.
For example, the reason for experiencing dehydration, whether it be passively over time or actively during intense physical activity, changes the appropriateness of some of these different biomarkers (Munoz, 2013).
During passive dehydrating conditions only urinary biomarkers indicate total body water loss (Munoz, 2013). In a 2013 study by Perrier, et al., individuals who habitually drank on average 2 liters of water per day more than others still had similar blood plasma osmolality. On the other hand, during acute, intense physical activity, saliva osmolality and blood serum osmolality are the most accurate, along with serial measurements of USG, body mass change, and serum osmolality.
Passive dehydration naturally progresses at a slower rate than active, exercise-induced dehydration. This allows various fluid systems time to equilibrate as much as possible. Conversely, active dehydration during exercise causes more exaggerated changes to systems that regulate fluid retention and electrolyte distribution for thermoregulatory control and blood volume maintenance. This helps explain, in part, why biomarkers like urinary indices are more effective at monitoring hydration status during passive conditions, while biomarkers like blood plasma or serum osmolality are better during active dehydrating conditions.
Another reason active dehydration monitoring becomes more challenging is due to increases in a number of individualized, physiological parameters that affect variability in all of these biomarkers. For example, the degree of fitness level as well as your degree of heat acclimation are two significant drivers of biomarker variability for hydration monitoring.
An Automated Urine Hydration Chart
Intake Health's InFlow product provides automated urinalysis for hydration assessment in real-time via color analysis (Bender, 2022). Automation and control of urine color assessment removes many of the shortcomings and pitfalls previously encountered with urine color measurements described above. Particularly,
Variable lighting control: Variable external lighting changes the way colors look. InFlow performs the color analysis internally, shielding external light variability.
Variable container size: Color appears different when looking at different volumes. InFlow standardizes the urine volume during each measurement.
Variable urine color comparison charts: Either different sources of color charts or different printer or paper quality can produce variable comparison charts. InFlow measures color directly for consistent comparisons.
Variability in individual skill and physiology: Individuals have variability in vision quality (color-blindness) as well as skill in observing and matching colors. InFlow automates this process and removes user burden and requirement of manual color-matching.
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