Intra-Person Variation of Urinary Biomarkers of Oxidative Stress and Inflammation

This article is featured in Highlights of This Issue, p. 899

Oxidative stress, the adverse effect of oxidants on physiologic function, has been implicated in the pathogenesis of a variety of human conditions and diseases, such as cancers, cardiovascular disease, neurodegenerative disease, and aging (1, 2). F₂-isoprostanes (F₂-IsoPs) are a unique series of prostaglandin-like compounds formed in vivo via a nonenzymatic mechanism involving the free radical–initiated peroxidation of arachidonic acid (3). F₂-IsoPs are further metabolized to form 2,3-dinor-5,6-dihydro-15-F_2t-IsoP (15-F_2t-IsoP-M), a major end product of F₂-IsoPs excreted in urine (4). Urine is considered to be an ideal biological material for the measurement of F₂-IsoPs because, unlike plasma, it does not contain high lipid content, which minimizes concern about the artifactual generation of isoprostanes by lipid autoxidation during sampling (5). Urinary F₂-IsoPs measured by mass spectrometric methods is considered as a reliable and accurate biomarker of oxidative stress in vivo (6, 7). Measurement of its end metabolite 15-F_2t-IsoP-M in urine may offer an additional advantage over its parent compounds, potentially providing a better integrated index of oxidative stress status in vivo (5). We have recently reported in a nested case-control study that elevated levels of urinary 15-F_2t-IsoP-M are associated with increased risk of breast cancer among obese women (8).

Cumulative evidence from both in vitro and animal studies suggests that cyclooxygenase-2 may be involved in the development and progression of cancer (9) and other diseases (10, 11). It is believed that the proinflammatory effects of the cyclooxygenase-2 pathway are largely mediated through prostaglandin E₂ (PGE₂). PGE₂ is quickly converted to 11α-hydroxy-9,15-dioxo-2,3,4,5-tetranor-prostane-1,20-dioic acid (PGE-M), a major PGE₂ metabolite, and excreted in urine (12). It is generally accepted that the most accurate approach for the assessment of the endogenous production of prostaglandins in humans is to quantify excreted prostaglandin metabolites in urine (12). Leukotrienes (LT) also play a major role in the inflammatory process (13). LTs are synthesized via 5-lipoxygenase, which catalyzes a two-step conversion of arachidonic acid to LTA₄ (14). The final and biologically active metabolites of the 5-lipoxygenase cascade are leukotriene B₄ and cysteinyl LTs (LTC₄, LTD₄, and LTE₄), which are derived from the unstable intermediate LTA₄. LTs are potent mediators of inflammation (15). LTE₄ is more stable than the other LTs and is excreted in urine, where it can be readily measured. It has been suggested that urinary LTE₄ is a reliable marker of endogenous cysteinyl LT formation (16).

Although measurements by recently developed mass spectrometry (MS)–based methods have shown high accuracy and sensitivity, no study, to the best of our knowledge, has evaluated the specific intrapersonal variations of urinary levels of F2-IsoPs, 15-F2t-IsoP-M, PGE-M, and LTE4. Understanding long-term intrapersonal variations is essential to the implementation and interpretation of epidemiologic research on the associations between these biomarkers and health outcomes because most epidemiologic studies have only collected one biospecimen. Using the resources collected by a dietary validation study conducted within the Shanghai Men's Health Study (SMHS), we evaluated intrapersonal variation of these four biomarkers and their correlations with selected oxidative stress– and inflammation-related conditions.

The parent study, the SMHS, an ongoing, population-based prospective cohort study of 61,500 men ages 40 to 74 years, was designed to investigate the associations of lifestyle factors with risk of cancers and other major chronic diseases. Recruitment for the SMHS was initiated in April 2002 and was completed in June 2006 with a response rate of 74.0%. A total of 196 subjects were randomly selected from the SMHS and completed a validation study between April 2003 and May 2004 to evaluate the performance of the SMHS food frequency and physical activity questionnaires. Participants completed 12 monthly 24-h dietary and physical activity recalls. Spot urine and blood samples were collected each quarter during the 1-y study period. Urine samples were collected using a sterilized cup containing 125 mg of ascorbic acid, transported in a portable, insulated bag with ice packs (at ∼0-4°C), processed within 6 h of collection, and stored at −80°C. The overall participation rate for the validation study was 69.3%. Taking into account both statistical power considerations and budget constraints, we randomly selected 48 men from among validation study participants who had provided four spot urine and blood samples throughout the year for the current study.

The levels of four urinary biomarkers were measured by accurate and precise laboratory methods. F₂-IsoPs and 15-F_2t-IsoP-M were measured using gas chromatography/negative ion chemical ionization MS as previously described (7, 17). Briefly, urine was extracted using C18 and silica solid-phase extraction cartridges after addition of an isotopically labeled internal standard. Endogenous F₂-IsoPs or 15-F_2t-IsoP-M was then converted to the pentafluorobenzyl ester, trimethylsilyl ether derivatives for gas chromatography/negative ion chemical ionization MS analysis. This analysis was done by using an Agilent 5973 gas chromatography MS instrument with the column temperature programmed from 190°C to 300°C at 20°C/min. The lower limit of detection for these assays is in the range of 5 pg.

PGE-M was measured using the liquid chromatography/tandem MS method described previously (18). Concisely, 1 mL of urine was acidified to pH 3 with HCl and endogenous PGE-M was then converted to the O-methyloxime derivative by treatment with methyloxime HCl. The sample was then extracted with a C18 solid-phase extraction column. The eluate was dried, reconstituted in mobile phase, and filtered for analysis using liquid chromatography/tandem MS. The lower limit of detection of PGE-M is 40 pg.

LTE₄ was measured by ultra-performance liquid chromatography/MS; the specific procedure was described by Duffield-Lillico et al. (19).

We compared the basic demographic characteristics and selected lifestyle factors of the participants in the current study with the participants of the parent cohort and 196 participants of the validation study using the t test for continuous variables and the χ² test for categorical variables. The measurements of each urinary biomarker were first ranked; then repeated-measures ANOVA was done to evaluate variation among the four seasons because these biomarkers were not normally distributed. Intraclass correlation coefficients (ICC) were calculated to evaluate intrapersonal variation of the biomarkers by using the following one-way random effect model: ICC = σ_B² / (σ_B² + σ_W²) = [between group variance / (between group variance + within group variance)] (ref. 20). To evaluate whether a single spot urine sample can reflect long-term levels of the biomarkers, Spearman correlation coefficients between each seasonal measurement and the average of other three seasonal measurements were estimated using the bootstrap method with 2,000 repeats for each biomarker.

The relationship between the average of the four seasonal urine measurements of these biomarkers and selected lifestyle factors was estimated by Spearman correlation coefficients. Smoking and alcohol consumption were analyzed based on the number of cigarettes per day and ounces per day, respectively. Physical activity was measured as energy expenditure, which was calculated by multiplying the time (in hours) spent on each activity by the corresponding MET (metabolic equivalent task) value obtained from the Compendium of Physical Activity (MET-h/d/y; ref. 21). Information on diabetes (yes or no) and hypertension (yes or no) was gathered at the baseline interview. A comorbidity score was generated from baseline information according to the Charlson index (22, 23). Observations 5 SD away from the mean were considered to be outliers (24); three values were excluded for this reason, one for 15-F_2t-IsoP-M and two for PGE-M. Statistical analyses were done using SAS version 9.2 for Windows software (SAS Institute, Inc.). Statistical significance was considered to be two-sided P < 0.05.

Participants of the current study were similar to the 196 validation study participants (data not shown) and to the parent SMHS cohort regarding age, body mass index, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, current smoking, current alcohol consumption, and total energy intake at baseline interview (Table 1), suggesting that the biomarker study participants are representative of the whole cohort.

The medians of urinary levels of F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄ were 1.90, 0.53, 13.04, and 0.09 ng/mg creatinine, respectively. These values were consistent with previously reported values of these urinary compounds in healthy Caucasian American adults (7, 25). No significant seasonal variations were observed, although the median level of F₂-IsoPs seemed to be slightly higher in winter than in the other three seasons (Table 2). However, the interquartile range for this marker did not vary by season. The correlations between a single measurement and the average of the three remaining measurements for the four urinary biomarkers were relatively high (0.47 ≤ r ≤ 0.61). The ICCs were 0.69, 0.76, 0.67, and 0.64 for F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄.

The four biomarkers were positively correlated with each other (0.21 ≤ r ≤0.65; Table 3). F₂-IsoPs and LTE₄ were positively associated with amount of smoking (r = 0.39 and 0.40, respectively); 15-F_2t-IsoP-M was positively correlated with amount of alcohol consumption (r = 0.32; Table 4).

Given the observed ICC, we also estimated attenuation factors if single measurements of the biomarkers were used in an epidemiologic study. Assuming a true relative risk of 2.0, by multiplying the natural logarithm of the specified true relative risks with the ICC and exponentiating the result (26), we estimated that the observed relative risk would be 80.0% of the true value for F₂-IsoPs, PGE-M, and LTE₄ and 85.0% for 15-F_2t-IsoP-M (data not shown in tables).

F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄ are oxidation products of arachidonic acid and markers of oxidative stress and/or inflammation. In this study, we found little seasonal or intrapersonal variation in these four biomarkers. The ICCs for the four seasonal measurements were reasonably high, and a single measurement correlated very well with the average of the three other measurements. Our results suggest that a single measurement of a spot urine sample of these biomarkers can reflect the levels of these biomarkers over a 1-year period.

Previous studies have suggested that PGE-M is a potential biomarker for the detection of risk for colorectal cancer, non–small cell lung cancer, and gastric cancer (18, 27-29). The level of urinary LTE₄ is elevated in persons with bronchial asthma, Crohn's disease, ulcerative colitis, and other diseases (19, 30). Smoking is also a known risk factor for some of these diseases (31). In the current study, we found that F₂-IsoPs and LTE₄ were positively correlated with amount of smoking, which is consistent with other studies (19, 32) and suggests that the effect of smoking on the above-mentioned diseases is mediated, at least in part, through oxidative and inflammation pathways.

Meagher et al. (33) found a dose-dependent increase in urinary F₂-IsoPs excretion with respect to alcohol consumption in 10 healthy volunteers. Although there was no significant relationship between F₂-IsoPs and alcohol consumption in the current study, we found that 15-F_2t-IsoP-M, the main urinary metabolite of F₂-IsoPs, was positively associated with alcohol consumption.

Exposure misclassification is one of the major concerns for epidemiologic studies and generally attenuates the risk estimate when the misclassification is nondifferential. In our study, we found that using a single urinary measurement of F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄ would result in moderately underestimated risk estimates (e.g., 15-20%, if the true relative risk were 2). These results suggest that these biomarkers can be very useful in epidemiologic studies.

Our study has several strengths, such as its population-based cohort study design and high response rates. The participants of the current study were randomly selected from the parent cohort and are representative of the parent study. The four urine samples collected over a 1-year period provided a unique opportunity to evaluate intrapersonal and seasonal variation for four newly emerged biomarkers of oxidative stress and inflammation. The main limitation of the study is the relatively small number of samples involved, which prevented an evaluation of associations of these biomarkers with additional lifestyle factors and conditions that are known to be associated with oxidative stress and inflammation. Because variation of biomarkers may be influenced by exposure level, the findings of our study may not be directly generalizable to other populations.

In summary, we found that urinary levels of F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄ are stable and that measurements based on a single spot urine sample reflect well the level of these biomarkers over 1 year among middle-aged and elderly Chinese men. Our study suggests that urinary levels of F₂-IsoPs, 15-F_2t-IsoP-M, PGE-M, and LTE₄ can be used as biomarkers in large epidemiologic studies.

The authors have no conflicts of interest to disclose.

We thank Regina Courtney and Rodica Gal-Chis for urine sample preparation.

Grant Support: National Cancer Institute grant RO1 CA 82729 and in part by grant P50CA90949. Urine sample preparation was conducted at the Survey and Biospecimen Shared Resources, which are supported, in part, by Vanderbilt-Ingram Cancer Center grant P30 CA68485. The authors also wish to thank the technical staff of the Eicosanoid Core Laboratory at Vanderbilt University for sample analysis supported by NIH grants GM15431 and ES13125.