Abstract
In vitro evidence implicates oxidative stress in many adverse health conditions, including colorectal neoplasia. In human studies, however, oxidative stress is measured by imperfect biomarkers, which are inconsistently associated with health outcomes. Structural equation modeling (SEM) offers one possible solution by modeling a latent (unobserved) construct from multiple biomarkers. Our goal was to investigate the association of a latent oxidative stress variable with colorectal adenoma. Using SEM, we analyzed pooled data from two cross-sectional studies of colorectal adenoma (n = 526) that measured five plasma biomarkers of oxidative stress and inflammation that comprised the latent oxidative stress variable: F2-isoprostanes (FIP), fluorescent oxidation products (FOP), mitochondrial DNA (MtDNA) copy number, γ-tocopherol (Gtoc), and C-reactive protein (CRP). Higher levels of oxidative stress were associated with colorectal adenoma [OR = 3.23 per SD increase in oxidative stress; 95% confidence interval (CI), 1.28–8.18]. The latent variable estimate was considerably stronger than the associations of adenoma with the individual biomarkers, which were modest and mostly nonsignificant. Risk factors were associated with adenoma via the oxidative stress pathway, particularly overweight and obesity with an OR = 1.50; 95% CI, 1.10–2.81; and OR = 2.95; 95% CI, 1.28–12.45, respectively. Oxidative stress may be positively associated with colorectal adenoma, and important risk factors may act through this mechanism, but the cross-sectional design of the current study precludes observing the directionality of associations. The presence of an adenoma could affect levels of the circulating biomarkers; thus, we should be cautious of strong conclusions until the findings are replicated in a follow-up study. Cancer Prev Res; 11(1); 52–58. ©2017 AACR.
Introduction
Defined as an imbalance between pro- and antioxidants (1), oxidative stress has been implicated in the initiation, promotion, and progression of carcinogenesis (2–4). Those in vitro findings, however, are not supported in the epidemiologic literature (5–7), and attempts to prevent cancer by antioxidant supplementation in human clinical trials did not yield the hypothesized results (8). This inconsistency may reflect insufficient knowledge on how to influence or measure oxidative stress in humans.
As oxidative stress is not directly observable in vivo, it is normally measured using biomarkers (9). Numerous biomarkers of oxidative stress have been studied (10), but because oxidative stress is complex and multifaceted, reliance on any single marker may be inadequate (11–13). Structural equation modeling (SEM) may offer a potential solution. SEM is a multivariate analytic technique for modeling theorized structural (causal) pathways, specified a priori, while incorporating observed variables as imperfect measures of unobservable (latent) constructs (14, 15). In our earlier analysis (16), five circulating biomarkers with hypothesized links to oxidative stress constructed an oxidative stress latent variable: F2-isoprostanes (FIP), a marker of lipid peroxidation; fluorescent oxidation products (FOP), a marker of nonspecific oxidation of large molecules; mitochondrial DNA copy number (MtDNA), a marker of oxidative cellular damage in close proximity to reactive oxygen species production (17); γ-tocopherol (Gtoc), a circulating marker more representative of its oxidative stress–influenced metabolism rather than nutritional intake (18); and C-reactive protein (CRP), a marker of acute inflammation accompanied by increased oxidation. In that analysis, we validated the oxidative stress latent variable in three ways: (i) demonstrating that the associations of the biomarkers with the latent variable followed expected directions; (ii) confirming hypothesized associations of the latent variable with pro- and antioxidant exposures; and (iii) showing that the associations of pro- and antioxidant exposures with the latent variable were stronger and more consistent than their corresponding associations with each of the five biomarkers considered individually (16).
In the current study, we assessed whether a similar latent oxidative stress variable was associated with newly diagnosed colorectal adenoma. We then compared the latent variable estimate to estimates of the five biomarkers modeled individually to demonstrate its novelty. For the analysis, we use pooled data from two cross-sectional studies of colorectal adenoma that collected information on demographic and lifestyle characteristics and measured plasma biomarkers of oxidative stress.
Materials and Methods
Study population
Markers of Adenomatous Polyps study I and II.
The Markers of Adenomatous Polyps I and II (MAP I and II) studies were conducted by the same principal investigator (R.M. Bostick) using almost identical protocols, described elsewhere (19). MAP I participants (n = 474) were recruited from gastroenterology practices in Winston-Salem and Charlotte, North Carolina, while MAP II (n = 233) participants were recruited from Consultants in Gastroenterology, a large, private clinic in Columbia, South Carolina. In both studies, participants were 30 to 74 years of age, with no prior history of colorectal neoplasms, scheduled for elective outpatient colonoscopy; the data were pooled for this analysis. For the analytic sample, 126 subjects were removed because of missing information on key variables (e.g., age, sex, race, etc.). Of the 581 remaining subjects, 91% were non-Hispanic whites; thus, we restricted the analysis to that sample (n = 526). Cases (n = 188) were defined as subjects newly diagnosed with a colon or rectal adenoma during colonoscopy, and noncases (n = 255) were defined as subjects found to be free of polyps. Participants with hyperplastic polyps or unknown outcome status (n = 83) contributed toward constructing the oxidative stress latent variable but not its association with colorectal adenoma.
Data collection
Before undergoing the colonoscopy, all consenting participants completed a mailed questionnaire collecting information on demographic characteristics, medical history, use of medications, and habits. The questionnaire included, but was not limited to, tobacco smoking and alcohol drinking history, physical activity, and use of aspirin and other NSAIDs. In addition, diet and supplement intakes were assessed through a modified 153-item Willet Food Frequency Questionnaire. Vitamin and mineral total intakes were calculated as dietary plus supplemental intakes.
All participants provided blood samples that were drawn into red-coated, prechilled Vacutainer tubes, plunged into ice, and protected from light exposure. Upon immediate delivery to the laboratory, the samples were centrifuged under refrigeration, after which plasma and serum were separately aliquoted into amber-colored cryopreservation vials. Air in the vials was displaced with inert gas, and the vials were sealed with O-ring screw caps then stored at −70°C.
The Molecular Epidemiology and Biomarker Research Laboratory at the University of Minnesota (Minneapolis, MN) performed all biomarker analyses. Gas chromatography–mass spectrometry was used to measure plasma FIP (20), using deuterium (4)-labeled 8-iso-prostaglandin F2-alpha as an internal standard with unlabeled, purified F2-isoprostane as a calibration standard. FOPs were measured in plasma samples using the modified Shimasaki method (21, 22). A mixed solution of plasma and ethanol/ether was centrifuged for 10 minutes at 3,000 rpm, and then 1.0 mL of supernatant was added to cuvettes for spectrofluorometric readings. Relative fluorescence intensity, in U/mL of plasma at 360/430 nm wavelengths, was calculated by a spectrofluorometer. Quinine sulfate diluted in 0.1 N H2SO4 was used for calibration. For approximately 22% of the participants, FOP was measured in serum because plasma samples were not available; this was not expected to affect the measurement because correlation between serum and plasma FOP concentrations was very high (r = 0.9; P < 0.001). High-sensitivity CRP was measured using latex-enhanced immunonephelometry on a Behring nephelometer II (BN-II) analyzer (inter-assay CV 4%; Behring Diagnostics). Gtoc was measured using high-performance liquid chromatography (23). MtDNA copy number was analyzed using qRT-PCR as described previously (24). Two primers were used, one for MtDNA and one for nuclear DNA. The ratio of MtDNA to nuclear DNA was determined by serial dilution of a healthy referent genomic DNA sample.
Structural equation model and statistical methods
As shown in Fig. 1, the baseline SEM model (Model 1) had one standardized continuous latent variable (here onward termed oxidative stress) composed of five continuous blood biomarkers thought to be consequences of oxidative stress (arrows emanating from oxidative stress): FIP, FOP, CRP, MtDNA, and Gtoc. The model included the main effect, oxidative stress to colorectal adenoma, and potential confounder variables (listed below) modeled as causes of oxidative stress and adenoma (arrows directed toward oxidative stress and adenoma). Because of distributional differences in MtDNA copy number between the MAP studies, we considered an alternative baseline SEM where the study variable directly affected the individual biomarkers, but not oxidative stress (Fig. 1B). This change may seem trivial, but the directionality of the arrows has implications for the model assumptions. In the first model, study-specific biomarker variability would manifest in the latent oxidative stress variable; in the second model, it would not, a feature we preferred. We considered two other models (Models 2 and 3) with additional potential confounders. Each model is summarized below:
Model 1: The baseline model included the following covariates: age (years), sex (women vs. men), body mass index (BMI; <25, 25–29.9, ≥30 kg/m2), smoking (never, former, current), alcohol drinking (never, former, current), regular aspirin use (≥once/week), regular NSAID use (≥once/week), and study (MAP I vs. MAP II); n = 526.
Model 2: The partially adjusted model included the covariates in the baseline model plus additional variables potentially associated with colorectal adenoma: reported physical activity (ordinal), total energy intake (ordinal), total dietary fiber intake (ordinal), plasma cholesterol levels (continuous, mg/dL), and a family history of colorectal cancer (yes/no); n = 457.
Model 3: The fully adjusted model included the covariates of Model 2 plus additional variables potentially associated with oxidative stress: total vitamin C intake (ordinal), and polyunsaturated fatty acid intake (PUFA; ordinal); n = 457. All ordinal variables were categorized as high, medium, or low using sex-specific tertile cut-off points among noncases.
A, Baseline model of oxidative stress and colorectal adenoma in the combined MAP datasets. B, An alternative baseline model that allows a study sample variable to directly affect each biomarker so that study-specific variability in each biomarker will not manifest in the latent oxidative stress variable. Oxidative stress is derived from the shared covariance of the five biomarkers: FIP, FOP, CRP, Gtoc, MtDNA.
The primary result of these analyses is the model-estimated effect of oxidative stress on colorectal adenoma, interpreted as the change in odds of adenoma per SD change in oxidative stress. As a comparison, we estimated associations of the five biomarkers with adenoma, individually. In addition, we assessed the effects of the explanatory (exposure) variables on oxidative stress (γs), interpreted as the linear SD change in oxidative stress per unit change in exposure, and we evaluated the indirect effect of the exposures on adenoma mediated by oxidative stress (25, 26), interpreted as the change in odds of adenoma per unit change in exposure. Confidence intervals (CI) for the indirect effects were estimated by bootstrap resampling with 1,000 replications (27). We conducted a few sensitivity analyses: First, we ran the baseline model among never smokers over concerns about residual confounding; next, we removed CRP from the baseline model to examine what effect, if any, inflammation may contribute toward the latent variable.
All SEMs were analyzed using Mplus software version 7.2 (28). We chose a generalized linear logit SEM under maximum likelihood estimation, which provided ORs for adenoma but not traditional SEM fit statistics. In Supplementary Data, we report the results and fit statistics of a probit SEM using a weighted least squares estimator. A two-sided P < 0.05 indicated statistical significance.
Results
Selected baseline characteristics of the analytic sample are in Table 1. The average age was 56.8 years, and the average BMI was 28.0 kg/m2. Women comprised 52% of all subjects, but only 41% of adenoma patients. Most of the subjects were never or former smokers (77%), and about half were current drinkers (51%).
Selected baseline characteristics of the analytic study sample from the non-Hispanic white subjects in the MAP I and II studies
Higher oxidative stress was strongly associated with colorectal adenoma (Table 2). In the baseline model, the odds of adenoma increased approximately 2-fold for each SD increase in oxidative stress, with an estimated OR of 2.18 and a 95% CI from 1.22 to 3.92. The OR (95% CI) estimates for the partially and fully adjusted models were 3.03 (1.29–7.09) and 3.23 (1.28–8.18), respectively. In a sensitivity analysis among never smokers, the oxidative stress estimate did not materially change, although it was no longer statistically significant due to the smaller sample size, n = 200 (OR = 2.82; 95% CI, 0.82–9.64). Compared with oxidative stress, the associations of adenoma with each individual biomarker (per SD) were much weaker. At an OR = 1.37 (95% CI, 1.03–1.82), FIP was the only significant finding of the five biomarkers, although CRP (OR = 1.23; 95% CI, 0.97–1.57) and Gtoc (OR = 1.27; 95% CI, 0.99–1.63) were borderline. We observed the strongest point estimate for FOP, but it was very unstable and imprecise.
Model estimated effects on colorectal adenoma for one SD increase in the oxidative stress latent variable, or the respective individual biomarker
The covariance between FIP, Gtoc, and CRP characterized the latent variable in the baseline and alternative SEM (Table 3); their standardized loading factors were similar in both models. FIP contributed the most to the oxidative stress latent variable (λs = 0.70; 95% CI, 0.61–0.79), followed by Gtoc (λs = 0.51; 95% CI, 0.41–0.61), and CRP (λs = 0.45; 95% CI, 0.35–0.54). The MtDNA loading factor depended entirely on its distributional differences between the two studies: baseline model (λs = −0.26; 95% CI, −0.41 to −0.12); alternative SEM in which the study variable directly affected the biomarkers (λs = <0.01; 95% CI, −0.10–0.10); we achieved the same result if study was removed from the model (data not shown). In neither model did FOP meaningfully contribute to oxidative stress. The loading factor results did not change with additional confounders. In the CRP-excluded sensitivity analysis, the factor loadings for FIP, FOP, MtDNA, and Gtoc did not materially change, and neither did the oxidative stress association with colorectal adenoma (OR = 1.96; 95% CI, 1.13–3.38). In Supplementary Table S1, we provide model fit statistics from a probit model analysis of the baseline models.
Indicator variables and their standardized loading factors from oxidative stress
Several explanatory variables were associated with oxidative stress, and some of these were associated with adenoma via the oxidative stress pathway. From the adjusted models, higher amounts of physical activity, dietary fiber, and total vitamin C intakes were estimated to lower oxidative stress (Table 4). Conversely, higher serum total cholesterol concentrations, and greater PUFA and total energy intakes were estimated to increase oxidative stress. Obesity, smoking, alcohol consumption, and regular aspirin use were all associated with oxidative stress (Table 4). The estimated oxidative stress pathway effects on adenoma for overweight and obesity were 1.50 (95% CI, 1.10–2.81) and 2.95 (95% CI, 1.28–12.45), respectively. The estimates for current smoking, current drinking, and regular aspirin use were borderline statistically significant, while those for former smoking, former drinking, and regular NSAID use did not appreciably differ from the null value (Table 5).
Model estimated effects of explanatory variables on the linear oxidative stress latent variable; γs estimates are per SD of oxidative stress
Model estimated indirect effects for adenoma risk factors via the oxidative stress pathway
Discussion
In a previous study, we presented evidence that a latent variable comprised of FIP, CRP, and Gtoc, but not FOP and MtDNA copy number, may adequately represent the construct of oxidative stress (16). In the current study, we found that a similar oxidative stress latent variable was strongly associated with colorectal adenoma risk. When modeled individually, the biomarker–adenoma associations were weaker and nonsignificant, demonstrating the utility of the latent variable. Numerous pro- and antioxidant risk factors predicted oxidative stress. Of those, overweight and obesity were associated with adenoma via the oxidative stress pathway, although we must be cautious because the cross-sectional study design precludes observing directionality. To our knowledge, this is the first study to use SEM with latent variables to investigate how oxidative stress may be related to a health outcome.
Directly comparing our results with the current literature is difficult as most researchers study biomarkers individually. In previous publications using the same dataset as ours, Kong and colleagues and Thyagarajan and colleagues reported associations of colorectal adenoma with log-transformed biomarkers of FIP, FOP, CRP, and MtDNA copy number ranging from 1.02 to 1.38 (29, 30), similar to the individual biomarker results we report. With different data, Siamakpour-Reihani and colleagues reported associations of four urinary isoprostanes with adenoma ranging from 0.88 to 1.16 (31). That prospective study (n = 425) measured FIP in urine approximately 10 years before colonoscopy, while we measured FIP in plasma shortly before colonoscopy; it is believed that plasma samples are generally preferred to urine (32). One case–control study (n = 695) reported nonsignificant higher levels of Gtoc in persons with adenoma relative to sigmoidoscopy-confirmed controls (33). Unlike Gtoc and FIP, CRP has been frequently studied with colorectal neoplasia. A meta-analysis of colorectal cancer found a higher risk (RR = 1.12; 95% CI, 1.01–1.25) for one log-transformed mg/L unit change in CRP (34). Four studies have focused on the CRP-adenoma association: Two case–control studies report nonsignificant, but modest, positive associations (OR = 1.45) for the highest study-specific level of CRP compared with the lowest (29, 35); two prospective studies found a null result and a nonsignificant protective result (OR = 0.61; refs. 36, 37).
Although it is conventional to assess oxidative stress with individual biomarkers, we considered it an unobservable construct from which we could model a latent variable using the shared covariance of multiple imperfect biomarkers. In theory, the individual biomarkers can be weakly associated with adenoma, but the latent variable would result in a stronger and more robust association, something we observed in this study. In a recent prospective study (n = 2,520), Aleksandrova and colleagues combined a reactive oxygen metabolite biomarker with CRP (ROM/CRP) through principal component analysis, and although only two biomarkers created the “factor,” the authors found that the highest quartile of ROM/CRP compared with the lowest increased colorectal cancer risk by 70% (38). This association, however, did not persist after excluding cases diagnosed within the first 2 years of follow-up, leading the authors to suggest that the ROM biomarker was either a late cause or early consequence of colorectal cancer (39). The current study suggests that oxidative stress may be associated earlier in the colorectal neoplasia natural history, as a cause and/or consequence of adenoma. If oxidative stress is more a consequence rather than a cause of colorectal neoplasia, it may still be useful as an early predictor.
We found that higher BMI was positively associated with colorectal adenoma via the oxidative stress pathway. Obesity is an established risk factor for adenoma (40, 41) and oxidative stress (42). It affects inflammatory cytokines, weakens antioxidant defenses, and elevates blood lipid and glucose levels, resulting in higher oxidative stress (43). Our inverse pathway findings for regular aspirin use, and current alcohol drinking were modest, along with the positive finding for current smoking. Aspirin is inversely associated with colorectal neoplasms and has reduced adenoma recurrence in randomized controlled trials (44), presumably by suppressing oxidative stress and inflammation (45). Research shows smoking increases oxidative stress (46) and is associated with higher adenoma risk (47, 48). The estimated inverse indirect effect of current drinking on adenoma may require further exploration. Although the association of alcohol consumption with oxidative stress is mixed, alcohol consumption is associated with higher adenoma risk (49). Interestingly, for both alcohol drinking and smoking, the model estimated influential, and harmful, direct effects on adenoma, suggesting that drinking and smoking may act through additional mechanisms.
An important limitation of the current study is that the biomarkers were measured from a single blood draw shortly before colonoscopy. The design cannot establish directionality of the oxidative stress–adenoma association, although the application of SEM assumes that exposure precedes the outcome. There is evidence suggesting that colorectal cancer may affect reactive oxygen metabolites (39), and despite colorectal adenomas being more benign tissues than carcinomas, we cannot rule out that adenoma growth had some effect on the circulating biomarkers. Despite this limitation, the analysis demonstrates that a latent variable approach consisting of multiple biomarkers may produce stronger associations with a health outcome than individual biomarkers. We cannot be certain that the latent variable in this study exclusively, or wholly, reflects the construct of oxidative stress, which may require additional biomarkers. As oxidative stress is both a cause and consequence of inflammation (2), it is possible the latent variable represents some combination of these two interrelated processes. We did, however, observe no material difference when we excluded CRP from the analysis, and NSAID use in the current study was associated with CRP and colorectal adenoma but not via the oxidative stress pathway. Finally, we note that separation of direct and indirect effects is difficult and requires strong assumptions (50). Although we attempted to adjust for as many confounders available to us, we suggest caution interpreting the magnitudes of the pathway associations.
Future studies of oxidative stress should consider the limitations noted for the current study. As colorectal neoplasms tend to grow slowly, only a prospective design, preferably with multiyear follow-up, can assure the proper temporal relation between blood biomarkers and adenoma incidence. Although we validated the oxidative stress latent variable in a previous study, further use of SEM with more oxidative stress biomarkers could help clarify this biologic phenomenon and its role in disease etiology and progression. The SEM methodology can be applied in other areas of research where it is necessary to combine different but imperfect measurements to describe a complex biologic phenomenon.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: R.C. Eldridge, M. Goodman, R.M. Bostick, M. Gross, W.D. Flanders
Development of methodology: R.C. Eldridge, W.D. Flanders
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M. Goodman, R.M. Bostick, M. Gross, B. Thyagarajan
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R.C. Eldridge, W.D. Flanders
Writing, review, and/or revision of the manuscript: R.C. Eldridge, M. Goodman, R.M. Bostick, V. Fedirko, B. Thyagarajan, W.D. Flanders
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): V. Fedirko
Grant Support
This work was supported by the Laney Graduate School, Emory University, R01 CA66539, R01 CA116795, the Fullerton Foundation, and Georgia Cancer Coalition.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Footnotes
Note: Supplementary data for this article are available at Cancer Prevention Research Online (http://cancerprevres.aacrjournals.org/).
- Received June 12, 2017.
- Revision received August 18, 2017.
- Accepted October 20, 2017.
- Published first October 26, 2017.
- ©2017 American Association for Cancer Research.
References
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