Polycyclic aromatic hydrocarbons and breast cancer: Effect measure modification by vitamin D and vitamin D-related single nucleotide polymorphisms

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Rhoden, Patrick T. Bradshaw, Susan E. Steck, Hazel B. Nichols, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2179262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Our objective was to examine whether vitamin D and vitamin D-related SNPs modify the association between individual PAH sources and breast cancer . Methods: This population-based case-control study included 1,026 women with first primary in situ or invasive breast cancer and 1,074 age-frequency matched controls. To evaluate effect measure modification (EMM) by measured vitamin D and vitamin D-related single nucleotide polymorphisms on multiple PAH measures (active cigarette smoking, smoking spouse, grilled/smoked meat consumption, synthetic log use, and vehicular traffic), we estimated odds ratios and 95% confidence intervals with unconditional logistic regression. EMM was assessed on the multiplicative (likelihood ratio tests) and additive scales (interaction contrast ratio). Results: Vitamin D modified the synthetic log use breast cancer association on the additive scale (ICR= -0.86; 95% CI: -1.67, -0.04) and multiplicative scales (OR=0.52; 95% CI: 0.09, 0.94). Contrary to expectation, the stratum-specific OR for synthetic log use among those with vitamin D <30 ng/mL was below the null compared to the elevated OR found for those with vitamin D ≥ 30ng/mL (0.89 vs 1.70). The effect estimates for other PAHs were imprecise. Conclusion: We found evidence of EMM for the association between synthetic log use and breast cancer by vitamin D. The pattern of association was unexpected and requires replication. Potential EMM was observed with several other PAH measures. Additionally, we observed EMM for several SNPs which were not statistically significant after adjustment for multiple comparisons. vitamin D polycyclic aromatic hydrocarbons breast cancer vitamin D-related single nucleotide polymorphisms Introduction Polycyclic aromatic hydrocarbons (PAHs) are persistent environmental contaminants that result from the incomplete combustion of organic materials [1], [2]. PAHs are known lung carcinogens and cause mammary tumors in experimental models [3], [4]. Common sources of PAH exposure include indoor and outdoor air pollution, cigarette smoke, environmental tobacco smoke, and diet[5]. The epidemiologic association between breast cancer and PAH sources has varied. For example, the relationship between active smoking and breast cancer has historically been inconsistent [6], [7]. However, a recent meta-analysis reported a positive association between current or former smoking and breast cancer risk [8]. Similarly, there has been some disagreement on the association between exposure to environmental tobacco smoke with breast cancer [9]–[13]. Dietary exposure from the consumption of grilled or smoked foods has been linked to breast cancer in several studies [14]–[17]. The associations between indoor air pollution [18]–[21] or outdoor air pollution [22]–[24] and breast cancer have also varied. Vitamin D is a fat-soluble vitamin and secosteroid hormone with a wide range of biological effects relevant to carcinogenesis, including cellular differentiation, growth, death, the formation of new blood vessels, and it’s ability to metastasize [25], [26]. Circulating 25-hydroxyvitamin D (25(OH)D) measures vitamin D status from sunlight exposure, diet, or supplement use. Vitamin D levels in blood have been inversely associated with breast cancer incidence and mortality in recent meta-analyses [27]. Several genes involved in vitamin D activity or metabolism include vitamin D receptor (VDR) (binds active form of vitamin D and control expression), CYP24A1(regulates the level of active vitamin D), CYP27B1 (enzyme that produces the active form of vitamin D) , and GC (vitamin D binding protein). Single nucleotide polymorphisms ( SNPs) of some vitamin D-related genes have been associated with breast cancer incidence and may influence vitamin D levels and synthesis. A 2020 meta-analysis reported an association between the GC (rs4588 and rs7041) and CYP27B1 (rs4646537 and rs3782130) polymorphisms and cancer susceptibility, including breast cancer[28] Treatment with the active form of vitamin D prevents pre-neoplastic lesion development in mammary gland explants after treatment with the PAH carcinogen 7,12-dimethylbenz(a)- anthracene (DMBA), suggesting direct anti-cancer effects of vitamin D on mammary tissue exposed to PAH [29]. It is plausible that PAHs and vitamin D may jointly impact breast cancer risk given the demonstrated effects of vitamin D on PAH exposures in experimental settings, yet whether circulating vitamin D or vitamin D-related SNPs impact the effect of PAH exposures on breast cancer development has not been investigated. Our objective was to examine whether vitamin D and vitamin D-related SNPs modifies the association between PAHs and breast cancer using data from the Long Island Breast Cancer Study Project (LIBCSP), a large population-based study. Material And Methods This study used data from the LIBCSP, a population-based case-control study designed to examine the effects of environmental exposures on breast cancer risk in Nassau and Suffolk counties on Long Island, New York. Details of the parent study have been published previously [30]. Institutional Review Board approval was obtained from all participating institutions. This analysis was reviewed by the UNC IRB and was determined to be exempt. Study Population LIBCSP population-based sample: The LIBCSP consists of English-speaking women from Long Island, NY. Study enrollment occurred between August 1, 1996 until July 31, 1997. Women aged 20 years or older diagnosed with either invasive or in situ were identified through 31 hospital pathology department in LI and NYC. Control were women over the age of 65 who were identified using probability based random digit dialing. Controls were matched based on the predicted age demographics of BC cases by 5-year age groups. The final study sample included 3,064 women of which 1,508 were cases (1,273 with invasive disease) and 1,556 controls [30]. Ninety-three percent of women identified as White, 5% Black, 2% as other. Of the total cohort 4% identified as Hispanic [30]. The racial distribution of the cohort is representative of the target population at study enrollment LIBCSP baseline assessments : A structured two-hour baseline questionnaire was conducted by trained interviewers shortly after diagnosis. The interview assessed pre- and at diagnosis BC risk factors and demographic information. Participation in the interviewers were higher among cases (82.1%) than in controls and (62.7%) [30]. To minimize the influence of treatment, blood samples were obtained before treatment initiation for 77.2 percent of breast cancer patients [30]. PAH Exposure Sources Assessment Detailed PAH source assessment methods in the LIBCSP have been previously published [31]–[34]. The interviewer-based questionnaire included questions on active and passive smoking, grilled/smoked meat consumption, synthetic wood usage, and historical residential addresses[16], [20], [31], [35], [36]. Active/ Passive Smoking— Current active smoking (yes, no) was defined as smoking during the previous 12 months. To evaluate residential ETS exposure study participants individuals were asked whether they have ever lived with a marital partner who smokes (n=1515 controls/1468 cases) [31]. Grilled/ Smoked Food Intake— Lifetime consumption was defined as the mean number of servings ingested per year based the distribution among controls. Women were asked to recount their consumption patterns of 4 categories of grilled or smoked foods (smoked beef, lamb, and pork; grilled/barbequed beef, lamb, and pork; smoked poultry or fish; and grilled/barbequed poultry or fish) during 6 decades of life. For breast cancer cases, assessment stopped at the age of diagnosis. Based on previous findings from the LIBCSP, this variable was dichotomized (<55 servings/year, 55+ servings/year) [16] Synthetic Log Use – Participants who reported using an indoor stove or fireplace at least 3 per times were asked what types of materials ( wood, coal, synthetic logs, or gas) were burned [20]. Vehicular Traffic —A validated geographic model was developed to estimate vehicular traffic exposure during and before 1995 among study participants [33], [49], [196]. BLR software was used to geocode women's present and former residential locations in Nassau and Suffolk. Geocoding was limited to addresses where study participants resided for at least 1 year. The traffic model incorporated a road network of half-million streets in the greater NY metro area The model included information on past US automobile PAH emission data, NY metro traffic patterns including historic traffic counts and weather and traffic related emission patterns, weather variables, and pollutant dispersion parameters [33]. In a previous analysis of the LIBCSP, the association with breast cancer was found to be limited to the top 5% of those exposed to vehicular traffic (low risk: <95 th percentile, high risk: ≥ 95 th percentile) [35]. Measurement of Plasma 25(OH)D Plasma 25(OH)D-- Plasma vitamin D was measured in 2,101 total study participants (1,026 cases and 1,075 controls). The Diasorin RIA method was used to quantify plasma 25(OH)D, which measures both vitamin D3 produced in the skin and dietary derived vitamin D2 [198]. Samples were tested between September 2007 and December 2007 using 8 distinct assay lots [37]. Adjustment for Seasonal Trend in Vitamin D To estimate the seasonal trend of vitamin D, data from controls were used to fit the following models: where w ( t ) is the measured vitamin D concentration for study participant at week t, ς( t ) is the seasonal trend in the population and we assume that the error, ε is approximately Normal (0, σ 2 ) and is independent of ς( t ) [38]. To remove variation due to season of blood draw, we added the study specific mean to the residuals obtained by applying the parameter estimates from the above model to the entire study population [39]. Adjusted vitamin D measurement was then dichotomized as <30 ng/mL and ≥30ng/mL based on the distribution of this constructed variable among controls. This cut-point was determined using restricted cubic splines based on the value above which breast cancer risk began to decrease. The adjusted values were used for all subsequent analyses that involved measured vitamin D. Genotyping Assays The SNP analysis was limited to White women (967 cases / 993 controls) due to sample size and population stratification concerns. We selected 25 SNPs based on a previous analysis in the LIBCSP for their known or suspected impact on the vitamin D pathway, or because they have shown associations with breast cancer in previous studies This selection included 13 SNPs in VDR : BsmI (rs1544410), rs2071358, rs2239181, rs2239182, rs2408876, rs2544038, rs3782905, rs7299460, TaqI (rs731236), ApaI (rs7975232), rs10875694, rs11168287, and rs11168314; 10 SNPs from 24-hydroxylase ( CYP24A1 ): rs927650, rs2181874, rs2244719, rs2585428, rs2762939, rs3787557, rs4809960, rs6022999, rs6068816, and rs13038432; and two from the vitamin D-binding protein ( GC ): rs4588 and rs7041 [40]. As described previously, SNPs were genotyped using the fluorogenic 5′-nuclease or TaqMan assay, using the TaqMan Core Reagent Kit (Applied Biosystems, Foster City, California [30]. The fluorescence profile of each well was measured in an ABI 7500HT Sequence Detection System, and the results analyzed with Sequence Detection Software [30]. Controls for genotype at each locus and two controls with no DNA were included on each plate [30]. Laboratory personnel were blinded to case/control status. Genotypes were dichotomized by homozygous common allele and heterozygous or homozygous minor allele. The referent group was determined based on genotypes with the presumed lowest risk in the main effects model from previous analysis in the LIBCSP[40]. Confounder Identification A review of the literature was used to develop a directed acyclic graph (DAG) and determine potential confounders[41], [42]. Based on the DAG, the minimally sufficient adjustment set included: age at menarche (modeled with restricted quadratic splines with five knots at equally spaced percentiles); parity (nulliparous, parous); lifetime alcohol intake (non-drinkers, <15g/day, 15g–30g/day, ≥30 g/day); education (high school graduate or less, some college, college or post-college); income (<$34,999, $35,000–$69,999, ≥$70,000); BMI (<25kg/m 2 , 25-30kg/m 2 , ≥30kg/m 2 ), months lactation (continuous), and the frequency matching factor, 5-year age group (20-24 year, 25-29 years, 30-34 years, 35-39 year, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-69 years, 70-74 years, 75-59 years, 80-84 years, 85-89 years). Statistical Analysis We used unconditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable models included the minimally sufficient confounder adjustment set described above. Effect measure modification (EMM) was evaluated on both the additive and multiplicative scales considering both measured vitamin D level and vitamin D-related SNPs as potential modifiers of the relationship between PAH exposure and the risk of breast cancer. For EMM on the additive scale, single-referent models, where the lowest risk group served as the referent, were constructed to compute interaction contrast ratios (ICRs) and corresponding 95% CIs. [43], [44].To analyze EMM on the multiplicative scale, models with and without the interaction term were compared to compute the likelihood ratio test (LRT) with an α=0.05 statistical significance criterion [43]. We also computed the ratios of odds ratios (RORs) and 95% CIs [45]. Treatment may affect circulating vitamin D levels, so we conducted sensitivity analyses restricting the sample to women in whom blood was collected prior to chemotherapy (n=1041). All analyses were completed in SAS 9.4 (Cary, NC). For each of the 25 selected polymorphisms, subjects were divided into three groups based on genotype (homozygous common, heterozygous, homozygous minor allele). Deviation from Hardy-Weinberg equilibrium was assessed using Proc Allele in SAS/Genetics version 9.4 (SAS Institute Inc., Cary, NC) at α = 0.05. No SNPs exhibited significant departure from HWE [46]. We next assessed the minor allele frequency in both cases and controls. Finally, the SNPs were assessed for linkage disequilibrium. We used the Benjamini-Hochberg false discovery rate (FDR) method for all SNP models to address multiple hypothesis testing for the associations between SNPs and breast cancer [47]. Results About 20% of women were current smokers, almost 50% reported living with a spouse who smoked, and about 70% of women reported having one or more servings of grilled/smoked food per week (Table 1). Approximately 15% reported using synthetic logs in their homes. Most women were White and parous. Cases tended to be older, have less education, were more likely to be overweight or obese, have fewer months lactation and lower income compared with controls. As shown in Table 2, there was evidence of antagonistic EMM by vitamin D for the synthetic log use breast cancer association on both the additive scale (ICR= -0.86; 95% CI: -1.67, -0.04) and multiplicative scales (ROR= 0.52; 95% CI: 0.29, 0.94). The stratum-specific OR for synthetic log use among those with vitamin D <30 ng/mL was attenuated and below the null compared to the elevated OR found for those with vitamin D ≥ 30ng/mL (0.89 vs 1.70) . Suggestive antagonistic EMM by vitamin D was also evident for the association between current active smoking and for grilled/smoked meat intakes, however, the effect estimates included the null. The OR for the association between current active smoking and breast cancer was modestly elevated among women with vitamin D ≥ 30ng/mL(OR= 1.33; 95% CI: 1.06, 1.68) compared to women with vitamin D <30 ng/mL (OR= 1.04; 95% CI: 0.65, 1.67). Women with vitamin D ≥ 30ng/mL(OR= 1.51; 95% CI: 1.18, 1.92) experienced and increased risk of breast cancer when compared to those with vitamin D <30 ng/mL (OR= 1.02; 95% CI: 0.70, 1.51). There was no evidence of EMM on the additive or multiplicative scales by vitamin D for ETS from spouse or vehicular traffic exposure. In sensitivity analyses, we evaluated these associations restricted to women in whom blood was collected prior to chemotherapy (n=991). The point estimates from these models were not substantially different from the previous discussed results (results not shown). Several vitamin D pathway SNPs showed evidence of EMM across PAH sources at the 5% significance level, however, after adjustment for multiple comparisons (FDR p-value >0.05) none were statistically significant. The results for these analyses are presented in the supplementary table S1-S4. Briefly, the VDR SNP rs2408876 acted as an antagonistic modifier for the exposures active smoking (ROR= 0.54.; 95% CI: 0.32, 0.90; ICR=-0.83 (-1.66, 0.00)) and synthetic log use (ROR= 0.52; 95% CI: 0.35, 0.99). This pattern of antagonistic modification was also observed for the CYP24A1 SNP rs6068816 (active smoking (ROR= 0.47; 95% CI: 0.25, 0.88) and grilled meat (ROR= 0.58; 95% CI: 0.34, 0.98; ICR=-0.67 (-1.32, -0.01)), and VDR SNPs rs73126 (ETS spouse (ROR= 0.69; 95% CI: 0.47,1.02) and grilled meat (ICR=0.53 (0.04, 1.01). Two SNPS acted as modifiers in opposing direction for PAH sources. The VDR SNP rs11168314 acted as an antagonistic modifier for vehicular traffic exposure (ROR= 0.40; 95% CI: 0.15,1.06) and synergistic modifier for synthetic log use (ROR= 1.58; 95% CI: 0.94, 2.68; ICR= 0.63; 95% CI: -0.04, 1.30). While the CYP24A1 SNP rs927650 acted as an antagonistic modifier for synthetic log use (ICR=0.59; -0.07, 1.25) and a synergistic modifier for grilled, smoked meat consumption (ROR= 0.35; 95% CI: -0.07, 0.75). Discussion In this analysis of data from a population-based case-control study, vitamin D modified the relationship between synthetic log use and breast cancer on the additive and multiplicative scales. An antagonistic pattern was observed of an increased odds of breast cancer with synthetic log use among women with higher vitamin D levels but not among women with lower vitamin D levels. There was also suggestive antagonistic EMM by vitamin D for the association with current active smoking and for grilled/smoked meat consumption and breast cancer. The observed antagonistic modification by vitamin D for the synthetic log use breast cancer relationship was unexpected. It was our hypothesis that women with high levels of vitamin D would experience a decrease in breast cancer risk.The biological plausibility for our observed associations is unknown. One explanation could be differential use of synthetic logs among women with higher vitamin D levels using fireplaces more frequently compared to women with lower vitamin D levels. Information on frequency of synthetic log use was not collected in the parent study so we were unable to assess this possibility. The relationship between vitamin D and breast cancer remains controversial. Several recent meta-analyses have found that higher levels of circulating vitamin D are associated with decreased breast cancer risk . A 2021 systematic review and meta-analysis reported that newly diagnosed breast cancer patients were more likely to have vitamin D levels below 20ng/mL when compared to controls(45% in cases compared to 33% in controls) [48]. A 2019 meta-analysis of 70 observational studies found a linear association between circulating vitamin D concentrations and breast cancer risk with overall risk decreasing by 6% for each 5 nmol/l increase in blood vitamin D [49]. Conversely, a Mendelian randomization study of 15,728 breast cancer cases did not find an association between breast cancer and vitamin D [50]. The authors do note that this does not preclude the possibility for more modest or non-linear effects of vitamin D. It is also important to note that Mendelian randomization studies have several strong assumptions which if violated can lead to biased results [51]. Previously in the LIBCSP, we observed a 72% reduction in breast cancer risk for the homozygous minor allele genotype CYP24A1 polymorphism rs6068816 (TT vs CC, OR=0.28, 95% CI: 0.10-0.71). CYP24A1 regulates the level of active vitamin D [52]. Additionally, the VDR polymorphisms BsmI (rs154410), TaqI (rs731236), and rs2544038 were inversely associated with breast cancer incidence [52]. The results of a reduced risk with BsmI were consistent with previous studies [56] [57], [58]. Several other studies conducted in primarily White populations which found a positive associations between BsmI and breast cancer risk [59]–[63] . The magnitude of effect we observed for TaqI was consistent with previous studies [64], [65]. In the present analysis, after controlling for multiple comparisons, we observed no EMM between PAH exposures and vitamin D-related SNPs. This could be attributed to sample size and power issues in the context of gene-exposure interactions. The exposure assessment in this study relied on women to report exposure across their life course. There is potential for error in recall for exposures in the distant past. There has not been an evaluation of the accuracy of self-reported use of stoves/fireplaces [20]. However, researchers have found individuals can reliably recall if they lived with a smoking spouse [66], [67] or if they ever smoked [68]. There has not been a validity study of recall specifically of grilled/smoked meat intake. There have, however been studies of long-term diet history recalls. Studies have found modest agreement for dietary intake reports between 11 and 24 years apart [69]–[72]. There are several additional limitations to this study. SNPs were selected based on their biologic potential to impact breast cancer risk, however after adjusting for multiple comparisons, none of the associations met the FDR threshold for statistical significance. Second, this analysis is based on a single vitamin D measurement at or near date of diagnosis. Given the ~ 3 week half-life this measurement may not reflect participants vitamin D concentrations during the relevant time period. Given the racial homogeneity of the LIBCSP, these results are only generalizable to White women. While this may limit generalizability, this more homogenous population is a strength as it reduces genetic variability. The frequency of the at-risk allele for relevant vitamin D genetic polymorphisms and their effects on serum vitamin D levels differs by racial subgroup, thus it is important to determine whether PAH-gene interactions and breast cancer risk varies among different populations [73], [74]. Likewise, vitamin D concentrations are known to vary race, future studies should explore potential subgroups effects of PAH-vitamin D interactions [75], [76]. Our analysis also had sample size limitations for some comparisons. As mentioned previously, sources of PAHs were used as a proxy for direct measurements of individual PAHs, though these sources are considered the most important contributors to PAH exposure in the general population [77]. PAH sources may contain combinations of chemicals that impact cancer risk. Heterocyclic aromatic amines (HAAs) are found in grilled/smoked food, while particulate matter,[78]; nitrogen oxides, polychlorinated biphenyls and VOCs are found in synthetic logs [79]. There are 60 or more carcinogens found in cigarette smoke;[31] and benzene and formaldehyde are emitted from vehicular traffic [80]. A strength of the study is the consideration of SNPs in other genes in addition to VDR as other genes may have an important role in understanding the relationship between vitamin D and breast cancer. Another strength is the use of newly diagnosed breast cancer cases from a population-based study with extensive covariate information. In addition, approximately two thirds of blood samples in our study were collected prior to treatment with chemotherapy. Importantly, we also considered several sources of PAH exposure. This study is the first, to our knowledge, to examine whether associations between PAHs and breast cancer incidence are modified by vitamin D status.­­ Previous studies have focused on the main effects of PAH sources and vitamin D independently as they relate to breast cancer incidence. In summary, in this population-based study, we found some evidence of EMM for the association between synthetic log use and breast cancer incidence by vitamin D. The pattern of association was unexpected and requires replication. Potential modification was observed with several other PAH measures; however, estimates were less precise. Additionally, we observed EMM for several SNPs which were not statistically significant after adjusting for multiple comparisons. Future research in larger studies is warranted to replicate these findings, particularly among diverse racial populations that may experience higher prevalence of vitamin D insufficiency or deficiency. Declarations Statement and Declarations Ethics Statement Institutional Review Board approval was obtained from all participating institutions. This analysis was reviewed by the UNC IRB and was determined to be exempt. Consent for publication Not applicable Availability of Data and Material The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing Interests The authors have no relevant financial or non-financial interests to disclose Funding Sources This works was supported by grants from the National Cancer Institute (Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship Grant Award 5F31CA247251) National Institute of Environmental Health and Sciences (Grant Award Number T32ES007018) Author Contribution The author contributions were as follows- JAR performed the analysis, drafted and revised the manuscript; PTB directed the analysis and edited the manuscript for important intellectual content; SES, HBN, KC and AFO provided direction in the development of the research question and edited the manuscript for important intellectual content. SLT and AIN and KDC were involved in the LIBCSP studies and revised the manuscript for important intellectual content. Corresponding Author Joyce A. Rhoden References E. Braithwaite, X. Wu, and Z. Wang, “Repair of DNA lesions induced by polycyclic aromatic hydrocarbons in human cell-free extracts: involvement of two excision repair mechanisms in vitro.,” Carcinogenesis , vol. 19, no. 7, pp. 1239–46, 1998, doi: 10.1093/carcin/19.7.1239. 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Distribution of demographic characteristics and PAH exposure measures for study participants, by case-control status, LIBCSP 1996–1997. Cases (n=1,026) Controls (n=1,074) N (%) N (%) Age <35 24 (2.3) 37 (3.5) 35-44 130 (12.7) 175 (16.3) 45-54 269 (26.2) 305 (28.4) 55-64 259 (25.2) 289 (26.9) 64-74 248 (24.2) 197 (18.3) 75-84 85 (8.3) 60 (5.6) 85+ 11 (1.1) 11 (1.0) Race White 967 (94.4) 992 (92.4) Non-White 57 (5.6) 82 (7.6) Missing 2 0 Education High school ­graduate or less 485 (47.3) 438 (43.7) Some college 254 (24.0) 292 (27.2) College graduate or post college 287 (28.0) 344 (34.0) Income <$34,999 361 (35.3) 326 (30.4) $35,000- $69,999 332 (32.5) 382 (35.6) ≥$70,000 330 (32.3) 365 (34.2) Missing 3 1 Lifetime Alcohol Intake Non-drinkers 384 (37.4) 390 (36.4) lifetime intake = 30g/day 51 (5.0) 74 (6.9) Missing 0 1 Parity Nulliparous 198 (13.1) 171 (11.0) Parous 1310 (86.9) 1385 (89.0) Months Lactation (mean (SD)) 3.66 (8.91) 4.64 (10.11) Age at Menarche (mean (SD)) 12.58 (1.54) 12.51 (1.64) Vitamin D (geometric mean (SD)) 35.14 (0.41) 38.15 (0.36) BMI = 30kg/m2 230 (22.6) 214 (20.2) Missing 9 15 Current Active Smoking No 828 (80.7) 873 (81.4) Yes 198 (19.3) 199 (18.6) Missing 0 2 ETS from Spouse Never 514 (51.1) 569 (54.4) Ever 491 (48.9) 478 (45.7) Missing 21 27 Grilled/barbecued/smoked meat intake ≤54 servings/year 287 (29.7) 340 (33.8) 55+ servings/year 680 (70.3) 666 (66.2) Missing 59 68 Synthetic Log Burning Never 855 (83.4) 932 (87.0) Ever 170 (16.6) 140 (13.1) Missing 1 2 Vehicular Traffic 1995 95th percentile 43 (5.0) 40 (4.30) Missing 161 141 Table 2. Additive and multiplicative effect measure modification by circulating vitamin D levels for multivariable-adjusted a associations between PAH sources and breast cancer in the LIBCSP 1996–1997 Vitamin D ≥ 30 Vitamin D <30 PAH Source Cases/Control OR (95% CI) Cases/Control OR (95% CI) b OR (95% CI) c ROR (95% CI) d p-value d ICR (95% CI) e p-value e Current Active Smoking Never 598/673 1.00 230/200 1.26 (0.96, 1.65) 1.00 Ever 144/150 1.33 (1.06, 1.68) 54/49 1.31 (0.86, 2.00) 1.04 (0.65, 1.67) 0.78 (0.47, 1.32) 0.36 -0.28(-0.95, 0.40) 0.42 ETS from Spouse Never 380/437 1.0 ref 134/132 1.20 (0.90, 1.61) 1.00 Ever 346/366 1.04 (0.84, 1.29) 145/112 1.41 (1.05, 1.90) 1.17 (0.82, 1.68) 1.12 (0.74, 1.69) 0.59 0.16 (-0.34, 0.66) 0.53 Grilled/smoked meat ≤54 servings 188/252 1.00 99/88 1.59 (1.11, 2.30) 1.00 55+ servings 515/518 1.51 (1.18, 1.92) 165/148 1.63 (1.20, 2.22) 1.02 (0.70, 1.51) 0.69 (0.44, 1.06) 0.09 -0.47 (-1.17, 0.24) 0.19 Synthetic Log Never 607/718 1.00 248/214 1.40 (1.12, 1.76) 1.00 Ever 134/104 1.70 (1.27, 2.27) 36/36 1.24 (0.76, 2.02) 0.89 (0.53, 1.49) 0.52 (0.29, 0.94) 0.03 -0.86 (-1.67, -0.04) 0.04 Vehicular traffic <95 th percentile 593/673 1.00 229/220 1.18 (0.93, 1.48) 1.00 ≥95 th percentile 35/34 1.09 (0.66, 1.80) 8/6 1.48 (0.50, 4.38) 1.26 (0.42, 3.76) 1.15 (0.35, 3.83) 0.82 0.21(-1.68, 2.10) 0.81 a adjusted for age at diagnosis/index date, age at menarche, alcohol intake, BMI, parity, race, income, lactation, and education b single reference coding lowest risk group exposure group vitamin D ≥ 30 c stratum specific coding within stratum of vitamin D d ROR, ratio of the odds ratio, interaction variable p-value e ICR, interaction contrast ratio for additive interaction Additional Declarations No competing interests reported. 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PAHs are known lung carcinogens and cause mammary tumors in experimental models [3], [4]. \u0026nbsp;Common sources of PAH exposure include indoor and outdoor air pollution, cigarette smoke, environmental tobacco smoke, and diet[5]. \u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe epidemiologic association between breast cancer and PAH sources has varied. \u0026nbsp;For example, the relationship between active smoking and breast cancer has historically been inconsistent [6], [7]. However, a recent meta-analysis reported a positive association between current or former smoking and breast cancer risk [8]. \u0026nbsp; Similarly, there has been some disagreement on the association between exposure to environmental tobacco smoke with breast cancer [9]\u0026ndash;[13]. Dietary exposure from the consumption of grilled or smoked foods has been linked to breast cancer in several studies [14]\u0026ndash;[17]. The associations between indoor air pollution [18]\u0026ndash;[21] \u0026nbsp;or outdoor air pollution [22]\u0026ndash;[24] and breast cancer have also varied. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVitamin D is a fat-soluble vitamin and secosteroid hormone with a wide range of biological effects relevant to carcinogenesis, including cellular differentiation, growth, death, the formation of new blood vessels, and it\u0026rsquo;s ability to metastasize [25], [26]. \u0026nbsp;Circulating 25-hydroxyvitamin D (25(OH)D) measures vitamin D status from sunlight exposure, diet, or supplement use. Vitamin D levels in blood have been inversely associated with breast cancer incidence and mortality in recent meta-analyses [27]. \u0026nbsp;Several genes involved in vitamin D activity or metabolism include vitamin D receptor (VDR) (binds active form of vitamin D and control expression), \u003cem\u003eCYP24A1(regulates the level of active vitamin D),\u0026nbsp;\u003c/em\u003e\u003cem\u003eCYP27B1\u0026nbsp;\u003c/em\u003e(enzyme that produces the active form of vitamin D)\u003cem\u003e,\u003c/em\u003e \u003cem\u003eand GC (vitamin D binding protein). \u0026nbsp;Single nucleotide polymorphisms (\u003c/em\u003eSNPs) of some vitamin D-related genes have been associated with breast cancer incidence and may influence vitamin D levels and synthesis. \u0026nbsp;A 2020 meta-analysis reported an association between the \u003cem\u003eGC\u003c/em\u003e (rs4588 and rs7041) and \u003cem\u003eCYP27B1\u003c/em\u003e (rs4646537 and rs3782130) polymorphisms and cancer susceptibility, including breast cancer[28] Treatment with the active form of vitamin D prevents pre-neoplastic lesion development in mammary gland explants after treatment with the PAH carcinogen 7,12-dimethylbenz(a)- anthracene (DMBA), suggesting direct anti-cancer effects of vitamin D on mammary tissue exposed to PAH [29].\u003c/p\u003e\n\u003cp\u003eIt is plausible that PAHs and vitamin D may jointly impact breast cancer risk given the demonstrated effects of vitamin D on PAH exposures in experimental settings, yet whether circulating vitamin D or vitamin D-related SNPs impact the effect of PAH exposures on breast cancer development has not been investigated. Our objective was to examine whether vitamin D and vitamin D-related SNPs modifies the association between PAHs and breast cancer using data from the Long Island Breast Cancer Study Project (LIBCSP), a large population-based study.\u0026nbsp;\u003c/p\u003e"},{"header":"Material And Methods ","content":"\u003cp\u003eThis study used data from the LIBCSP, a population-based case-control study designed to examine the effects of environmental exposures on breast cancer risk in Nassau and Suffolk counties on Long Island, New York. Details of the parent study have been published previously\u0026nbsp;[30]. Institutional Review Board approval was obtained from all participating institutions. This analysis was reviewed by the UNC IRB and was determined to be exempt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLIBCSP population-based sample:\u003c/em\u003e The LIBCSP consists of English-speaking women from Long Island, NY. Study enrollment occurred \u0026nbsp; between August 1, 1996 until July 31, 1997. Women aged 20 years or older diagnosed with either invasive or \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003e were identified through 31 hospital pathology department in LI and NYC. Control were women over the age of 65 who were identified using probability based random digit dialing. \u0026nbsp;Controls were matched based on the predicted age demographics of BC cases by 5-year age groups. The final study sample included 3,064 women of which 1,508 were cases (1,273 with invasive disease) and 1,556 controls\u0026nbsp;[30].\u0026nbsp;\u0026nbsp;Ninety-three percent of women identified as White, 5% Black, 2% as other. \u0026nbsp;Of the total cohort 4% identified as Hispanic\u0026nbsp;[30].\u0026nbsp;The racial distribution of the cohort is representative of the target population at study enrollment\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cem\u003eLIBCSP baseline assessments\u003c/em\u003e:\u0026nbsp;A structured two-hour baseline questionnaire was conducted by trained interviewers shortly after diagnosis. The interview assessed pre- and at diagnosis BC risk factors and demographic information. \u0026nbsp;Participation in the interviewers were higher among cases (82.1%) \u0026nbsp; than in controls and (62.7%)\u0026nbsp;[30].\u0026nbsp;To minimize the influence of treatment, blood samples were obtained before treatment initiation for 77.2 percent of breast cancer patients [30].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePAH Exposure Sources Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed PAH source assessment methods in the LIBCSP have been previously published [31]\u0026ndash;[34].\u0026nbsp; \u0026nbsp;The interviewer-based questionnaire included questions on active and passive smoking, grilled/smoked meat consumption, synthetic wood usage, and historical residential addresses[16], [20], [31], [35], [36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eActive/ Passive Smoking\u0026mdash;\u003c/em\u003e Current active smoking (yes, no) was defined as smoking during the previous 12 months. To evaluate residential ETS exposure study participants individuals were asked whether they have ever lived with a marital partner who smokes (n=1515 controls/1468 cases) [31].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGrilled/ Smoked Food Intake\u0026mdash;\u003c/em\u003e\u0026nbsp; Lifetime consumption was defined as the mean number of servings ingested per year based the distribution among controls. Women were asked to recount their consumption patterns of 4 categories of grilled or smoked foods (smoked beef, lamb, and pork; grilled/barbequed beef, lamb, and pork; smoked poultry or fish; and grilled/barbequed poultry or fish) \u0026nbsp;during 6 decades of life. \u0026nbsp;For breast cancer cases, assessment stopped at the age of diagnosis. Based on previous findings from the LIBCSP, this variable was dichotomized (\u0026lt;55 servings/year, 55+ servings/year)\u0026nbsp;[16]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Synthetic Log Use \u0026ndash;\u003c/em\u003e Participants who reported using an indoor stove or fireplace at least 3 per times were asked what types of materials ( wood, coal, synthetic logs, or gas) were burned [20].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cem\u003eVehicular Traffic\u003c/em\u003e\u0026mdash;A validated geographic model was developed to estimate vehicular traffic exposure during and before 1995 among study participants [33], [49], [196]. \u0026nbsp; BLR software was used to geocode women\u0026apos;s present and former residential locations in Nassau and Suffolk. Geocoding was limited to addresses where study participants resided for at least 1 year. The traffic model incorporated a road network of half-million streets in the greater NY metro area The model included \u0026nbsp;information on past US automobile PAH emission data, NY metro \u0026nbsp; traffic patterns including historic traffic counts and weather and traffic related emission patterns, weather variables, and pollutant dispersion parameters [33]. \u0026nbsp;In a previous analysis of the LIBCSP, the association with breast cancer was found to be limited to the top 5% of those exposed to vehicular traffic (low risk: \u0026lt;95\u003csup\u003eth\u003c/sup\u003e percentile, high risk: \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile)\u0026nbsp; [35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Plasma 25(OH)D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePlasma 25(OH)D-- \u0026nbsp;\u003c/em\u003e Plasma vitamin D was measured \u0026nbsp;in 2,101 total study participants (1,026 cases and 1,075 controls). The Diasorin RIA method was used to quantify plasma 25(OH)D, which measures both vitamin D3 produced in the skin and dietary derived vitamin D2 [198]. Samples were tested between September 2007 and December 2007 using 8 distinct assay lots\u0026nbsp;[37]. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdjustment for Seasonal Trend in Vitamin D\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the seasonal trend of vitamin D, data from controls were used to fit the following models:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003ew\u003c/em\u003e(\u003cem\u003et\u003c/em\u003e) is the measured vitamin D concentration for study participant at week \u003cem\u003et,\u0026nbsp;\u003c/em\u003e\u0026sigmaf;(\u003cem\u003et\u003c/em\u003e) is the seasonal trend in the population and we assume that the error, \u0026epsilon; is approximately Normal (0, \u0026sigma;\u003csup\u003e2\u003c/sup\u003e) and is independent of \u0026sigmaf;(\u003cem\u003et\u003c/em\u003e)\u0026nbsp;[38]. To remove variation due to season of blood draw, we added the study specific mean to the residuals obtained by applying the parameter estimates from the above model to the entire study population\u0026nbsp;[39]. Adjusted vitamin D measurement was then dichotomized as \u0026lt;30 ng/mL and \u0026ge;30ng/mL based on the distribution of this constructed variable among controls. This cut-point was determined using restricted cubic splines based on the value above which breast cancer risk began to decrease. The adjusted values were used for all subsequent analyses that involved measured vitamin D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping Assays\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SNP analysis was limited to White women (967 cases / 993 controls) due to sample size and population stratification concerns. We selected 25 SNPs based on a previous analysis in the LIBCSP for their known or suspected impact on the vitamin D pathway, or because they have shown associations with breast cancer in previous studies\u0026nbsp;This selection included 13 SNPs in \u003cem\u003eVDR\u003c/em\u003e: \u003cem\u003eBsmI\u003c/em\u003e (rs1544410), rs2071358, rs2239181, rs2239182, rs2408876, rs2544038, rs3782905, rs7299460, \u003cem\u003eTaqI\u003c/em\u003e (rs731236), \u003cem\u003eApaI\u003c/em\u003e (rs7975232), rs10875694, rs11168287, and rs11168314; 10 SNPs from 24-hydroxylase (\u003cem\u003eCYP24A1\u003c/em\u003e): rs927650, rs2181874, rs2244719, rs2585428, rs2762939, rs3787557, rs4809960, rs6022999, rs6068816, and rs13038432; and two from the vitamin D-binding protein (\u003cem\u003eGC\u003c/em\u003e): rs4588 and rs7041 [40].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;As described previously, SNPs were genotyped using the fluorogenic 5\u0026prime;-nuclease or TaqMan assay, using the TaqMan Core Reagent Kit (Applied Biosystems, Foster City, California\u0026nbsp;[30]. \u0026nbsp;The fluorescence profile of each well was measured in an ABI 7500HT Sequence Detection System, and the results analyzed with Sequence Detection Software [30]. \u0026nbsp;Controls for genotype at each locus and two controls with no DNA were included on each plate [30]. \u0026nbsp; Laboratory personnel were blinded to case/control status.\u003c/p\u003e\n\u003cp\u003eGenotypes were dichotomized by homozygous common allele and heterozygous or homozygous minor allele. The referent group was determined based on genotypes with the presumed lowest risk in the main effects model from previous analysis in the LIBCSP[40]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfounder Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA review of the literature was used to develop a directed acyclic graph (DAG) and determine potential confounders[41], [42]. Based on the DAG, the minimally sufficient adjustment set included: age at menarche (modeled with restricted quadratic splines with five knots at equally spaced percentiles); parity (nulliparous, parous); lifetime alcohol intake (non-drinkers, \u0026lt;15g/day, 15g\u0026ndash;30g/day, \u0026ge;30 g/day); education (high school graduate or less, some college, college or post-college); income (\u0026lt;$34,999, $35,000\u0026ndash;$69,999, \u0026ge;$70,000); \u0026nbsp;BMI (\u0026lt;25kg/m\u003csup\u003e2\u003c/sup\u003e, 25-30kg/m\u003csup\u003e2\u003c/sup\u003e , \u0026ge;30kg/m\u003csup\u003e2\u003c/sup\u003e), months lactation (continuous), and the frequency matching factor, 5-year age group (20-24 year, 25-29 years, 30-34 years, 35-39 year, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-69 years, 70-74 years, 75-59 years, 80-84 years, 85-89 years). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;We used unconditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable models included the minimally sufficient confounder adjustment set described above. Effect measure modification (EMM) was evaluated on both the additive and multiplicative scales considering both measured vitamin D level and vitamin D-related SNPs as potential modifiers of the relationship between PAH exposure and the risk of breast cancer. For EMM on the additive scale, single-referent models, where the lowest risk group served as the referent, were constructed to compute interaction contrast ratios (ICRs) and corresponding 95% CIs.\u0026nbsp;[43], [44].To analyze EMM on the multiplicative scale, models with and without the interaction term were compared to compute the likelihood ratio test (LRT) with an \u0026alpha;=0.05 statistical significance criterion\u0026nbsp;[43]. We also computed the ratios of odds ratios (RORs) and 95% CIs\u0026nbsp;[45]. Treatment may affect circulating vitamin D levels, so we conducted sensitivity analyses restricting the sample to women in whom blood was collected prior to chemotherapy (n=1041). All analyses were completed in SAS 9.4 (Cary, NC).\u003c/p\u003e\n\u003cp\u003eFor each of the 25 selected polymorphisms, subjects were divided into three groups based on genotype (homozygous common, heterozygous, homozygous minor allele). Deviation from Hardy-Weinberg equilibrium was assessed \u0026nbsp;using Proc Allele in SAS/Genetics version 9.4 (SAS Institute Inc., Cary, NC) at \u0026alpha; = 0.05. \u0026nbsp;No SNPs exhibited significant departure from HWE\u0026nbsp;[46]. We next assessed the minor allele frequency in both cases and controls. Finally, the SNPs were assessed for linkage disequilibrium. We used the Benjamini-Hochberg false discovery rate (FDR) method for all SNP models to address multiple hypothesis testing for the associations between SNPs and breast cancer\u0026nbsp;[47].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAbout 20% of women were current smokers, almost 50% reported living with a spouse who smoked, and about 70% of women reported having one or more servings of grilled/smoked food per week (Table 1). \u0026nbsp;Approximately 15% reported using synthetic logs in their homes. Most women were White and parous. Cases tended to be older, have less education, were more likely to be overweight or obese, have fewer months lactation and lower income compared with controls. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, there was evidence of antagonistic EMM by vitamin D for the synthetic log use breast cancer \u0026nbsp;association on both the additive scale (ICR= -0.86; 95% CI: -1.67, -0.04) and multiplicative scales (ROR= 0.52; 95% CI: 0.29, 0.94). \u0026nbsp;The stratum-specific OR for synthetic log use among those with vitamin D \u0026lt;30 ng/mL was attenuated and below the null compared to the elevated OR found for those with vitamin D \u0026ge; 30ng/mL (0.89 vs 1.70)\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSuggestive antagonistic EMM by vitamin D was also evident for the association between current active smoking and for grilled/smoked meat intakes, however, the effect estimates included the null. The OR for the association between current active smoking and breast cancer was modestly elevated among women with vitamin D \u0026ge; 30ng/mL(OR= 1.33; 95% CI: 1.06, 1.68) compared to women with vitamin D \u0026lt;30 ng/mL (OR= 1.04; 95% CI: 0.65, 1.67). \u0026nbsp;Women with vitamin D \u0026ge; 30ng/mL(OR= 1.51; 95% CI: 1.18, 1.92) experienced and increased risk of breast cancer when compared to those with vitamin D \u0026lt;30 ng/mL (OR= 1.02; 95% CI: 0.70, 1.51).\u003c/p\u003e\n\u003cp\u003eThere was no evidence of EMM on the additive or multiplicative scales by vitamin D for ETS from spouse or vehicular traffic exposure. In sensitivity analyses, we evaluated these associations restricted to women in whom blood was collected prior to chemotherapy (n=991). The point estimates from these models were not substantially different from the previous discussed results (results not shown).\u003c/p\u003e\n\u003cp\u003eSeveral vitamin D pathway SNPs showed evidence of EMM across PAH sources at the 5% significance level, however, after adjustment for multiple comparisons (FDR p-value \u0026gt;0.05) none were statistically significant. The results for these analyses are presented in the supplementary table S1-S4. Briefly, the \u003cem\u003eVDR\u003c/em\u003e SNP rs2408876 acted as an antagonistic modifier for the exposures active smoking (ROR= 0.54.; 95% CI: 0.32, 0.90; ICR=-0.83 (-1.66, 0.00)) and synthetic log use (ROR= 0.52; 95% CI: 0.35, 0.99). \u0026nbsp; This pattern of antagonistic modification was also observed for the \u003cem\u003eCYP24A1\u003c/em\u003e SNP rs6068816 (active smoking (ROR= 0.47; 95% CI: 0.25, 0.88) and grilled meat (ROR= 0.58; 95% CI: 0.34, 0.98; ICR=-0.67 (-1.32, -0.01)), and \u003cem\u003eVDR\u0026nbsp;\u003c/em\u003eSNPs rs73126 (ETS spouse (ROR= 0.69; 95% CI: 0.47,1.02) and grilled meat (ICR=0.53 (0.04, 1.01). \u0026nbsp;Two SNPS acted as modifiers in opposing direction for PAH sources. \u0026nbsp;The \u003cem\u003eVDR\u003c/em\u003e SNP rs11168314 acted as an antagonistic modifier for vehicular traffic exposure (ROR= 0.40; 95% CI: 0.15,1.06) and synergistic modifier for synthetic log use (ROR= 1.58; 95% CI: 0.94, 2.68; ICR= 0.63; 95% CI: -0.04, 1.30). While the \u003cem\u003eCYP24A1\u003c/em\u003e SNP rs927650 acted as an antagonistic modifier for synthetic log use (ICR=0.59; -0.07, 1.25) and a synergistic modifier for grilled, smoked meat consumption (ROR= 0.35; 95% CI: -0.07, 0.75).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this analysis of data from a population-based case-control study, vitamin D modified the relationship between synthetic log use and breast cancer on the additive and multiplicative scales. An antagonistic pattern was observed of an increased odds of breast cancer with synthetic log use among women with higher vitamin D levels but not among women with lower vitamin D levels. There was also suggestive antagonistic EMM by vitamin D for the association with current active smoking and for grilled/smoked meat consumption and breast cancer. The observed antagonistic modification by vitamin D for the synthetic log use breast cancer relationship was unexpected. It was our hypothesis that women with high levels of vitamin D would experience a decrease in breast cancer risk.The biological plausibility for our observed associations is unknown. \u0026nbsp;One explanation could be differential use of synthetic logs among women with higher vitamin D levels using fireplaces more frequently compared to women with lower vitamin D levels. Information on frequency of synthetic log use was not collected in the parent study so we were unable to assess this possibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe relationship between vitamin D and breast cancer remains controversial. \u0026nbsp;Several recent meta-analyses have found that higher levels of circulating vitamin D are associated with decreased breast cancer risk . \u0026nbsp;A 2021 systematic review and meta-analysis reported that newly diagnosed breast cancer patients were more likely to have vitamin D levels below 20ng/mL when compared to controls(45% in cases compared to 33% in controls)\u0026nbsp;[48].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; A 2019 meta-analysis of 70 observational studies found a linear association between circulating vitamin D concentrations and breast cancer risk with overall risk decreasing by 6% for each 5 nmol/l increase in blood vitamin D\u0026nbsp;[49]. \u0026nbsp;Conversely, a Mendelian randomization study of 15,728 breast cancer cases did not find an association between breast cancer and vitamin D\u0026nbsp;[50]. The authors do note that this does not preclude the possibility for more modest or non-linear effects of vitamin D. It is also important to note that Mendelian randomization studies have several strong assumptions which if violated can lead to biased results\u0026nbsp;[51].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Previously in the LIBCSP, we observed a 72% reduction in breast cancer risk for the homozygous minor allele genotype \u003cem\u003eCYP24A1\u0026nbsp;\u003c/em\u003epolymorphism rs6068816 (TT vs CC, OR=0.28, 95% CI: 0.10-0.71). \u003cem\u003eCYP24A1\u0026nbsp;\u003c/em\u003eregulates the level of active vitamin D\u0026nbsp;[52]. \u0026nbsp;Additionally, the \u003cem\u003eVDR\u0026nbsp;\u003c/em\u003epolymorphisms \u003cem\u003eBsmI (rs154410), TaqI (rs731236),\u003c/em\u003e and\u003cem\u003e\u0026nbsp;rs2544038\u0026nbsp;\u003c/em\u003ewere inversely associated with breast cancer incidence [52]. The results of a reduced risk with \u003cem\u003eBsmI\u003c/em\u003e were consistent with previous studies [56] [57], [58].\u0026nbsp; Several other studies conducted in primarily White populations which found a positive associations between \u003cem\u003eBsmI and breast cancer risk\u003c/em\u003e\u003cem\u003e\u003cem\u003e[59]\u0026ndash;[63]\u003c/em\u003e\u003c/em\u003e. The magnitude of effect we observed for \u003cem\u003eTaqI\u0026nbsp;\u003c/em\u003ewas consistent with previous studies [64], [65].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In the present analysis, after controlling for multiple comparisons, we observed no EMM between PAH exposures and vitamin D-related SNPs. This could be attributed to sample size and power issues in the context of gene-exposure interactions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe exposure assessment in this study relied on women to report exposure across their life course. There is potential for error in recall for exposures in the distant past. There has not been an evaluation of the accuracy of self-reported use of stoves/fireplaces\u0026nbsp;[20]. \u0026nbsp;However, researchers have found individuals can reliably recall if they lived with a smoking spouse [66], [67] or if they ever smoked\u0026nbsp;[68]. There has not been a validity study of recall specifically of grilled/smoked meat intake. There have, however been studies of long-term diet history recalls. Studies have found modest agreement for dietary intake reports between 11 and 24 years apart\u0026nbsp;[69]\u0026ndash;[72].\u003c/p\u003e\n\u003cp\u003eThere are several additional limitations to this study. SNPs were selected based on their biologic potential to impact breast cancer risk, \u0026nbsp;however after adjusting for multiple comparisons, none of the associations met the FDR threshold for statistical significance. Second, this analysis is based on a single vitamin D measurement at or near date of diagnosis. \u0026nbsp;Given the ~ 3 week half-life this measurement may not reflect participants vitamin D concentrations during the relevant time period. \u0026nbsp;Given the racial homogeneity of the LIBCSP, these results are only generalizable to White women. \u0026nbsp;While this may limit generalizability, this more homogenous population is a strength as it reduces genetic variability. The frequency of the at-risk allele for relevant vitamin D genetic polymorphisms and their effects on serum vitamin D levels differs by racial subgroup, thus it is important to determine whether PAH-gene interactions and breast cancer risk varies among different populations\u0026nbsp;[73], [74]. Likewise, vitamin D concentrations are known to vary race, future studies should explore potential subgroups effects of PAH-vitamin D interactions\u0026nbsp;[75], [76]. \u0026nbsp;Our analysis also had sample size limitations for some comparisons. As mentioned previously, sources of PAHs were used as a proxy for direct measurements of individual PAHs, though these sources are considered the most important contributors to PAH exposure in the general population\u0026nbsp;[77]. \u0026nbsp;PAH sources may contain combinations of chemicals that impact cancer risk. Heterocyclic aromatic amines (HAAs) are found in grilled/smoked food, while particulate matter,[78]; nitrogen oxides, polychlorinated biphenyls and VOCs are found in synthetic logs\u0026nbsp;[79]. There are 60 or more carcinogens found in cigarette smoke;[31] and benzene and formaldehyde are emitted from vehicular traffic\u0026nbsp;[80]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA strength of the study is the consideration of SNPs in other genes in addition to \u003cem\u003eVDR\u003c/em\u003e as other genes may have an important role in understanding the relationship between vitamin D and breast cancer. Another strength is the use of newly diagnosed breast cancer cases from a population-based study with extensive covariate information. In addition, approximately two thirds of blood samples in our study were collected prior to treatment with chemotherapy. \u0026nbsp;Importantly, we also considered several sources of PAH exposure.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study is the first, to our knowledge, to examine whether associations between PAHs and breast cancer incidence are modified by vitamin D status.\u0026shy;\u0026shy; Previous studies have focused on the main effects of PAH sources and vitamin D independently as they relate to breast cancer incidence. In summary, in this population-based study, we found some evidence of EMM for the association between synthetic log use and breast cancer incidence by vitamin D. The pattern of association was unexpected and requires replication. Potential modification was observed with several other PAH measures; however, estimates were less precise. Additionally, we observed EMM for several SNPs which were not statistically significant after adjusting for multiple comparisons. Future research in larger studies is warranted to replicate these findings, particularly among diverse racial populations that may experience higher prevalence of vitamin D insufficiency or deficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was obtained from all participating institutions. This analysis was reviewed by the UNC IRB and was determined to be exempt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis works was supported by grants from the National Cancer Institute (Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship Grant Award 5F31CA247251) \u0026nbsp;National Institute of Environmental Health and Sciences (Grant Award Number T32ES007018)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e The author contributions were as follows- JAR performed the analysis, drafted and revised the manuscript; PTB directed the analysis and edited the manuscript for important intellectual content; \u0026nbsp;SES, HBN, KC and AFO provided direction in the development of the research question and edited the manuscript for important intellectual content. SLT and AIN and KDC were involved in the LIBCSP studies and revised the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e Joyce A. Rhoden\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eE. Braithwaite, X. Wu, and Z. Wang, \u0026ldquo;Repair of DNA lesions induced by polycyclic aromatic hydrocarbons in human cell-free extracts: involvement of two excision repair mechanisms in vitro.,\u0026rdquo; \u003cem\u003eCarcinogenesis\u003c/em\u003e, vol. 19, no. 7, pp. 1239\u0026ndash;46, 1998, doi: 10.1093/carcin/19.7.1239.\u003c/li\u003e\n\u003cli\u003eJ. Korsh, A. Shen, K. Aliano, and T. Davenport, \u0026ldquo;Polycyclic Aromatic Hydrocarbons and Breast Cancer: A Review of the Literature,\u0026rdquo; \u003cem\u003eBreast Care\u003c/em\u003e, vol. 10, no. 5, pp. 316\u0026ndash;318, Oct. 2015, doi: 10.1159/000436956.\u003c/li\u003e\n\u003cli\u003eJ. J. Morris and E. 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Graham, \u0026ldquo;Dietary history from the distant past: A methodological study,\u0026rdquo; \u003cem\u003eNutrition and Cancer\u003c/em\u003e, vol. 5, no. 2, pp. 69\u0026ndash;77, Jan. 1983, doi: 10.1080/01635588309513781.\u003c/li\u003e\n\u003cli\u003eO. M. Jensen, J. Wahrendorf, A. Rosenqvist, and A. Geser, \u0026ldquo;The reliability of questionnaire-derived historical dietary information and temporal stability of food habits in individuals.,\u0026rdquo; \u003cem\u003eAm J Epidemiol\u003c/em\u003e, vol. 120, no. 2, pp. 281\u0026ndash;90, Aug. 1984.\u003c/li\u003e\n\u003cli\u003eK. D. Lindsted and J. W. Kuzma, \u0026ldquo;Long‐term (24‐year) recall reliability in cancer cases and controls using a 21‐item food frequency questionnaire,\u0026rdquo; \u003cem\u003eNutrition and Cancer\u003c/em\u003e, vol. 12, no. 2, pp. 135\u0026ndash;149, Jan. 1989, doi: 10.1080/01635588909514012.\u003c/li\u003e\n\u003cli\u003eJ. Sobell, G. Block, P. Koslowe, J. Tobin, and R. Andres, \u0026ldquo;Validation of a retrospective questionnaire assessing diet 10-15 years ago.,\u0026rdquo; \u003cem\u003eAm J Epidemiol\u003c/em\u003e, vol. 130, no. 1, pp. 173\u0026ndash;87, Jul. 1989.\u003c/li\u003e\n\u003cli\u003eA. G. Uitterlinden, Y. Fang, J. B. J. van Meurs, H. A. P. Pols, and J. P. T. M. van Leeuwen, \u0026ldquo;Genetics and biology of vitamin D receptor polymorphisms,\u0026rdquo; \u003cem\u003eGene\u003c/em\u003e, vol. 338, no. 2. Gene, pp. 143\u0026ndash;156, Sep. 01, 2004. doi: 10.1016/j.gene.2004.05.014.\u003c/li\u003e\n\u003cli\u003eC. E. Powe \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Vitamin D\u0026ndash;Binding Protein and Vitamin D Status of Black Americans and White Americans,\u0026rdquo; \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, vol. 369, no. 21, pp. 1991\u0026ndash;2000, Nov. 2013, doi: 10.1056/nejmoa1306357.\u003c/li\u003e\n\u003cli\u003eS. S. Harris, \u0026ldquo;Vitamin D and African Americans,\u0026rdquo; in \u003cem\u003eJournal of Nutrition\u003c/em\u003e, 2006, vol. 136, no. 4, pp. 1126\u0026ndash;1129. doi: 10.1093/jn/136.4.1126.\u003c/li\u003e\n\u003cli\u003eD. M. Mitchell, M. P. Henao, J. S. Finkelstein, and S. A. M. Burnett-Bowie, \u0026ldquo;Prevalence and predictors of vitamin D deficiency in healthy adults,\u0026rdquo; \u003cem\u003eEndocrine Practice\u003c/em\u003e, vol. 18, no. 6, pp. 914\u0026ndash;923, Nov. 2012, doi: 10.4158/EP12072.OR.\u003c/li\u003e\n\u003cli\u003eC.-E. Bostr\u0026ouml;m \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air.,\u0026rdquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, vol. 110, no. suppl 3, pp. 451\u0026ndash;488, Jun. 2002, doi: 10.1289/ehp.110-1241197.\u003c/li\u003e\n\u003cli\u003eM. G. Knize, C. P. Salmon, P. Pais, and J. S. Felton, \u0026ldquo;Food heating and the formation of heterocyclic aromatic amine and polycyclic aromatic hydrocarbon mutagens/carcinogens.,\u0026rdquo; \u003cem\u003eAdv Exp Med Biol\u003c/em\u003e, vol. 459, pp. 179\u0026ndash;93, 1999, doi: 10.1007/978-1-4615-4853-9_12.\u003c/li\u003e\n\u003cli\u003eV. S. Li, \u0026ldquo;Content and emission characteristics of Artificial Wax Firelogs,\u0026rdquo; pp. 1\u0026ndash;16, 2006.\u003c/li\u003e\n\u003cli\u003eD. Loomis, W. Huang, and G. Chen, \u0026ldquo;The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: focus on China,\u0026rdquo; \u003cem\u003eChinese Journal of Cancer\u003c/em\u003e, vol. 33, no. 4, pp. 189\u0026ndash;196, Apr. 2014, doi: 10.5732/cjc.014.10028.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Distribution of demographic characteristics and PAH exposure measures for study participants, by case-control status, LIBCSP 1996\u0026ndash;1997.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"360\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases (n=1,026)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (n=1,074)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e24 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e37 (3.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e130 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e175 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e269 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e305 (28.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e259 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e289 (26.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e64-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e248 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e197 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e75-84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e85 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e60 (5.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e85+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e11 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e11 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e967 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e992 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eNon-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e57 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e82 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eHigh school \u0026shy;graduate or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e485 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e438 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e254 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e292 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eCollege graduate or post college\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e287 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e344 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;$34,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e361 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e326 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e$35,000- $69,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e332 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e382 (35.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026ge;$70,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e330 (32.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e365 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifetime Alcohol Intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;Non-drinkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e384 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e390 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003elifetime intake \u0026lt;15g/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e480 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e526 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003elifetime intake 15-30g/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e111 (10.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e83 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003elifetime intake \u0026gt;= 30g/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e51 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e74 (6.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e198 (13.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e171 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eParous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e1310 (86.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e1385 (89.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonths Lactation (mean (SD))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e3.66 (8.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e4.64 (10.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at Menarche (mean (SD))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e12.58 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e12.51 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin D (geometric mean (SD))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e35.14 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e38.15 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;25kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e457 (44.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e534 (50.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e25kg-30kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e330 (32.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e311 (29.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt;= 30kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e230 (22.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e214 (20.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Active Smoking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e828 (80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e873 (81.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e198 (19.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e199 (18.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eETS from Spouse\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eNever\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e514 (51.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e569 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e491 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e478 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrilled/barbecued/smoked meat intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026le;54 servings/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e287 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e340 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e55+ servings/year\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e680 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e666 (66.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynthetic Log Burning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eNever\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e855 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e932 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e170 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e140 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVehicular Traffic 1995\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;95th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e822 (95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e893 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003e=\u0026gt;95th percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e43 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e40 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"58.333333333333336%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.666666666666668%\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eAdditive and multiplicative effect measure modification by circulating vitamin D levels for multivariable-adjusted\u003csup\u003ea\u003c/sup\u003e associations between PAH sources and breast cancer in the LIBCSP 1996\u0026ndash;1997\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"999\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.213213213213214%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"20.42042042042042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin D \u0026ge; 30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"30.63063063063063%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin D \u0026lt;30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAH Source\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases/Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases/Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e\u003cstrong\u003eROR (95% CI)\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICR (95% CI)\u003c/strong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ee\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Active Smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e598/673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e230/200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.26 (0.96, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e144/150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.33 (1.06, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e54/49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.31 (0.86, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.04 (0.65, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e0.78 (0.47, 1.32)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e-0.28(-0.95, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003eETS from Spouse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e380/437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.0 ref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e134/132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.20 (0.90, 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e346/366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.04 (0.84, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e145/112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.41 (1.05, 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.17 (0.82, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.12 (0.74, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e0.16 (-0.34, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrilled/smoked meat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u0026le;54 servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e188/252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e99/88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.59 (1.11, 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e55+ servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e515/518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.51 (1.18, 1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e165/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.63 (1.20, 2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.02 (0.70, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e0.69 (0.44, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e-0.47 (-1.17, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynthetic Log\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e607/718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e248/214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.40 (1.12, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003eEver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e134/104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.70 (1.27, 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e36/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.24 (0.76, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e0.89 (0.53, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e0.52 (0.29, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e-0.86 (-1.67, -0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVehicular traffic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u0026lt;95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e593/673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e229/220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.18 (0.93, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.213213213213214%\"\u003e\n \u003cp\u003e\u0026ge;95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e35/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.21121121121121%\"\u003e\n \u003cp\u003e1.09 (0.66, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.20920920920921%\"\u003e\n \u003cp\u003e8/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.61061061061061%\"\u003e\n \u003cp\u003e1.48 (0.50, 4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.26 (0.42, 3.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.81081081081081%\"\u003e\n \u003cp\u003e1.15 (0.35, 3.83)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.606606606606607%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.012012012012011%\"\u003e\n \u003cp\u003e0.21(-1.68, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.306306306306307%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" width=\"100%\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e adjusted for age at diagnosis/index date, age at menarche, alcohol intake, BMI, parity, race, income, lactation, and education\u003cbr\u003e\u0026nbsp;\u003csup\u003eb\u0026nbsp;\u003c/sup\u003esingle reference coding lowest risk group exposure group vitamin D \u0026ge; 30\u003cbr\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003estratum specific coding within stratum of vitamin D\u0026nbsp;\u003cbr\u003e\u003csup\u003ed\u003c/sup\u003e ROR, ratio of the odds ratio, interaction variable p-value\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ee\u0026nbsp;\u003c/sup\u003eICR, interaction contrast ratio for additive interaction\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vitamin D, polycyclic aromatic hydrocarbons, breast cancer, vitamin D-related single nucleotide polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-2179262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2179262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePurpose:\u003c/em\u003e Our objective was to examine whether vitamin D and vitamin D-related SNPs modify the association between individual PAH sources and breast cancer .\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e This population-based case-control study included 1,026 women with first primary in situ or invasive breast cancer and 1,074 age-frequency matched controls. To evaluate effect measure modification (EMM) by measured vitamin D and vitamin D-related single nucleotide polymorphisms on multiple PAH measures (active cigarette smoking, smoking spouse, grilled/smoked meat consumption, synthetic log use, and vehicular traffic), we estimated odds ratios and 95% confidence intervals with unconditional logistic regression. EMM was assessed on the multiplicative (likelihood ratio tests) and additive scales (interaction contrast ratio).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e Vitamin D modified the synthetic log use breast cancer association on the additive scale (ICR= -0.86; 95% CI: -1.67, -0.04) and multiplicative scales (OR=0.52; 95% CI: 0.09, 0.94). Contrary to expectation, the stratum-specific OR for synthetic log use among those with vitamin D \u0026lt;30 ng/mL was below the null compared to the elevated OR found for those with vitamin D ≥ 30ng/mL (0.89 vs 1.70). The effect estimates for other PAHs were imprecise.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion: \u003c/em\u003eWe found evidence of EMM for the association between synthetic log use and breast cancer by vitamin D. The pattern of association was unexpected and requires replication. Potential EMM was observed with several other PAH measures. Additionally, we observed EMM for several SNPs which were not statistically significant after adjustment for multiple comparisons.\u003c/p\u003e","manuscriptTitle":"Polycyclic aromatic hydrocarbons and breast cancer: Effect measure modification by vitamin D and vitamin D-related single nucleotide polymorphisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-16 09:59:51","doi":"10.21203/rs.3.rs-2179262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8c59472-df8c-499b-b630-46306d80c5de","owner":[],"postedDate":"November 16th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-24T09:14:09+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-16 09:59:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2179262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2179262","identity":"rs-2179262","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}