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Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers, imputation of missing data, (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis, (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Background Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). Methods and findings We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case–control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10−8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10−5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some—but not all—metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., −0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10−5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10−3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. Conclusions This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI—the principal modifiable risk factor of kidney cancer., In a case-control study, Florence Guida and colleagues identify metabolites associated with risk of kidney cancer, and use Mendelian randomization techniques to study the role of body mass index in this relationship., Author summary Why was this study done? Several modifiable risk factors have been established for kidney cancer, among which elevated body mass index (BMI) and obesity are central. The biological mechanisms underlying these relationships are poorly understood, but obesity-related metabolic perturbations may be important. What did the researchers do and find? We looked at the association between kidney cancer and the levels of 1,416 metabolites measured in blood on average 8 years before the disease onset. The study included 1,305 kidney cancer cases and 1,305 healthy controls. We found 25 metabolites robustly associated with kidney cancer risk. Specifically, multiple glycerophospholipids (GPLs) were inversely associated with risk, while several amino acids were positively associated with risk. Accounting for BMI highlighted that some—but not all—metabolites associated with kidney cancer risk are influenced by BMI. What do these findings mean? These findings illustrate the potential utility of prospectively measured metabolites in helping us to understand the aetiology of kidney cancer. By examining overlap between the metabolomic profile of prospective risk of kidney cancer and that of modifiable risk factors for the disease—in this case BMI—we can begin to identify biological pathways relevant to disease onset.

Experimental studies have reported on the anti-inflammatory properties of polyphenols. However, results from epidemiological investigations have been inconsistent and especially studies using biomarkers for assessment of polyphenol intake have been scant. We aimed to characterise the association between plasma concentrations of thirty-five polyphenol compounds and low-grade systemic inflammation state as measured by high-sensitivity C-reactive protein (hsCRP). A cross-sectional data analysis was performed based on 315 participants in the European Prospective Investigation into Cancer and Nutrition cohort with available measurements of plasma polyphenols and hsCRP. In logistic regression analysis, the OR and 95 % CI of elevated serum hsCRP (>3 mg/l) were calculated within quartiles and per standard deviation higher level of plasma polyphenol concentrations. In a multivariable-adjusted model, the sum of plasma concentrations of all polyphenols measured (per standard deviation) was associated with 29 (95 % CI 50, 1) % lower odds of elevated hsCRP. In the class of flavonoids, daidzein was inversely associated with elevated hsCRP (OR 0·66, 95 % CI 0·46, 0·96). Among phenolic acids, statistically significant associations were observed for 3,5-dihydroxyphenylpropionic acid (OR 0·58, 95 % CI 0·39, 0·86), 3,4-dihydroxyphenylpropionic acid (OR 0·63, 95 % CI 0·46, 0·87), ferulic acid (OR 0·65, 95 % CI 0·44, 0·96) and caffeic acid (OR 0·69, 95 % CI 0·51, 0·93). The odds of elevated hsCRP were significantly reduced for hydroxytyrosol (OR 0·67, 95 % CI 0·48, 0·93). The present study showed that polyphenol biomarkers are associated with lower odds of elevated hsCRP. Whether diet rich in bioactive polyphenol compounds could be an effective strategy to prevent or modulate deleterious health effects of inflammation should be addressed by further well-powered longitudinal studies.

Experimental studies have reported on the anti-inflammatory properties of polyphenols. However, results from epidemiological investigations have been inconsistent and especially studies using biomarkers for assessment of polyphenol intake have been scant. We aimed to characterise the association between plasma concentrations of thirty-five polyphenol compounds and low-grade systemic inflammation state as measured by high-sensitivity C-reactive protein (hsCRP). A cross-sectional data analysis was performed based on 315 participants in the European Prospective Investigation into Cancer and Nutrition cohort with available measurements of plasma polyphenols and hsCRP. In logistic regression analysis, the OR and 95 % CI of elevated serum hsCRP (>3 mg/l) were calculated within quartiles and per standard deviation higher level of plasma polyphenol concentrations. In a multivariable-adjusted model, the sum of plasma concentrations of all polyphenols measured (per standard deviation) was associated with 29 (95 % CI 50, 1) % lower odds of elevated hsCRP. In the class of flavonoids, daidzein was inversely associated with elevated hsCRP (OR 0·66, 95 % CI 0·46, 0·96). Among phenolic acids, statistically significant associations were observed for 3,5-dihydroxyphenylpropionic acid (OR 0·58, 95 % CI 0·39, 0·86), 3,4-dihydroxyphenylpropionic acid (OR 0·63, 95 % CI 0·46, 0·87), ferulic acid (OR 0·65, 95 % CI 0·44, 0·96) and caffeic acid (OR 0·69, 95 % CI 0·51, 0·93). The odds of elevated hsCRP were significantly reduced for hydroxytyrosol (OR 0·67, 95 % CI 0·48, 0·93). The present study showed that polyphenol biomarkers are associated with lower odds of elevated hsCRP. Whether diet rich in bioactive polyphenol compounds could be an effective strategy to prevent or modulate deleterious health effects of inflammation should be addressed by further well-powered longitudinal studies. © The Authors 2019.

Experimental studies have reported on the anti-inflammatory properties of polyphenols. However, results from epidemiological investigations have been inconsistent and especially studies using biomarkers for assessment of polyphenol intake have been scant. We aimed to characterise the association between plasma concentrations of thirty-five polyphenol compounds and low-grade systemic inflammation state as measured by high-sensitivity C-reactive protein (hsCRP). A cross-sectional data analysis was performed based on 315 participants in the European Prospective Investigation into Cancer and Nutrition cohort with available measurements of plasma polyphenols and hsCRP. In logistic regression analysis, the OR and 95 % CI of elevated serum hsCRP (>3 mg/l) were calculated within quartiles and per standard deviation higher level of plasma polyphenol concentrations. In a multivariable-adjusted model, the sum of plasma concentrations of all polyphenols measured (per standard deviation) was associated with 29 (95 % CI 50, 1) % lower odds of elevated hsCRP. In the class of flavonoids, daidzein was inversely associated with elevated hsCRP (OR 0 center dot 66, 95 % CI 0 center dot 46, 0 center dot 96). Among phenolic acids, statistically significant associations were observed for 3,5-dihydroxyphenylpropionic acid (OR 0 center dot 58, 95 % CI 0 center dot 39, 0 center dot 86), 3,4-dihydroxyphenylpropionic acid (OR 0 center dot 63, 95 % CI 0 center dot 46, 0 center dot 87), ferulic acid (OR 0 center dot 65, 95 % CI 0 center dot 44, 0 center dot 96) and caffeic acid (OR 0 center dot 69, 95 % CI 0 center dot 51, 0 center dot 93). The odds of elevated hsCRP were significantly reduced for hydroxytyrosol (OR 0 center dot 67, 95 % CI 0 center dot 48, 0 center dot 93). The present study showed that polyphenol biomarkers are associated with lower odds of elevated hsCRP. Whether diet rich in bioactive polyphenol compounds could be an effective strategy to prevent or modulate deleterious health effects of inflammation should be addressed by further well-powered longitudinal studies.

Emerging evidence suggests that a metabolic profile associated with obesity may be a more relevant risk factor for some cancers than adiposity per se. Basal metabolic rate (BMR) is an indicator of overall body metabolism and may be a proxy for the impact of a specific metabolic profile on cancer risk. Therefore, we investigated the association of predicted BMR with incidence of 13 obesity-related cancers in the European Prospective Investigation into Cancer and Nutrition (EPIC). BMR at baseline was calculated using the WHO/FAO/UNU equations and the relationships between BMR and cancer risk were investigated using multivariable Cox proportional hazards regression models. A total of 141,295 men and 317,613 women, with a mean follow-up of 14 years were included in the analysis. Overall, higher BMR was associated with a greater risk for most cancers that have been linked with obesity. However, among normal weight participants, higher BMR was associated with elevated risks of esophageal adenocarcinoma (hazard ratio per 1-standard deviation change in BMR [HR1-SD]: 2.46; 95% CI 1.20; 5.03) and distal colon cancer (HR1-SD: 1.33; 95% CI 1.001; 1.77) among men and with proximal colon (HR1-SD: 1.16; 95% CI 1.01; 1.35), pancreatic (HR1-SD: 1.37; 95% CI 1.13; 1.66), thyroid (HR1-SD: 1.65; 95% CI 1.33; 2.05), postmenopausal breast (HR1-SD: 1.17; 95% CI 1.11; 1.22) and endometrial (HR1-SD: 1.20; 95% CI 1.03; 1.40) cancers in women. These results indicate that higher BMR may be an indicator of a metabolic phenotype associated with risk of certain cancer types, and may be a useful predictor of cancer risk independent of body fatness. © 2019 International Agency for Research on Cancer (IARC/WHO); licensed by UICC

Background: The use of biomarkers of environmental exposure to explore new risk factors for pancreatic cancer presents clinical, logistic, and methodological challenges that are also relevant in research on other complex diseases. Objectives: First, to summarize the main design features of a prospective case-control study –nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort– on plasma concentrations of persistent organic pollutants (POPs) and pancreatic cancer risk. And second, to assess the main methodological challenges posed by associations among characteristics and habits of study participants, fasting status, time from blood draw to cancer diagnosis, disease progression bias, basis of cancer diagnosis, and plasma concentrations of lipids and POPs. Results from etiologic analyses on POPs and pancreatic cancer risk, and other analyses, will be reported in future articles. Methods: Study subjects were 1533 participants (513 cases and 1020 controls matched by study centre, sex, age at blood collection, date and time of blood collection, and fasting status) enrolled between 1992 and 2000. Plasma concentrations of 22 POPs were measured by gas chromatography - triple quadrupole mass spectrometry (GC-MS/MS). To estimate the magnitude of the associations we calculated multivariate-adjusted odds ratios by unconditional logistic regression, and adjusted geometric means by General Linear Regression Models. Results: There were differences among countries in subjects’ characteristics (as age, gender, smoking, lipid and POP concentrations), and in study characteristics (as time from blood collection to index date, year of last follow-up, length of follow-up, basis of cancer diagnosis, and fasting status). Adjusting for centre and time of blood collection, no factors were significantly associated with fasting status. Plasma concentrations of lipids were related to age, body mass index, fasting, country, and smoking. We detected and quantified 16 of the 22 POPs in more than 90% of individuals. All 22 POPs were detected in some participants, and the smallest number of POPs detected in one person was 15 (median, 19) with few differences by country. The highest concentrations were found for p,p’-DDE, PCBs 153 and 180 (median concentration: 3371, 1023, and 810 pg/mL, respectively). We assessed the possible occurrence of disease progression bias (DPB) in eight situations defined by lipid and POP measurements, on one hand, and by four factors: interval from blood draw to index date, tumour subsite, tumour stage, and grade of differentiation, on the other. In seven of the eight situations results supported the absence of DPB. Conclusions: The coexistence of differences across study centres in some design features and participant characteristics is of relevance to other multicentre studies. Relationships among subjects’ characteristics and among such characteristics and design features may play important roles in the forthcoming analyses on the association between plasma concentrations of POPs and pancreatic cancer risk. © 2018 Elsevier Inc.

Background: Epidemiological studies suggest that haem iron, which is found predominantly in red meat and increases endogenous formation of carcinogenic N-nitroso compounds, may be positively associated with lung cancer. The objective was to examine the relationship between haem iron intake and lung cancer risk using detailed smoking history data and serum cotinine to control for potential confounding. Methods: In the European Prospective Investigation into Cancer and Nutrition (EPIC), 416,746 individuals from 10 countries completed demographic and dietary questionnaires at recruitment. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident lung cancer (n = 3731) risk relative to haem iron, non-haem iron, and total dietary iron intake. A corresponding analysis was conducted among a nested subset of 800 lung cancer cases and 1489 matched controls for whom serum cotinine was available. Results: Haem iron was associated with lung cancer risk, including after adjustment for details of smoking history (time since quitting, number of cigarettes per day): as a continuous variable (HR per 0.3 mg/1000 kcal 1.03, 95% CI 1.00–1.07), and in the highest versus lowest quintile (HR 1.16, 95% CI 1.02–1.32; trend across quintiles: P = 0.035). In contrast, non-haem iron intake was related inversely with lung cancer risk; however, this association attenuated after adjustment for smoking history. Additional adjustment for serum cotinine did not considerably alter the associations detected in the nested case–control subset. Conclusions: Greater haem iron intake may be modestly associated with lung cancer risk. © 2018, Springer Nature Limited.

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (PACT = 0.10; PACT significance threshold was P < 0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Background: Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. Methods: A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. Results: Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. Conclusions: These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies. © 2019 The Author(s).

Importance: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. Objective: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. Design, Setting, and Participants: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). Main Outcomes and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). Results: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P =.003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. Conclusions and Relevance: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.. © 2018 American Medical Association. All rights reserved.

Background Chronic inflammation plays a critical role in the pathogenesis of the 2 major types of gastric cancer. Several foods, nutrients, and nonnutrient food components seem to be involved in the regulation of chronic inflammation. Objective We assessed the association between the inflammatory potential of the diet and the risk of gastric carcinoma, overall and for the 2 major subsites: cardia cancers and noncardia cancers. Design A total of 476,160 subjects (30% men, 70% women) from the European Investigation into Cancer and Nutrition (EPIC) study were followed for 14 y, during which 913 incident cases of gastric carcinoma were identified, including 236 located in the cardia, 341 in the distal part of the stomach (noncardia), and 336 with overlapping or unknown tumor site. The dietary inflammatory potential was assessed by means of an inflammatory score of the diet (ISD), calculated with the use of 28 dietary components and their corresponding inflammatory scores. The association between the ISD and gastric cancer risk was estimated by HRs and 95% CIs calculated by multivariate Cox regression models adjusted for confounders. Results The inflammatory potential of the diet was associated with an increased risk of gastric cancer. The HR (95% CI) for each increase in 1 SD of the ISD were 1.25 (1.12, 1.39) for all gastric cancers, 1.30 (1.06, 1.59) for cardia cancers, and 1.07 (0.89, 1.28) for noncardia cancers. The corresponding values for the highest compared with the lowest quartiles of the ISD were 1.66 (1.26, 2.20), 1.94 (1.14, 3.30), and 1.07 (0.70, 1.70), respectively. Conclusions Our results suggest that low-grade chronic inflammation induced by the diet may be associated with gastric cancer risk. This pattern seems to be more consistent for gastric carcinomas located in the cardia than for those located in the distal stomach. This study is listed on the ISRCTN registry as ISRCTN12136108. © 2018 American Society for Nutrition. All rights reserved.

Background: Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features. In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results: A sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions: The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation. © 2018 The Author(s).

Purpose: Renal cell carcinoma (RCC) has the potential for cure with surgery when diagnosed at an early stage. Kidney injury molecule-1 (KIM-1) has been shown to be elevated in the plasma of RCC patients. We aimed to test whether plasma KIM-1 could represent a means of detecting RCC prior to clinical diagnosis. Experimental Design: KIM-1 concentrations were measured in prediagnostic plasma from 190 RCC cases and 190 controls nested within a population-based prospective cohort study. Cases had entered the cohort up to 5 years before diagnosis, and controls were matched on cases for date of birth, date at blood donation, sex, and country. We applied conditional logistic regression and flexible parametric survival models to evaluate the association between plasma KIM-1 concentrations and RCC risk and survival. Results: The incidence rate ratio (IRR) of RCC for a doubling in KIM-1 concentration was 1.71 [95% confidence interval (CI), 1.44–2.03, P ¼ 4.1 1023], corresponding to an IRR of 63.3 (95% CI, 16.2–246.9) comparing the 80th to the 20th percentiles of the KIM-1 distribution in this sample. Compared with a risk model including known risk factors of RCC (age, sex, country, body mass index, and tobacco smoking status), a risk model additionally including KIM-1 substantially improved discrimination between cases and controls (area under the receiver-operating characteristic curve of 0.8 compared with 0.7). High plasma KIM-1 concentrations were also associated with poorer survival (P ¼ 0.0053). Conclusions: Plasma KIM-1 concentrations could predict RCC incidence up to 5 years prior to diagnosis and were associated with poorer survival. © 2018 American Association for Cancer Research.