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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 x 10.sup.-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 x 10.sup.-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 [ß.sub.BMI ] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 x 10.sup.-5). BMI was also associated with increased levels of glutamate (ß.sub.BMI : 0.12, p = 1.5 x 10.sup.-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., Author(s): Florence Guida 1, Vanessa Y. Tan 2,3, Laura J. Corbin 2,3, Karl Smith-Byrne 1, Karine Alcala 1, Claudia Langenberg 4, Isobel D. Stewart 4, Adam S. Butterworth 5,6,7,8, Praveen [...]

Background Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation. Methods and findings Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (OR.sub.SD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (OR.sub.SD : 1.56, 95% confidence interval [CI] 1.44-1.70), with comparable results for waist-to-hip ratio (OR.sub.SD : 1.63, 95% CI 1.40-1.90) and body fat percentage (OR.sub.SD : 1.66, 95% CI 1.44-1.90). This analysis further indicated that higher fasting insulin (OR.sub.SD : 1.82, 95% CI 1.30-2.55) and diastolic blood pressure (DBP; OR.sub.SD : 1.28, 95% CI 1.11-1.47), but not systolic blood pressure (OR.sub.SD : 0.98, 95% CI 0.84-1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose. Conclusions This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk., Author(s): Mattias Johansson 1,*, Robert Carreras-Torres 1, Ghislaine Scelo 1, Mark P. Purdue 2, Daniela Mariosa 1, David C. Muller 3, Nicolas J. Timpson 4, Philip C. Haycock 4, Kevin [...]

Background Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. Methods and Findings The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m.sup.2 ), (2) metabolically healthy/overweight (BMI [greater than or equal to] 25 kg/m.sup.2 ), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m.sup.2 ), and (4) metabolically unhealthy/overweight (BMI [greater than or equal to] 25 kg/m.sup.2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [[greater than or equal to]80 cm for women and [greater than or equal to]94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of Conclusions These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer., Author(s): Neil Murphy 1,*, Amanda J. Cross 1, Mustapha Abubakar 2, Mazda Jenab 3, Krasimira Aleksandrova 4, Marie-Christine Boutron-Ruault 5,6,7, Laure Dossus 5,6,7, Antoine Racine 5,6,7, Tilman Kühn 8, Verena [...]

Background Obesity has been positively associated with upper gastrointestinal cancers, but prospective data by subtype/subsite are limited. Obesity influences hormonal factors, which may play a role in these cancers. We examined anthropometry, body fat and reproductive factors in relation to oesophageal and gastric cancer by subtype/subsite in the UK Biobank cohort. Methods Among 458,713 UK Biobank participants, 339 oesophageal adenocarcinomas, 124 oesophageal squamous cell carcinomas, 137 gastric cardia and 92 gastric non-cardia cancers were diagnosed during a mean of 6.5 years follow-up. Cox models estimated multivariable hazard ratios (HRs) and 95% confidence intervals (CIs). Results Body mass index (BMI), hip circumference, waist circumference, waist-to-hip ratio, waist-to-height ratio, total body fat and trunk fat were positively associated with oesophageal adenocarcinoma (highest vs lowest category: HR = 2.33, 95%-CI:1.65-3.28; HR = 1.56, 95%-CI:1.15-2.13; HR = 2.30, 95%-CI:1.47-3.57; HR = 1.71, 95%-CI:1.01-2.90; HR = 2.87, 95%-CI:1.88-4.38; HR = 1.96, 95%-CI:1.30-2.96; HR = 2.34, 95%-CI:1.70-3.22, respectively). Although there were no statistically significant associations in combined sex analyses, BMI (HR = 1.83, 95%-CI:1.00-3.37), waist circumference (HR = 2.21, 95%-CI:1.27-3.84) and waist-to-hip ratio (HR = 1.92, 95%-CI:1.11-3.29) were associated with gastric cardia cancer in men; however, mutual adjustment attenuated the associations for BMI and waist-to-hip ratio. For oesophageal squamous cell carcinoma, statistically significant inverse associations were observed among women for BMI, hip circumference, waist circumference, waist-to-height ratio, total body fat and trunk fat, although they were based on small numbers. In addition, older age at first (HR = 0.44, 95%-CI:0.22-0.88) and last live birth (HR = 0.44, 95%-CI:0.22-0.87) were inversely associated with oesophageal squamous cell carcinoma and having a stillbirth/miscarriage/termination was positively associated (HR = 1.84, 95%-CI:1.10-3.07). Conclusions Obesity and abdominal obesity specifically may be a risk factor for oesophageal adenocarcinoma and gastric cardia cancer in men. Some reproductive factors may be associated with oesophageal squamous cell carcinoma in women. [ABSTRACT FROM AUTHOR]

Introduction: There are controversial results about the role of serum leptin and adiponectin levels as biomarkers of the severity of proteinuria in lupus nephritis. Objective: The aim of this study was to evaluate the relationship between serum leptin and adiponectin levels with severity of proteinuria secondary to lupus nephritis (LN). Methods: In a cross-sectional study, 103 women with systemic lupus erythematosus (SLE) were evaluated for kidney involvement. We compared 30 SLE patients with LN, all of them with proteinuria, versus 73 SLE patients without renal involvement (no LN). A comprehensive set of clinical and laboratory variables was assessed, including serum levels of leptin and adiponectin by ELISA. Multivariate analyses were used to adjust for potential confounders associated with proteinuria in LN. Results: We found higher adiponectin levels in the LN group compared with the no LN group (20.4 ± 10.3 vs 15.6 ± 7.8 μg/mL; p = 0.02), whereas no differences were observed in leptin levels (33.3 ± 31.4 vs 22.5 ± 25.5 ng/mL; p = 0.07). Severity of proteinuria correlated with an increase in adiponectin levels (r = 0.31; p = 0.001), but no correlation was observed with leptin. Adiponectin levels were not related to anti-dsDNA or anti-nucleosome antibodies. In the logistic regression, adiponectin levels were associated with a high risk of proteinuria in SLE (OR = 1.06; 95% CI 1.01–1.12; p = 0.02). Instead, leptin was not associated with LN. Conclusion: These findings indicate that adiponectin levels are useful markers associated with proteinuria in LN. Further longitudinal studies are required to identify if these levels are predictive of renal relapse. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the second most common cause of cancer death worldwide, but is still lacking sensitive and specific biomarkers for early diagnosis and prognosis. In this study, we applied targeted massively parallel semiconductor sequencing to assess methylation on a panel of genes (FBLN1, HINT2, LAMC1, LTBP1, LTBP2, PSMA2, PSMA7, PXDN, TGFB1, UBE2L3, VIM and YWHAZ) in plasma circulating cell-free DNA (cfDNA) and to evaluate the potential of these genes as HCC biomarkers in two different series, one from France (42 HCC cases and 42 controls) and one from Thailand (42 HCC cases, 26 chronic liver disease cases and 42 controls). We also analyzed a set of HCC and adjacent tissues and liver cell lines to further compare with ‘The Cancer Genome Atlas’ (TCGA) data. The methylation in cfDNA was detected for FBLN1, PSMA7, PXDN and VIM, with differences in methylation patterns between cases and controls for FBLN1 and VIM. The average methylation level across analyzed CpG-sites was associated with higher odds of HCC for VIM (1.48 [1.02, 2.16] for French cases and 2.18 [1.28, 3.72] for Thai cases), and lower odds of HCC for FBLN1 (0.89 [0.76, 1.03] for French cases and 0.75 [0.63, 0.88] for Thai cases). In conclusion, our study provides evidence that changes in VIM and FBLN1 methylation levels in cfDNA are associated with HCC and could represent useful plasma-based biomarkers. Also, the potential to investigate methylation patterns in cfDNA could bring new strategies for HCC detection and monitoring high-risk groups and response to treatment. [ABSTRACT FROM AUTHOR]

Prospective cohort studies have found that prediagnostic circulating vitamin B6 is inversely associated with both risk of kidney cancer and kidney cancer prognosis. We investigated whether circulating concentrations of vitamin B6 at kidney cancer diagnosis are associated with risk of death using a case-cohort study of 630 renal cell carcinoma (RCC) patients. Blood was collected at the time of diagnosis, and vitamin B6 concentrations were quantified using LC-MS/MS. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox regression models. After adjusting for stage, age, and sex, the hazard was 3 times lower among those in the highest compared to the lowest fourth of B6 concentration (HR4vs1 0.33, 95% CI [0.18, 0.60]). This inverse association was solely driven by death from RCC (HR4vs1 0.22, 95% CI [0.11, 0.46]), and not death from other causes (HR4vs1 0.89, 95% CI [0.35, 2.28], p-interaction = 0.008). These results suggest that circulating vitamin B6 could provide additional prognostic information for kidney cancer patients beyond that afforded by tumour stage. [ABSTRACT FROM AUTHOR]

Background: Detection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer. Material and Methods: Plasma samples from 100 early stage (I to IIIA) non–small-cell lung cancer (NSCLC) patients and 100 non-cancer controls were screened for 754 circulating microRNAs via qRT-PCR, using TaqMan MicroRNA Arrays. Logistic regression with a lasso penalty was used to select a panel of microRNAs that discriminate between cases and controls. Internal validation of model discrimination was conducted by calculating the bootstrap optimism-corrected AUC for the selected model. Results: We identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone. Conclusion: Our 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors. [ABSTRACT FROM AUTHOR]

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. [ABSTRACT FROM AUTHOR]