Observational and genetic associations between cardiorespiratory fitness and cancer: a UK Biobank and international consortia study.

BACKGROUND: The association of fitness with cancer risk is not clear. METHODS: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method. RESULTS: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min-1⋅kg-1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73-0.89), colorectal (0.94, 0.90-0.99), and breast cancer (0.96, 0.92-0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min-1⋅kg-1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86-0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. DISCUSSION: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.

Plasma metabolites in childhood Burkitt lymphoma cases and cancer-free controls in Uganda.

INTRODUCTION: Burkitt lymphoma (BL) is an aggressive non-Hodgkin lymphoma associated with Plasmodium falciparum and Epstein-Barr virus, both of which affect metabolic pathways. The metabolomic patterns of BL is unknown. MATERIALS AND METHODS: We measured 627 metabolites in pre-chemotherapy treatment plasma samples from 25 male children (6-11 years) with BL and 25 cancer-free area- and age-frequency-matched male controls from the Epidemiology of Burkitt Lymphoma in East African Children and Minors study in Uganda using liquid chromatography-tandem mass spectrometry. Unconditional, age-adjusted logistic regression analysis was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) for the BL association with 1-standard deviation increase in the log-metabolite concentration, adjusting for multiple comparisons using false discovery rate (FDR) thresholds and Bonferroni correction. RESULTS: Compared to controls, levels for 42 metabolite concentrations differed in BL cases (FDR < 0.001), including triacylglyceride (18:0_38:6), alpha-aminobutyric acid (AABA), ceramide (d18:1/20:0), phosphatidylcholine ae C40:6 and phosphatidylcholine C38:6 as the top signals associated with BL (ORs = 6.9 to 14.7, P < 2.4✕10- 4). Two metabolites (triacylglyceride (18:0_38:6) and AABA) selected using stepwise logistic regression discriminated BL cases from controls with an area under the curve of 0.97 (95% CI: 0.94, 1.00). CONCLUSION: Our findings warrant further examination of plasma metabolites as potential biomarkers for BL risk/diagnosis.

Serum perfluorooctane sulfonate and perfluorooctanoate and risk of postmenopausal breast cancer according to hormone receptor status: An analysis in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Per- and polyfluoroalkyl substances (PFAS) are highly persistent endocrine-disrupting chemicals that may contribute to breast cancer development; however, epidemiologic evidence is limited. We investigated associations between prediagnostic serum levels of perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) and postmenopausal breast cancer risk, overall and by hormone receptor status, in a nested case-control study of 621 cases and 621 matched controls in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. PFOS and PFOA levels were determined based on serum metabolomic profiling performed using ultraperformance liquid chromatography-tandem mass spectrometry. We used multivariable conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between each PFAS and breast cancer risk, overall, by estrogen receptor (ER) or progesterone receptor (PR) status, and by joint ER/PR status. We found little evidence of association between PFOS or PFOA and breast cancer risk overall. However, in subtype-specific analyses, we observed statistically significant increased risks of ER+, PR+, and ER+/PR+ tumors for the third vs lowest quartile of serum PFOS (ORs [95% CIs] = 1.59 [1.01-2.50], 2.34 [1.29-4.23], and 2.19 [1.21-3.98], respectively) and elevated but nonstatistically significant ORs for the fourth quartile. Conversely, for PFOA, modest positive associations with ER-, PR-, ER+/PR-, and ER-/PR- tumors were generally seen in the upper quartiles. Our findings contribute evidence supporting positive associations between serum PFOS and hormone receptor-positive tumors, and possibly between PFOA and receptor-negative tumors. Future prospective studies incorporating tumor hormone receptor status are needed to better understand the role of PFAS in breast cancer etiology.

Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations.

BACKGROUND: Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles. OBJECTIVES: Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles. METHODS: We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts. RESULTS: Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED. CONCLUSIONS: Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.

Lipidomics and pancreatic cancer risk in two prospective studies.

Pancreatic ductal carcinoma (PDAC) is highly fatal with limited understanding of mechanisms underlying its carcinogenesis. We comprehensively investigated whether lipidomic measures were associated with PDAC in two prospective studies. We measured 904 lipid species and 252 fatty acids across 15 lipid classes in pre-diagnostic serum (up to 24 years) in a PDAC nested-case control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, NCT00002540) with 332 matched case-control sets including 272 having serial blood samples and Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC, NCT00342992) with 374 matched case-control sets. Controls were matched to cases by cohort, age, sex, race, and date at blood draw. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) per one-standard deviation increase in log-lipid concentrations within each cohort, and combined ORs using fixed-effects meta-analyses. Forty-three lipid species were associated with PDAC (false discovery rate, FDR ≤ 0.10), including lysophosphatidylcholines (LPC, n = 2), phosphatidylethanolamines (PE, n = 17), triacylglycerols (n = 13), phosphatidylcholines (PC, n = 3), diacylglycerols (n = 4), monoacylglycerols (MAG, n = 2), cholesteryl esters (CE, n = 1), and sphingomyelins (n = 1). LPC(18:2) and PE(O-16:0/18:2) showed significant inverse associations with PDAC at the Bonferroni threshold (P value < 5.5 × 10-5). The fatty acids LPC[18:2], LPC[16:0], PC[15:0], MAG[18:1] and CE[22:0] were significantly associated with PDAC (FDR < 0.10). Similar associations were observed in both cohorts. There was no significant association for the differences between PLCO serial lipidomic measures or heterogeneity by follow-up time overall. Results support that the pre-diagnostic serum lipidome, including 43 lipid species from 8 lipid classes and 5 fatty acids, is associated with PDAC.

Sources of Variability in Serum Lipidomic Measurements and Implications for Epidemiologic Studies.

Epidemiological studies using lipidomic approaches can identify lipids associated with exposures and diseases. We evaluated the sources of variability of lipidomic profiles measured in blood samples and the implications when designing epidemiologic studies. We measured 918 lipid species in nonfasting baseline serum from 693 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, with 570 participants having serial blood samples separated by 1-5 years and 72 blinded replicate quality control samples. Blood samples were collected during 1993-2006. For each lipid species, we calculated the between-individual, within-individual, and technical variances, and we estimated the statistical power to detect associations in case-control studies. The technical variability was moderate, with a median intraclass correlation coefficient of 0.79. The combination of technical and within-individual variances accounted for most of the variability in 74% of the lipid species. For an average true relative risk of 3 (comparing upper and lower quartiles) after correction for multiple comparisons at the Bonferroni significance threshold (α = 0.05/918 = 5.45 ×10-5), we estimated that a study with 500, 1,000, and 5,000 total participants (1:1 case-control ratio) would have 19%, 57%, and 99% power, respectively. Epidemiologic studies examining associations between lipidomic profiles and disease require large samples sizes to detect moderate effect sizes associations.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia.

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

Body Composition and Metabolomics in the Alberta Physical Activity and Breast Cancer Prevention Trial.

BACKGROUND: Obesity is correlated with many biomarkers, but the extent to which these correlate with underlying body composition is poorly understood. OBJECTIVES: Our objectives were to 1) describe/compare distinct contributions of fat/lean mass with BMI-metabolite correlations and 2) identify novel metabolite biomarkers of fat/lean mass. METHODS: The Alberta Physical Activity and Breast Cancer Prevention Trial was a 2-center randomized trial of healthy, inactive, postmenopausal women (n = 304). BMI (in kg/m2) was calculated using weight and height, whereas DXA estimated fat/lean mass. Ultra-performance liquid chromatography and mass spectrometry measured relative concentrations of serum metabolite concentrations. We estimated partial Pearson correlations between 1052 metabolites and BMI, adjusting for age, smoking, and site. Fat mass index (FMI; kg/m2) and lean mass index (LMI; kg/m2) correlations were estimated similarly, with mutual adjustment to evaluate independent effects. RESULTS: Using a Bonferroni-corrected α level <4.75 × 10-5,  we observed 53 BMI-correlated metabolites (|r| = 0.24-0.42). Of those, 21 were robustly correlated with FMI (|r| > 0.20), 25 modestly (0.10 ≤ |r| ≤ 0.20), and 7 virtually null (|r| < 0.10). Ten of 53 were more strongly correlated with LMI than with FMI. Examining non-BMI-correlated metabolites, 6 robustly correlated with FMI (|r| = 0.24-0.31) and 2 with LMI (r = 0.25-0.26). For these, correlations for fat and lean mass were in opposing directions compared with BMI-correlated metabolites, in which correlations were mostly in the same direction. CONCLUSIONS: Our results demonstrate how a thorough evaluation of the components of fat and lean mass, along with BMI, provides a more accurate assessment of the associations between body composition and metabolites than BMI alone. Such an assessment makes evident that some metabolites correlated with BMI predominantly reflect lean mass rather than fat, and some metabolites related to body composition are not correlated with BMI. Correctly characterizing these relations is important for an accurate understanding of how and why obesity is associated with disease.

Independent and joint associations of weightlifting and aerobic activity with all-cause, cardiovascular disease and cancer mortality in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

OBJECTIVES: Both aerobic moderate to vigorous physical activity (MVPA) and muscle-strengthening exercise (MSE) are recommended, but the mortality benefits of weightlifting, a specific type of MSE, are limited. METHODS: In the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, we used Cox proportional hazards regression to calculate hazard ratios (HRs) and 95% CIs for the associations between weightlifting and mortality, adjusting for demographics, lifestyle and behavioural risk factors. The sample included 99 713 adults who completed the follow-up questionnaire that assessed weightlifting who were subsequently followed up through 2016 to determine mortality (median 9, IQR 7.6-10.6 years). RESULTS: Mean age at the follow-up questionnaire was 71.3 (IQR 66-76) years, 52.6% female, with mean body mass index of 27.8 (SD 4.9) kg/m2. Weightlifting was associated with a 9% lower risk of all-cause mortality (HR=0.91 (95% CI 0.88 to 0.94)) and CVD mortality (0.91 (95% CI 0.86 to 0.97)) after adjusting for MVPA. Joint models revealed that adults who met aerobic MVPA recommendations but did not weightlift had a 32% lower all-cause mortality risk (HR=0.68 (95% CI 0.65 to 0.70)), while those who also reported weightlifting 1-2 times/week had a 41% lower risk (HR=0.59 (95% CI 0.54 to 0.64)), both compared with adults reporting no aerobic MVPA or weightlifting. Without adjustment for MVPA, weightlifting was associated with lower cancer mortality (HR=0.85 (95% CI 0.80 to 0.91)). CONCLUSION: Weightlifting and MVPA were associated with a lower risk of all-cause and CVD mortality, but not cancer mortality. Adults who met recommended amounts of both types of exercise appeared to gain additional benefit.

Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies.

Many epidemiologic studies use metabolomics for discovery-based research. The degree to which sample handling may influence findings, however, is poorly understood. In 2016, serum samples from 13 volunteers from the US Department of Agriculture's Beltsville Human Nutrition Research Center were subjected to different clotting (30 minutes/120 minutes) and refrigeration (0 minutes/24 hours) conditions, as well as different numbers (0/1/4) and temperatures (ice/refrigerator/room temperature) of thaws. The median absolute percent difference (APD) between metabolite levels and correlations between levels across conditions were estimated for 628 metabolites. The potential for handling artifacts to induce false-positive associations was estimated using variable hypothetical scenarios in which 1%-100% of case samples had different handling than control samples. All handling conditions influenced metabolite levels. Across metabolites, the median APD when extending clotting time was 9.08%. When increasing the number of thaws from 0 to 4, the median APD was 10.05% for ice and 5.54% for room temperature. Metabolite levels were correlated highly across conditions (all r's ≥ 0.84), indicating that relative ranks were preserved. However, if handling varied even modestly by case status, our hypotheticals showed that results can be biased and can result in false-positive findings. Sample handling affects levels of metabolites, and special care should be taken to minimize effects. Shorter room-temperature thaws should be preferred over longer ice thaws, and handling should be meticulously matched by case status.

Identification of 102 Correlations between Serum Metabolites and Habitual Diet in a Metabolomics Study of the Prostate, Lung, Colorectal, and Ovarian Cancer Trial.

BACKGROUND: Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. OBJECTIVE: The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. METHODS: We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55-75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. RESULTS: Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44). CONCLUSIONS: Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed.

Group testing in mediation analysis.

We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women.

Prospective serum metabolomic profiling of lethal prostate cancer.

Impaired metabolism may play an important role in the pathogenesis of lethal prostate cancer, yet there is a paucity of evidence regarding the association. We conducted a large prospective serum metabolomic analysis of lethal prostate cancer in 523 cases and 523 matched controls nested within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from baseline fasting serum collection to prostate cancer death was 18 years (maximum 30 years). We identified 860 known biochemicals through an ultrahigh-performance LC-MS/MS platform. Conditional logistic regression models estimated odds ratios (OR) and 95% confidence intervals of risk associated with 1-standard deviation (s.d.) increases in log-metabolite signals. We identified 34 metabolites associated with lethal prostate cancer with a false discovery rate (FDR) < 0.15. Notably, higher serum thioproline, and thioproline combined with two other cysteine-related amino acids and redox metabolites, cystine and cysteine, were associated with reduced risk (1-s.d. OR = 0.75 and 0.71, respectively; p ≤ 8.2 × 10-5 ). By contrast, the dipeptide leucylglycine (OR = 1.36, p = 8.2 × 10-5 ), and three gamma-glutamyl amino acids (OR = 1.28-1.30, p ≤ 4.6 × 10-4 ) were associated with increased risk of lethal prostate cancer. Cases with metastatic disease at diagnosis (n = 179) showed elevated risk for several lipids, including especially the ketone body 3-hydroxybutyrate (BHBA), acyl carnitines, and dicarboxylic fatty acids (1.37 ≤ OR ≤ 1.49, FDR < 0.15). These findings provide a prospective metabolomic profile of lethal prostate cancer characterized by altered biochemicals in the redox, dipeptide, pyrimidine, and gamma-glutamyl amino acid pathways, whereas ketone bodies and fatty acids were associated specifically with metastatic disease.

Breast cancer risk in relation to plasma metabolites among Hispanic and African American women.

PURPOSE: The metabolic etiology of breast cancer has been explored in the past several years using metabolomics. However, most of these studies only included non-Hispanic White individuals. METHODS: To fill this gap, we performed a two-step (discovery and validation) metabolomics profiling in plasma samples from 358 breast cancer patients and 138 healthy controls. All study subjects were either Hispanics or non-Hispanic African Americans. RESULTS: A panel of 14 identified metabolites significantly differed between breast cancer cases and healthy controls in both the discovery and validation sets. Most of these identified metabolites were lipids. In the pathway analysis, citrate cycle (TCA cycle), arginine and proline metabolism, and linoleic acid metabolism pathways were observed, and they significantly differed between breast cancer cases and healthy controls in both sets. From those 14 metabolites, we selected 9 non-correlated metabolites to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.87- and 1.63-fold increased risk of breast cancer in the discovery and validation sets, respectively (Odds ratio (OR) 1.87, 95% Confidence interval (CI) 1.50, 2.32; OR 1.63, 95% CI 1.36, 1.95). CONCLUSIONS: In summary, our study identified metabolic profiles and pathways that significantly differed between breast cancer cases and healthy controls in Hispanic or non-Hispanic African American women. The results from our study might provide new insights on the metabolic etiology of breast cancer.

Correction to: Breast cancer risk in relation to plasma metabolites among Hispanic and African American women.

In the original publication of the article, the sixth author name Krita A. Zanetti was mistakenly included as co-author. The corrected author group is given in the correction article. The original article has been corrected.

FWER and FDR control when testing multiple mediators.

Motivation: The biological pathways linking exposures and disease risk are often poorly understood. To gain insight into these pathways, studies may try to identify biomarkers that mediate the exposure/disease relationship. Such studies often simultaneously test hundreds or thousands of biomarkers. Results: We consider a set of m biomarkers and a corresponding set of null hypotheses, where the jth null hypothesis states that biomarker j does not mediate the exposure/disease relationship. We propose a Multiple Comparison Procedure (MCP) that rejects a set of null hypotheses or, equivalently, identifies a set of mediators, while asymptotically controlling the Family-Wise Error Rate (FWER) or False Discovery Rate (FDR). We use simulations to show that, compared to currently available methods, our proposed method has higher statistical power to detect true mediators. We then apply our method to a breast cancer study and identify nine metabolites that may mediate the known relationship between an increased BMI and an increased risk of breast cancer. Availability and implementation: R package MultiMed on https://github.com/SiminaB/MultiMed. Supplementary information: Supplementary data are available at Bioinformatics online.

Serum Metabolomic Profiling of All-Cause Mortality: A Prospective Analysis in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study Cohort.

Tobacco use, hypertension, hyperglycemia, overweight, and inactivity are leading causes of overall and cardiovascular disease (CVD) mortality worldwide, yet the relevant metabolic alterations responsible are largely unknown. We conducted a serum metabolomic analysis of 620 men in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (1985-2013). During 28 years of follow-up, there were 435 deaths (197 CVD and 107 cancer). The analysis included 406 known metabolites measured with ultra-high-performance liquid chromatography/mass spectrometry-gas chromatography/mass spectrometry. We used Cox regression to estimate mortality hazard ratios for a 1-standard-deviation difference in metabolite signals. The strongest associations with overall mortality were N-acetylvaline (hazard ratio (HR) = 1.28; P < 4.1 × 10-5, below Bonferroni statistical threshold) and dimethylglycine, 7-methylguanine, C-glycosyltryptophan, taurocholate, and N-acetyltryptophan (1.23 ≤ HR ≤ 1.32; 5 × 10-5 ≤ P ≤ 1 × 10-4). C-Glycosyltryptophan, 7-methylguanine, and 4-androsten-3ß,17ß-diol disulfate were statistically significantly associated with CVD mortality (1.49 ≤ HR ≤ 1.62, P < 4.1 × 10-5). No metabolite was associated with cancer mortality, at a false discovery rate of <0.1. Individuals with a 1-standard-deviation higher metabolite risk score had increased all-cause and CVD mortality in the test set (HR = 1.4, P = 0.05; HR = 1.8, P = 0.003, respectively). The several serum metabolites and their composite risk score independently associated with all-cause and CVD mortality may provide potential leads regarding the molecular basis of mortality.

Alcohol and oestrogen metabolites in postmenopausal women in the Women's Health Initiative Observational Study.

BACKGROUND: Alcohol consumption is associated with an increased risk of several cancers. Potential mechanisms include altered oestrogen metabolism. Parent oestrogens metabolise into alternate pathways of oestrogen metabolites that may have variable effects on cancer pathogenesis. We examined associations of alcohol consumption with circulating oestrogen/oestrogen metabolites in postmenopausal women in the Women's Health Initiative (WHI)-Observational Study (OS). METHODS: We conducted a cross-sectional analysis of prediagnosis ovarian/endometrial cancer case-control data within WHI-OS (N=1864). Alcohol consumption was measured by validated food frequency questionnaire. Fasting serum parent oestrogens/oestrogen metabolites were assayed using liquid chromatography tandem mass-spectrometry. Geometric mean analyte concentrations (GM, pmol l-1) were calculated by alcohol category using inverse-probability weighted linear regression, adjusting for venepuncture age/year, race, smoking, body mass index, years since menopause, oral contraceptive duration, caffeine intake, and physical activity. RESULTS: There was evidence for a positive association between alcohol consumption and oestrone, oestradiol and 2-hydroxylation oestrogen metabolite concentrations among menopausal hormone therapy (MHT) users. We observed an association between liquor consumption and parent oestrogens among non-MHT users, who consumed larger doses of liquor than MHT users. CONCLUSIONS: Among postmenopausal women, the association between alcohol intake and parent oestrogen, but not oestrogen metabolite concentrations, may be influenced by MHT and type of alcohol.

Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry.

BACKGROUND: Metabolomics is emerging as an important tool for detecting differences between diseased and non-diseased individuals. However, prospective studies are limited. METHODS: We examined the detectability, reliability, and distribution of metabolites measured in pre-diagnostic plasma samples in a pilot study of women enrolled in the Northern California site of the Breast Cancer Family Registry. The study included 45 cases diagnosed with breast cancer at least one year after the blood draw, and 45 controls. Controls were matched on age (within 5 years), family status, BRCA status, and menopausal status. Duplicate samples were included for reliability assessment. We used a liquid chromatography/gas chromatography mass spectrometer platform to measure metabolites. We calculated intraclass correlations (ICCs) among duplicate samples, and coefficients of variation (CVs) across metabolites. RESULTS: Of the 661 named metabolites detected, 338 (51%) were found in all samples, and 490 (74%) in more than 80% of samples. The median ICC between duplicates was 0.96 (25th - 75th percentile: 0.82-0.99). We observed a greater than 20% case-control difference in 24 metabolites (p < 0.05), although these associations were not significant after adjusting for multiple comparisons. CONCLUSIONS: These data show that assays are reproducible for many metabolites, there is a minimal laboratory variation for the same sample, and a large between-person variation. Despite small sample size, differences between cases and controls in some metabolites suggest that a well-powered large-scale study is likely to detect biological meaningful differences to provide a better understanding of breast cancer etiology.

Weathering the storm: A case of thyroid storm refractory to conventional treatment benefiting from therapeutic plasma exchange.

Thyroid storm is a severe manifestation of thyrotoxicosis characterized by systemic organ dysfunction secondary to a hypermetabolic state. Although antithyroid drugs, steroids, beta-blockers, antipyretics, and cholestyramine are the standard of care, some patients inadequately respond to these conventional therapies. Therapeutic plasma exchange has been previously utilized as a treatment modality in patients with a poor response to routine therapies or with contraindications to them. Herein, we report our experience with the management of a case of thyroid storm refractory to conventional treatment but responsive to therapeutic plasma exchange.