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ObjectivesBoth 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.MethodsIn 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).ResultsMean 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)).ConclusionWeightlifting 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.

Supplementary Tables 1-3. Supplementary Table 1 - a) Correlations between selectb serum inflammation markers among lung cancer controls. b) Correlations between select and serum inflammation markers among NHL controls. c) Correlations between selectb serum inflammation markers among ovarian cancer controls. Supplementary Table 2. Odds ratios (ORs)a and 95% confidence intervals (CIs) for selectb circulating inflammation markers and body mass index. Supplementary Table 3. Odds ratios (ORs)a and 95% confidence intervals (CIs) for select circulating inflammation markers and body mass index (per 5-kg/m2) by case-control study

Tables 1-17, Figure S1, and acknowledgements.

PDF file - 16K, The correlation of the (BIV) measured from PLCO and SPA.

PDF file - 13K, The plot illustrates the distributions of the technical CV's (metabolite levels on a log scale), a measure of laboratory variability. The x-axis represents the metabolite quantile ranking (e.g. 0.5 represents the median), the y-axis represents the actual CV, and the curves (black for SPA and dashed red for PLCO) show the CV for the specified metabolite quantile ranking.

Supplementary Figure 1 from Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies

Supplementary Materials and Methods: Details on development of sampling weights and their use in analysis.

Hazard ratios stratified by potential effect modifiers

Background: The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there are little data describing the effect of fecal sample collection methods on metabolomic measures.Methods: We collected fecal samples from 18 volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated (i) technical reproducibility within sample replicates, (ii) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and (iii) concordance of metabolite measures with the putative “gold standard,” day 0 samples without solution.Results: Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC > 0.87) but not FIT (median ICC = 0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the “gold standard” were higher for 95% ethanol (median rs = 0.82) and FOBT (median rs = 0.70) than for FIT (median rs = 0.40).Conclusions: Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the “gold standard” is highest with 95% ethanol and acceptable with FOBT.Impact: Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics. Cancer Epidemiol Biomarkers Prev; 25(11); 1483–90. ©2016 AACR.

PDF file - 15K, The correlation between the ICCs measured for the metabolites in both PLCO and SPA.

PDF file - 94K, Metabolite Classification

PDF file - 65K

The association between leisure-time physical activity (LTPA) and male breast cancer risk is unclear. In the Male Breast Cancer Pooling Project, with 449 cases and 13,855 matched controls, we used logistic regression with study stratification to generate adjusted ORs and 95% confidence intervals (CI) for LTPA tertiles and male breast cancer risk. Compared with low LTPA, medium and high LTPA were not associated with male breast cancer risk (OR, 1.01; 95% CI, 0.79–1.29; 0.90, 0.69–1.18, respectively). In joint-effects analyses, compared with the referent of high body mass index (BMI; ≥25 kg/m2)/low LTPA, neither medium nor high PA was associated with risk among high BMI men, but normal BMI men (2) with low or medium LTPA were at a nonsignificant ∼16% reduced risk and those with high LTPA were at a 27% reduced risk (OR, 0.73; 95% CI, 0.50–1.07). Physical activity alone may not confer protection against male breast cancer risk. Cancer Epidemiol Biomarkers Prev; 24(12); 1898–901. ©2015 AACR.

Metabolites in fecal samples collected with no solution that were frozen immediately (day 0) by super and sub metabolic pathway

Background: Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies.Methods: Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study.Results: Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of “usual” levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability.Conclusion: Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies.Impact: We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology. Cancer Epidemiol Biomarkers Prev; 22(4); 631–40. ©2013 AACR.

Background: Epidemiologic studies examining circulating levels of inflammatory markers in relation to obesity and physical inactivity may aid in our understanding of the role of inflammation in obesity-related cancers. However, previous studies on this topic have focused on a limited set of markers.Methods: We evaluated associations between body mass index (BMI) and vigorous physical activity level, based on self-report, and serum levels of 78 inflammation-related markers. Markers were measured using a bead-based multiplex method among 1,703 men and women, ages 55–74 years, and with no prior history of cancer at blood draw, and selected for case–control studies nested within the Prostate, Lung, Ovarian, and Colorectal Cancer Screening Trial. Analyses were adjusted for age, sex, smoking, case–control study, physical activity, and BMI.Results: Twelve markers were positively associated with BMI after FDR correction. ORs and 95% confidence interval (CI) for highest versus lowest levels of CCL2/MCP-1, CXCL5/ENA-78, sTNFRII, CXCL10/IP-10, CXCL6/GCP2, CCL13/MCP-4, amylin, CRP, C-peptide, CCL19/MIP-3b, insulin, and leptin were: 1.50 (1.14–1.98), 1.52 (1.12–2.05), 1.61 (1.17–2.20), 1.69 (1.25–2.28), 1.74 (1.24–2.44), 1.75 (1.22–2.50), 1.91 (1.31–2.78), 2.41 (1.36–4.25), 2.78 (1.83–4.24), 3.30 (2.28–4.78), 4.05 (2.51–6.55), and 50.03 (19.87–125.99) per 5 kg/m2, respectively. Only CXCL12/SDF-1a was associated with physical activity (≥3 vs. Conclusions: BMI was associated with a wide range of circulating markers involved in the inflammatory response.Impact: This cross-sectional analysis identified serum markers could be considered in future studies aimed at understanding the underlying mechanisms linking inflammation with obesity and obesity-related cancers. Cancer Epidemiol Biomarkers Prev; 23(12); 2840–9. ©2014 AACR.

Supplementary Table 1A: The Pearson correlation coefficient of untransformed metabolite levels was estimated between each pair of metabolites within each of the four studies. Supplementary Table 1B: The Pearson correlation coefficient of untransformed metabolite levels was estimated between each of the metabolic pathway groups within each of the four studies. Supplementary Table 2A.: We evaluated the odds ratio (OR) of breast cancer in women with estrogen levels in the 2nd, 3rd, 4th, and 5th quintiles of each study, as compared to women in the lowest quintile, using analyses that did not adjust for any other estrogen variable. Supplementary Table 2B.: We evaluated the OR of breast cancer in women with analyte/path:Total ratios in the 2nd, 3rd, 4th, and 5th quintiles of each study, as compared to women in the lowest quintile, using analyses that adjusted for total estrogen level. Supplementary Table 3: Inter-decile odds ratios (ORs) and 95% confidence intervals from each study contributing to the meta-analysis of all studies for analyte:total ratios from models adjusted for total estrogen level. Supplementary Table 4: Inter-decile odds ratios (ORs) and 95% confidence intervals from each study contributing to the meta-analysis of all studies for analyte:total ratios from models adjusted for total estrogen level. Supplementary Table 5: Inter-decile odds ratios (ORs) and 95% confidence intervals (CIs) from random effects meta-analysis of all studies for estrogen levels, unadjusted for total estrogen levels, and inter-decile ORs and 95% CIs from random effects meta-analysis for analyte:total ratios from models adjusted for total estrogen level. Supplementary Table 6: Inter-decile odds ratios (ORs) for Path2:Path16 ratio within subgroups of individuals using models adjusted for total estrogen level. Supplementary Table 7: We classified women into 9 groups, defined by tertiles of total estrogen level and tertiles of Path-2:Path-16 ratio. Supplementary Table 8: We measured the association between the Path-2:Path-16 ratio (dependent variable) and each of the covariates listed in the first column (independent variable). Supplementary Figure 1: The Path-2:Path-16 ratio is plotted as a function of the log of the total estrogen level within each study. Supplementary Figure 2: Odds ratios and 95% confidence intervals comparing the risk of breast cancer in individuals with a higher analyte or pathway concentration (90th percentile) to individuals with a lower concentration (10th percentile). Supplementary Figure 3: Odds ratios and 95% confidence intervals comparing the risk of breast cancer in individuals with a higher analyte:total or pathway:total ratio (90th percentile) to individuals with a lower ratio (10th percentile).

Metabolites in fecal samples collected with FIT tubes that were frozen immediately (day 0) by super and sub metabolic pathway

Number of metabolites in fecal samples by collection method and time

Technical reproducibility, stability, and concordance for metabolites with {greater than or equal to}80% detectability in samples with no solution and in samples collected with ethanol, FOBT, or FIT

PDF file - 46K, A)The proportion of metabolites in each of 10 categories that have an ICC exceeding 0.2, 0.5, and 0.8 in SPA. N is the total number of metabolites within the specified category. B) The proportion of metabolites in each of 10 categories that have an ICC exceeding 0.2, 0.5, and 0.8 in PLCO. N is the total number of metabolites within the specified category.

Metabolites in fecal samples collected with FOBT cards that were frozen immediately (day 0) by super and sub metabolic pathway

The average age at menarche declined in European and U.S. populations during the 19th and 20th centuries. The timing of pubertal events may have broad implications for chronic disease risks in aging women. Here we tested for associations of recalled menarcheal age with risks of 19 cancers in 536,450 women [median age, 60 years (range, 31–39 years)] in nine prospective U.S. and European cohorts that enrolled participants from 1981 to 1998. Cox regression estimated multivariable-adjusted HRs and 95% confidence intervals (CI) for associations of the age at menarche with risk of each cancer in each cohort and random-effects meta-analysis was used to generate summary estimates for each cancer. Over a median 10 years of follow-up, 60,968 women were diagnosed with a first primary incident cancer. Inverse linear associations were observed for seven of 19 cancers studied. Each additional year in the age at menarche was associated with reduced risks of endometrial cancer (HR = 0.91; 95% CI, 0.89–0.94), liver cancer (HR = 0.92; 95% CI, 0.85–0.99), melanoma (HR = 0.95; 95% CI, 0.93–0.98), bladder cancer (HR = 0.96; 95% CI, 0.93–0.99), and cancers of the colon (HR = 0.97; 95% CI, 0.96–0.99), lung (HR = 0.98; 95% CI, 0.96–0.99), and breast (HR = 0.98; 95% CI, 0.93–0.99). All but one of these associations remained statistically significant following adjustment for baseline body mass index. Similarities in the observed associations between menarche and seven cancers suggest shared underlying causes rooted early in life. We propose as a testable hypothesis that early exposure to sex hormones increases mid-life cancer risks by altering functional capacities of stem cells with roles in systemic energy balance and tissue homeostasis.Significance:Age at menarche is associated with risk for seven cancers in middle-aged women, and understanding the shared underlying causal pathways across these cancers may suggest new avenues for cancer prevention.

Participant characteristics (N=18)

PDF file - 22K, Each metabolite level was normalized by three steps: a) log transforming b) subtracting the mean of the log(metabolite) level c) dividing by the standard deviation of the log(metabolite) level. This figure shows the histogram of all (number of subjects x number of metabolites) normalized levels. The absence of longer tails (e.g. little kurtosis) suggests that taking the log of metabolite levels produced relatively normally distributed measurements.

Endogenous estradiol and estrone are linked causally to increased risks of breast cancer. In this study, we evaluated multiple competing hypotheses for how metabolism of these parent estrogens may influence risk. Prediagnostic concentrations of estradiol, estrone, and 13 metabolites were measured in 1,298 postmenopausal cases of breast cancer and 1,524 matched controls in four separate patient cohorts. The median time between sample collection and diagnosis was 4.4 to 12.7 years across the cohorts. Estrogen analytes were measured in serum or urine by liquid chromatography-tandem mass spectrometry. Total estrogen levels (summing all 15 estrogens/estrogen metabolites) were associated strongly and positively with breast cancer risk. Normalizing total estrogen levels, we also found that a relative increase in levels of 2-hydroxylation pathway metabolites, or in the ratio of 2-hydroxylation:16-hydroxylation pathway metabolites, were associated inversely with breast cancer risk. These associations varied by total estrogen levels, with the largest risk reductions occurring in women in the highest tertile. With appropriate validation, these findings suggest opportunities for breast cancer prevention by modifying individual estrogen metabolism profiles through either lifestyle alterations or chemopreventive strategies. Cancer Res; 77(4); 918–25. ©2017 AACR.

PDF file - 59K, A list of the identified metabolites with the lowest values of between-subject variability, (e.g. the highest within-subject variability), among all metabolites with an ICC > 0.8. Rows include metabolite name, , the equivalent value from the age and gender adjusted (A.G.A) model, the equivalent from a female-only model, p-value for the metabolite's association with age, and p-value for the metabolite's association with gender.

Supplemental Table 1 has selected results by estrogen receptor status and Supplemental Table 2 shows the number of cases for results presented in Supplemental Table 1

Background: Obesity has been associated with substantially higher risk of inflammatory breast cancer (IBC) than other breast cancer. Here, we assess whether comorbidities of obesity, namely diabetes, abnormal glucose, dyslipidemia, and hypertension, are differentially related to risk of IBC and other breast cancers by tumor stage at diagnosis (localized/regional/distant/unstaged).Methods: We used linked SEER-Medicare data, with female breast cancer cases ages 66+ years identified by SEER registries (years 1992–2011). We divided first breast cancers into IBC (N = 2,306), locally advanced non-IBC (LABC; N = 10,347), and other (N = 197,276). We selected female controls (N = 200,000) from a stratified 5% random sample of Medicare recipients alive and breast cancer free. We assessed exposures until 12 months before diagnosis/selection using Medicare claims data. We estimated odds ratios (OR) and 99.9% confidence intervals (CI) using unconditional logistic regression.Results: Diabetes was associated with increased risk of distant IBC (98.5% of IBC cases; OR 1.44; 99.9% CI 1.21–1.71), distant (OR 1.24; 99.9% CI, 1.09–1.40) and regional (OR 1.29 (99.9% CI, 1.14–1.45) LABC, and distant (OR 1.23; 99.9% CI, 1.10–1.39) and unstaged (OR 1.32; 99.9% CI, 1.18–1.47) other breast cancers. Dyslipidemia was associated with reduced risk of IBC (OR 0.80; 95% CI, 0.67–0.94) and other breast cancers except localized disease. Results were similar by tumor estrogen receptor status. Abnormal glucose levels and hypertension had little association with risk of any tumor type.Conclusions: Associations with diabetes and dyslipidemia were similar for distant stage IBC and other advanced tumors.Impact: If confirmed, such findings could suggest avenues for prevention. Cancer Epidemiol Biomarkers Prev; 26(6); 862–8. ©2017 AACR.