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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.

SMCHD1 mutations cause congenital arhinia (absent nose) and a muscular dystrophy called FSHD2. In FSHD2, loss of SMCHD1 repressive activity causes expression of double homeobox 4 (DUX4) in muscle tissue, where it is toxic. Studies of arhinia patients suggest a primary defect in nasal placode cells (human nose progenitors). Here, we show that upon SMCHD1 ablation, DUX4 becomes derepressed in H9 human embryonic stem cells (hESCs) as they differentiate toward a placode cell fate, triggering cell death. Arhinia and FSHD2 patient-derived induced pluripotent stem cells (iPSCs) express DUX4 when converted to placode cells and demonstrate variable degrees of cell death, suggesting an environmental disease modifier. HSV-1 may be one such modifier as herpesvirus infection amplifies DUX4 expression in SMCHD1 KO hESC and patient iPSC. These studies suggest that arhinia, like FSHD2, is due to compromised SMCHD1 repressive activity in a cell-specific context and provide evidence for an environmental modifier.

DMD pathogenic variants for Duchenne and Becker muscular dystrophy are detectable with high sensitivity by standard clinical exome analyses of genomic DNA. However, up to 7% of DMD mutations are deep intronic and analysis of muscle-derived RNA is an important diagnostic step for patients who have negative genomic testing but abnormal dystrophin expression in muscle. In this study, muscle biopsies were evaluated from 19 patients with clinical features of a dystrophinopathy, but negative clinical DMD mutation analysis. Reverse transcription-polymerase chain reactionor high-throughput RNA sequencingmethods identified 19 mutations with one of three pathogenic pseudoexon types: deep intronic point mutations, deletions or insertions, and translocations. In association with point mutations creating intronic splice acceptor sites, we observed the first examples of DMD pseudo 3'-terminal exon mutations causing high efficiency transcription termination within introns. This connection between splicing and premature transcription termination is reminiscent of U1 snRNP-mediating telescripting in sustaining RNA polymerase II elongation across large genes, such as DMD. We propose a novel classification of three distinct types of mutations identifiable by muscle RNA analysis, each of which differ in potential treatment approaches. Recognition and appropriate characterization may lead to therapies directed toward full-length dystrophin expression for some patients.

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/m(2)) 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/m(2)) and lean mass index (LMI; kg/m(2)) correlations were estimated similarly, with mutual adjustment to evaluate independent effects. RESULTS: Using a Bonferroni-corrected α level 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.

The molecular diagnosis of facioscapulohumeral muscular dystrophy (FSHD) relies on detecting contractions of the unique D4Z4 repeat array at the chromosome 4q35 locus in the presence of a permissive 4q35A haplotype. Long, intact DNA molecules are required for accurate sizing of D4Z4 repeats. We validated the use of optical genome mapping to determine size and haplotype of D4Z4 alleles for FSHD analysis. The cohort included 36 unique DNA specimens from fresh blood samples or archived agarose plugs. High-molecular- weight DNA underwent sequence-specific labeling followed by separation and image analysis with data collection on the Saphyr system. D4Z4 allele sizes were calculated and haplotypes determined from the labeling patterns. Each specimen had previous diagnostic testing using restriction enzyme digests with EcoRI, EcoRI/BlnI, XapI, or HindIII, followed by pulsed field gel electrophoresis and Southern blot analysis with appropriate probes. Optical genome mapping detected 4q35 and 10q26 alleles ranging from 1 to 79 D4Z4 repeats and showed strong correlation with Southern blot allele sizing (R(2) = 0.95) and haplotyping (133 of 134; 99.4% haplotype match). Analysis of inter-assay and intra-assay runs showed high reproducibility (0.03 to 0.94 %CV). Subsequent optical genome mapping for routine clinical testing from 315 clinical FSHD cases compared favorably with historical result trends. Optical genome mapping is an accurate and highly reproducible method for chromosomal abnormalities associated with FSHD.

Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case–control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate

The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

Higher amounts of physical activity are associated with increased longevity. However, whether different leisure time physical activity types are differentially associated with mortality risk is not established.To examine whether participation in equivalent amounts of physical activity (7.5 to15 metabolic equivalent of task [MET] hours per week) through different activity types is associated with mortality risk and to investigate the shape of the dose-response association.Participants in this cohort were respondents from the National Institutes of Health-AARP Diet and Health Study who completed the follow-up questionnaire between 2004 and 2005. This questionnaire collected data on weekly durations of different types of physical activities. Mortality was ascertained through December 31, 2019.MET hours per week spent participating in the following activities: running, cycling, swimming, other aerobic exercise, racquet sports, golf, and walking for exercise.All-cause, cardiovascular, and cancer mortality. Separate multivariable-adjusted Cox proportional hazards regression models were fitted to estimate hazard ratios (HRs) and 95% CIs of mortality for each of the 7 types of leisure time physical activities, as well as the sum of these activities.A total of 272 550 participants (157 415 men [58%]; mean [SD] age at baseline, 70.5 [5.4] years [range, 59-82 years]) provided information on types of leisure time activity, and 118 153 (43%) died during a mean (SD) follow-up of 12.4 (3.9) years. In comparison with those who did not participate in each activity, 7.5 to less than 15 MET hours per week of racquet sports (HR, 0.84; 95% CI, 0.75-0.93) and running (HR, 0.85; 95% CI, 0.78-0.92) were associated with the greatest relative risk reductions for all-cause mortality, followed by walking for exercise (HR, 0.91; 95% CI, 0.89-0.93), other aerobic activity (HR, 0.93; 95% CI, 0.90-0.95), golf (HR, 0.93; 95% CI, 0.90-0.97), swimming (HR, 0.95; 95% CI, 0.92-0.98), and cycling (HR, 0.97; 95% CI, 0.95-0.99). Each activity showed a curvilinear dose-response association with mortality risk; low MET hours per week of physical activity for any given activity type were associated with a large reduction in mortality risk, with diminishing returns for each increment in activity thereafter. Associations were similar for cardiovascular and cancer mortality.This cohort study of older individuals found differences between different types of leisure time activities and mortality risk, but there were significant associations between participating in 7.5 to less than 15 MET hours per week of any activity and mortality risk.

Abstract Duchenne muscular dystrophy (DMD) is a degenerative muscular disease affecting roughly one in 5000 males at birth. The disease is often caused by inherited X-linked recessive pathogenic variants in the dystrophin gene, but may also arise from de novo mutations. Disease-causing variants include nonsense, out of frame deletions or duplications that result in loss of dystrophin protein expression. There is currently no cure for DMD and the few treatment options available aim at slowing muscle degradation. New advances in gene therapy and understanding of dystrophin (DYS) expression in other muscular dystrophies have opened new opportunities for treatment. Therefore, reliable methods are needed to monitor dystrophin expression and assess the efficacy of new therapies for muscular dystrophies such as DMD and Becker muscular dystrophy (BMD). Here, we describe the validation of a novel Western blot (WB) method for the quantitation of mini-dystrophin protein in human skeletal muscle tissues that is easy to adopt in most laboratory settings. This WB method was assessed through precision, accuracy, selectivity, dilution linearity, stability, and repeatability. Based on mini-DYS standard performance, the assay has a dynamic range of 0.5–15 ng protein (per 5 µg total protein per lane), precision of 3.3 to 25.5%, and accuracy of − 7.5 to 3.3%. Our stability assessment showed that the protein is stable after 4 F/T cycles, up to 2 h at RT and after 7 months at − 70°C. Furthermore, our WB method was compared to the results from our recently published LC–MS method. Graphical Abstract Workflow for our quantitative WB method to determine mini-dystrophin levels in muscle tissues (created in Biorender.com). Step 1 involves protein extraction from skeletal muscle tissue lysates from control, DMD, or BMD biospecimen. Step 2 measures total protein concentrations. Step 3 involves running gel electrophoresis with wild-type dystrophin (wt-DYS) from muscle tissue extracts alongside mini-dystrophin STD curve and mini-DYS and protein normalization with housekeeping GAPDH.

BACKGROUND: Trimethylamine N-oxide (TMAO), a diet-derived, gut microbial-host cometabolite, has been linked to cardiometabolic diseases. However, the relations remain unclear between diet, TMAO, and cardiometabolic health in general populations from different regions and ethnicities. OBJECTIVES: To examine associations of circulating TMAO with dietary and cardiometabolic factors in a pooled analysis of 16 population-based studies from the United States, Europe, and Asia. METHODS: Included were 32,166 adults (16,269 white, 13,293 Asian, 1247 Hispanic/Latino, 1236 black, and 121 others) without cardiovascular disease, cancer, chronic kidney disease, or inflammatory bowel disease. Linear regression coefficients (β) were computed for standardized TMAO with harmonized variables. Study-specific results were combined by random-effects meta-analysis. A false discovery rate

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.

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.