BACKGROUND:Although metabolomic profiling offers promise for the prediction of coronary heart disease (CHD), and metabolic risk factors are more strongly associated with CHD in women than men, ...limited data are available for women.
METHODS:We applied a liquid chromatography–tandem mass spectrometry metabolomics platform to measure 371 metabolites in a discovery set of postmenopausal women (472 incident CHD cases, 472 controls) with validation in an independent set of postmenopausal women (312 incident CHD cases, 315 controls).
RESULTS:Eight metabolites, primarily oxidized lipids, were significantly dysregulated in cases after the adjustment for matching and CHD risk factors in both the discovery and validation data sets. One oxidized phospholipid, C34:2 hydroxy-phosphatidylcholine, remained associated with CHD after further adjustment for other validated metabolites. Subjects with C34:2 hydroxy-phosphatidylcholine levels in the highest quartile had a 4.7-fold increase in CHD odds in comparison with the lowest quartile; C34:2 hydroxy-phosphatidylcholine also significantly improved the area under the curve (P<0.01) for CHD. The C34:2 hydroxy-phosphatidylcholine findings were replicated in a third replication data set of 980 men and women (230 cardiovascular events) with a stronger association observed in women.
CONCLUSIONS:These data replicate known metabolite predictors, identify novel markers, and support the relationship between lipid oxidation and subsequent CHD.
Recent animal and small clinical studies have suggested depression is related to altered lipid and amino acid profiles. However, this has not been examined in a population-based sample, particularly ...in women. We identified multiple metabolites associated with depression as potential candidates from prior studies. Cross-sectional data from three independent samples of postmenopausal women were analyzed, including women from the Women's Health Initiative-Observational Study (WHI-OS, n = 926), the WHI-Hormone Trials (WHI-HT; n = 1,325), and the Nurses' Health Study II Mind-Body Study (NHSII-MBS; n = 218). Positive depression status was defined as having any of the following: elevated depressive symptoms, antidepressant use, or depression history. Plasma metabolites were measured using liquid chromatography-tandem mass spectrometry (21 phosphatidylcholines (PCs), 7 lysophosphatidylethanolamines, 5 ceramides, 3 branched chain amino acids, and 9 neurotransmitters). Associations between depression status and metabolites were evaluated using multivariable linear regression; results were pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery rate (FDR). Prevalence rates of positive depression status were 24.4% (WHI-OS), 25.7% (WHI-HT), and 44.7% (NHSII-MBS). After multivariable adjustment, positive depression status was associated with higher levels of glutamate and PC 36 : 1/38 : 3, and lower levels of tryptophan and GABA-to-glutamate and GABA-to-glutamine ratio (FDR-p < 0.05). Positive associations with LPE 18 : 0/18 : 1 and inverse associations with valine and serotonin were also observed, although these associations did not survive FDR adjustment. Associations of positive depression status with several candidate metabolites including PC 36 : 1/38 : 3 and amino acids involved in neurotransmission suggest potential depression-related metabolic alterations in postmenopausal women, with possible implications for later chronic disease.
BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of ...conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Laboratory studies indicate that chemicals in fruits and vegetables have anti-carcinogenic and anti-inflammatory activities that can lower breast cancer risk. However, ...epidemiologic studies of the association between fruit and vegetable intake and breast cancer risk have produced mixed results. Measurement error, confounding, and an emphasis on diet in later adulthood may contribute to weak associations. This paper describes a randomized controlled diet intervention trial in breastfeeding women to examine the effect of high fruit and vegetable intake on breast cancer risk factors, including weight, DNA methylation and inflammatory markers.
Methods
Eligible breastfeeding women who reside within a 35-mile radius of Amherst, MA are enrolled at five to six weeks postpartum and randomly assigned to a Fruit and Vegetable Intervention Arm (target n = 200) or to a USDA MyPlate Control Arm (target n = 200). The Fruit and Vegetable Intervention group receives weekly telephone or video-based counseling to encourage intake of at least eight to ten daily servings of fruits and vegetables and a weekly delivery of a supplemental box of fruits and vegetables for 20 weeks, and less intensive counseling for up to one year. Breastmilk and infant fecal specimens are collected at baseline, 10 and 20 weeks. Anthropometric measurements are obtained at these timepoints and at the 1-year follow-up. The primary outcomes are change in DNA methylation in breast epithelial cells and change in inflammatory markers in breastmilk from randomization to 20 weeks; and change in weight, waist circumference, and fruit and vegetable intake for the period from randomization to 20 weeks and 1 year.
Discussion
This 1-year randomized diet intervention trial in breastfeeding women will assess whether intake of at least eight to ten daily servings of fruits and vegetables per day improves biomarkers of breast cancer risk directly in the breast (i.e., DNA methylation and inflammatory markers) and helps women maintain a healthy weight.
Trial registration
: ClinicalTrials.gov Identifier: NCT04374747. Registered May 5, 2020.
https://www.clinicaltrials.gov/ct2/show/NCT04374747
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data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this ...paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques.
the analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper.
no single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-dimensionality data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related ...metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: OR
(95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: OR
(95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.
The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to ...error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.
We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.
Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.
Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We evaluated associations of the Empirical Dietary Index for Hyperinsulinemia (EDIH), Empirical Dietary Inflammatory Pattern (EDIP) and Healthy Eating Index (HEI2015) and their metabolomics profiles ...with the risk of total and site-specific cancers. We used baseline food frequency questionnaires to calculate dietary scores among 112,468 postmenopausal women in the Women's Health Initiative. We used multivariable-adjusted Cox regression to estimate hazard ratios (HR) and 95% confidence intervals for cancer risk estimation. Metabolomic profile scores were derived using elastic-net regression with leave-one-out cross validation. In over 17.8 years, 18,768 incident invasive cancers were adjudicated. Higher EDIH and EDIP scores were associated with greater total cancer risk, and higher HEI-2015 with lower risk: HR
(95% CI): EDIH, 1.10 (1.04-1.15); EDIP, 1.08 (1.02-1.15); HEI-2015, 0.93 (0.89-0.98). The multivariable-adjusted incidence rate difference(Q5
Q1) for total cancer was: +52 (EDIH), +41 (EDIP) and -49 (HEI-2015) per 100,000 person years. All three indices were associated with colorectal cancer, and EDIH and EDIP with endometrial and breast cancer risk. EDIH was further associated with luminal-B, ER-negative and triple negative breast cancer subtypes. Dietary patterns contributing to hyperinsulinemia and inflammation were associated with greater cancer risk, and higher overall dietary quality, with lower risk. The findings warrant the testing of these dietary patterns in clinical trials for cancer prevention among postmenopausal women.
Several studies have suggested that global DNA methylation in circulating white blood cells (WBC) is associated with breast cancer risk.
To address conflicting results and concerns that the findings ...for WBC DNA methylation in some prior studies may reflect disease effects, we evaluated the relationship between global levels of WBC DNA methylation in white blood cells and breast cancer risk in a case-control study nested within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) cohort. A total of 428 invasive breast cancer cases and 419 controls, frequency matched on age at entry (55-59, 60-64, 65-69, ≥70 years), year of entry (on/before September 30, 1997, on/after October 1, 1997) and period of DNA extraction (previously extracted, newly extracted) were included. The ratio of 5-methyl-2' deoxycytidine 5-mdC to 2'-deoxyguanine dG, assuming dG = 5-mdC + 2'-deoxycytidine dC (%5-mdC), was determined by liquid chromatography-electrospray ionization-tandem mass spectrometry, an especially accurate method for assessing total genomic DNA methylation.
Odds ratio (OR) estimates and 95% confidence intervals (CI) for breast cancer risk adjusted for age at entry, year of entry, and period of DNA extraction, were 1.0 (referent), 0.89 (95% CI, 0.6-1.3), 0.88 (95% CI, 0.6-1.3), and 0.84 (95% CI, 0.6-1.2) for women in the highest compared to lowest quartile levels of %5md-C (p for trend = .39). Effects did not meaningfully vary by time elapsed from WBC collection to diagnosis.
These results do not support the hypothesis that global DNA hypomethylation in WBC DNA is associated with increased breast cancer risk prior to the appearance of clinical disease.
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