Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts.

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.

Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies.

BACKGROUND: A healthy lifestyle is associated with a lower premature mortality risk and with longer life expectancy. However, the metabolic pathways of a healthy lifestyle and how they relate to mortality and longevity are unclear. We aimed to identify and replicate a healthy lifestyle metabolomic signature and examine how it is related to total and cause-specific mortality risk and longevity. METHODS: In four large cohorts with 13,056 individuals and 28-year follow-up, we assessed five healthy lifestyle factors, used liquid chromatography mass spectrometry to profile plasma metabolites, and ascertained deaths with death certificates. The unique healthy lifestyle metabolomic signature was identified using an elastic regression. Multivariable Cox regressions were used to assess associations of the signature with mortality and longevity. FINDINGS: The identified healthy lifestyle metabolomic signature was reflective of lipid metabolism pathways. Shorter and more saturated triacylglycerol and diacylglycerol metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were positively associated. Participants with a higher healthy lifestyle metabolomic signature had a 17% lower risk of all-cause mortality, 19% for cardiovascular disease mortality, and 17% for cancer mortality and were 25% more likely to reach longevity. The healthy lifestyle metabolomic signature explained 38% of the association between the self-reported healthy lifestyle score and total mortality risk and 49% of the association with longevity. CONCLUSIONS: This study identifies a metabolomic signature that measures adherence to a healthy lifestyle and shows prediction of total and cause-specific mortality and longevity. FUNDING: This work was funded by the NIH, CIHR, AHA, Novo Nordisk Foundation, and SciLifeLab.

A Plasma Metabolite Score Related to Psychological Distress and Diabetes Risk: A Nested Case-control Study in US Women.

CONTEXT: Psychological distress has been linked to diabetes risk. Few population-based, epidemiologic studies have investigated the potential molecular mechanisms (e.g., metabolic dysregulation) underlying this association. OBJECTIVE: To evaluate the association between a metabolomic signature for psychological distress and diabetes risk. METHODS: We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses' Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 matched controls. Blood samples were collected between 1989-1990 and incident diabetes was diagnosed between 1992-2008. Based on our prior work, we calculated a weighted plasma metabolite-based distress score (MDS) comprised of 19 metabolites. We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% CI for diabetes risk according to MDS. RESULTS: After adjusting for sociodemographic factors, family history of diabetes, and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the MDS was 1.00 (reference) for Q1, 1.16 (0.77, 1.73) for Q2, 1.30 (0.88, 1.91) for Q3, 1.99 (1.36, 2.92) for Q4, and 2.47 (1.66, 3.67) for Q5. Each SD increase in MDS was associated with 36% higher diabetes risk (95% CI: 1.21, 1.54; p-trend<0.0001). This association was moderately attenuated after additional adjustment for BMI (comparable OR: 1.17; 95% CI: 1.02, 1.35; p-trend=0.02). The MDS explained 17.6% of the association between self-reported psychological distress (defined as presence of depression or anxiety symptoms) and diabetes risk (p=0.04). CONCLUSIONS: MDS was significantly associated with diabetes risk in women. These results suggest that differences in multiple lipid and amino acid metabolites may underlie the observed association between psychological distress and diabetes risk.

Metabolomic profiles of chronic distress are associated with cardiovascular disease risk and inflammation-related risk factors.

BACKGROUND: Chronic psychological distress is associated with increased risk of cardiovascular disease (CVD) and investigators have posited inflammatory factors may be centrally involved in these relationships. However, mechanistic evidence and molecular underpinnings of these processes remain unclear, and data are particularly sparse among women. This study examined if a metabolite profile linked with distress was associated with increased CVD risk and inflammation-related risk factors. METHODS: A plasma metabolite-based distress score (MDS) of twenty chronic psychological distress-related metabolites was developed in cross-sectional, 1:1 matched case-control data comprised of 558 women from the Nurses' Health Study (NHS; 279 women with distress, 279 controls). This MDS was then evaluated in two other cohorts: the Women's Health Initiative Observational Cohort (WHI-OS) and the Prevención con Dieta Mediterránea (PREDIMED) trial. We tested the MDS's association with risk of future CVD in each sample and with levels of C-reactive protein (CRP) in the WHI-OS. The WHI-OS subsample included 944 postmenopausal women (472 CHD cases; mean time to event = 5.8 years); the PREDIMED subsample included 980 men and women (224 CVD cases, mean time to event = 3.1 years). RESULTS: In the WHI-OS, a 1-SD increase in the plasma MDS was associated with a 20% increased incident CHD risk (odds ratio [OR] = 1.20, 95% CI: 1.04 - 1.38), adjusting for known CVD risk factors excluding total and HDL cholesterol. This association was attenuated after including total and HDL cholesterol. CRP mediated an average 12.9% (95% CI: 4.9% - 28%, p < 10-15) of the total effect of MDS on CHD risk when adjusting for matching factors. This effect was attenuated after adjusting for known CVD risk factors. Of the metabolites in the MDS, tryptophan and threonine were inversely associated with incident CHD risk in univariate models. In PREDIMED, each one SD increase in the MDS was associated with an OR of 1.19 (95% CI: 1.00 - 1.41) for incident CVD risk, after adjusting all risk factors. Similar associations were observed in men and women. Four metabolites in the MDS were associated with incident CVD risk in PREDIMED in univariate models. Biliverdin and C36:5 phosphatidylcholine (PC) plasmalogen had inverse associations; C16:0 ceramide and C18:0 lysophosphatidylethanolamine(LPE) each had positive associations with CVD risk. CONCLUSIONS: Our study points to molecular alterations that may underlie the association between chronic distress and subsequent risk of cardiovascular disease in adults.

Metabolomic Evaluation of Air Pollution-related Bone Damage and Potential Mediation.

Ambient air pollution has been associated with bone damage. However, no studies have evaluated the metabolomic response to air pollutants and its potential influence on bone health in postmenopausal women. We analyzed data from WHI participants with plasma samples. Whole-body, total hip, femoral neck, and spine BMD at enrollment and follow-up (Y1, Y3, Y6). Daily particulate matter NO, NO2, PM10 and SO2 were averaged over 1-, 3-, and 5-year periods before metabolomic assessments. Statistical analyses included multivariable-adjusted linear mixed models, pathways analyses, and mediation modeling. NO, NO2, and SO2, but not PM10, were associated with taurine, inosine, and C38:4 phosphatidylethanolamine (PE), at all averaging periods. We found a partial mediation of C38:4 PE in the association between 1-year average NO and lumbar spine BMD (p-value: 0.032). This is the first study suggesting that a PE may partially mediate air pollution-related bone damage in postmenopausal women.

SpiderLearner: An ensemble approach to Gaussian graphical model estimation.

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood-based loss function. K $$ K $$ -fold cross-validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out-of-sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open-source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM.

Hyperinsulinemic and Pro-Inflammatory Dietary Patterns and Metabolomic Profiles Are Associated with Increased Risk of Total and Site-Specific Cancers among Postmenopausal Women.

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: HRQ5vsQ1(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(Q5vsQ1) 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.

Association of Plasma Metabolomic Biomarkers With Persistent Tinnitus: A Population-Based Case-Control Study.

Importance: Persistent tinnitus is common, disabling, and difficult to treat. Objective: To evaluate the association between circulating metabolites and persistent tinnitus. Design, Setting, and Participants: This was a population-based case-control study of 6477 women who were participants in the Nurses' Health Study (NHS) and NHS II with metabolomic profiles and tinnitus data. Information on tinnitus onset and frequency was collected on biennial questionnaires (2009-2017). For cases, metabolomic profiles were measured (2015-2021) in blood samples collected after the date of the participant's first report of persistent tinnitus (NHS, 1989-1999 and 2010-2012; NHS II, 1996-1999). Data analyses were performed from January 24, 2022, to January 14, 2023. Exposures: In total, 466 plasma metabolites from 488 cases of persistent tinnitus and 5989 controls were profiled using 3 complementary liquid chromatography tandem mass spectrometry approaches. Main Outcomes and Measures: Logistic regression was used to estimate odds ratios (ORs) of persistent tinnitus (per 1 SD increase in metabolite values) and 95% CIs for each individual metabolite. Metabolite set enrichment analysis was used to identify metabolite classes enriched for associations with tinnitus. Results: Of the 6477 study participants (mean [SD] age, 52 [9] years; 6477 [100%] female; 6121 [95%] White individuals) who were registered nurses, 488 reported experiencing daily persistent (≥5 minutes) tinnitus. Compared with participants with no tinnitus (5989 controls), those with persistent tinnitus were slightly older (53.0 vs 51.8 years) and more likely to be postmenopausal, using oral postmenopausal hormone therapy, and have type 2 diabetes, hypertension, and/or hearing loss at baseline. Compared with controls, homocitrulline (OR, 1.32; (95% CI, 1.16-1.50); C38:6 phosphatidylethanolamine (PE; OR, 1.24; 95% CIs, 1.12-1.38), C52:6 triglyceride (TAG; OR, 1.22; 95% CIs, 1.10-1.36), C36:4 PE (OR, 1.22; 95% CIs, 1.10-1.35), C40:6 PE (OR, 1.22; 95% CIs, 1.09-1.35), and C56:7 TAG (OR, 1.21; 95% CIs, 1.09-1.34) were positively associated, whereas α-keto-ß-methylvalerate (OR, 0.68; 95% CIs, 0.56-0.82) and levulinate (OR, 0.60; 95% CIs, 0.46-0.79) were inversely associated with persistent tinnitus. Among metabolite classes, TAGs (normalized enrichment score [NES], 2.68), PEs (NES, 2.48), and diglycerides (NES, 1.65) were positively associated, whereas phosphatidylcholine plasmalogens (NES, -1.91), lysophosphatidylcholines (NES, -2.23), and cholesteryl esters (NES,-2.31) were inversely associated with persistent tinnitus. Conclusions and Relevance: This population-based case-control study of metabolomic profiles and tinnitus identified novel plasma metabolites and metabolite classes that were significantly associated with persistent tinnitus, suggesting that metabolomic studies may help improve understanding of tinnitus pathophysiology and identify therapeutic targets for this challenging disorder.

Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress.

Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.

New Moms Wellness Study: the randomized controlled trial study protocol for an intervention study to increase fruit and vegetable intake and lower breast cancer risk through weekly counseling and supplemental fruit and vegetable box delivery in breastfeeding women.

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 .

Differences in Metabolomic Profiles Between Black and White Women and Risk of Coronary Heart Disease: an Observational Study of Women From Four US Cohorts.

BACKGROUND: Racial differences in metabolomic profiles may reflect underlying differences in social determinants of health by self-reported race and may be related to racial disparities in coronary heart disease (CHD) among women in the United States. However, the magnitude of differences in metabolomic profiles between Black and White women in the United States has not been well-described. It also remains unknown whether such differences are related to differences in CHD risk. METHODS: Plasma metabolomic profiles were analyzed using liquid chromatography-tandem mass spectrometry in the WHI-OS (Women's Health Initiative-Observational Study; 138 Black and 696 White women), WHI-HT trials (WHI-Hormone Therapy; 156 Black and 1138 White women), MESA (Multi-Ethnic Study of Atherosclerosis; 114 Black and 219 White women), JHS (Jackson Heart Study; 1465 Black women with 107 incident CHD cases), and NHS (Nurses' Health Study; 2506 White women with 136 incident CHD cases). First, linear regression models were used to estimate associations between self-reported race and 472 metabolites in WHI-OS (discovery); findings were replicated in WHI-HT and validated in MESA. Second, we used elastic net regression to construct a racial difference metabolomic pattern (RDMP) representing differences in the metabolomic patterns between Black and White women in the WHI-OS; the RDMP was validated in the WHI-HT and MESA. Third, using conditional logistic regressions in the WHI (717 CHD cases and 719 matched controls), we examined associations of metabolites with large differences in levels by race and the RDMP with risk of CHD, and the results were replicated in Black women from the JHS and White women from the NHS. RESULTS: Of the 472 tested metabolites, levels of 259 (54.9%) metabolites, mostly lipid metabolites and amino acids, significantly differed between Black and White women in both WHI-OS and WHI-HT after adjusting for baseline characteristics, socioeconomic status, lifestyle factors, baseline health conditions, and medication use (false discovery rate <0.05); similar trends were observed in MESA. The RDMP, composed of 152 metabolites, was identified in the WHI-OS and showed significantly different distributions between Black and White women in the WHI-HT and MESA. Higher RDMP quartiles were associated with an increased risk of incident CHD (odds ratio=1.51 [0.97-2.37] for the highest quartile comparing to the lowest; Ptrend=0.02), independent of self-reported race and known CHD risk factors. In race-stratified analyses, the RDMP-CHD associations were more pronounced in White women. Similar patterns were observed in Black women from the JHS and White women from the NHS. CONCLUSIONS: Metabolomic profiles significantly and substantially differ between Black and White women and may be associated with CHD risk and racial disparities in US women.

Gaussian graphical models with applications to omics analyses.

Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at https://github.com/katehoffshutta/ggmTutorial.

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal-offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant-metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal-offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal-offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses' Health Studies.

BACKGROUND: Adiposity is consistently positively associated with postmenopausal breast cancer and inversely associated with premenopausal breast cancer risk, though the reasons for this difference remain unclear. METHODS: In this nested case-control study of 1649 breast cancer cases and 1649 matched controls from the Nurses' Health Study (NHS) and the NHSII, we selected lipid and polar metabolites correlated with BMI, waist circumference, weight change since age 18, or derived fat mass, and developed a metabolomic score for each measure using LASSO regression. Logistic regression was used to investigate the association between this score and breast cancer risk, adjusted for risk factors and stratified by menopausal status at blood draw and diagnosis. RESULTS: Metabolite scores developed among only premenopausal or postmenopausal women were highly correlated with scores developed in all women (r = 0.93-0.96). Higher metabolomic adiposity scores were generally inversely related to breast cancer risk among premenopausal women. Among postmenopausal women, significant positive trends with risk were observed (e.g., metabolomic waist circumference score OR Q4 vs. Q1 = 1.47, 95% CI = 1.03-2.08, P-trend = 0.01). CONCLUSIONS: Though the same metabolites represented adiposity in pre- and postmenopausal women, breast cancer risk associations differed suggesting that metabolic dysregulation may have a differential association with pre- vs. postmenopausal breast cancer.

Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women.

OBJECTIVE: Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life. METHODS: We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate. RESULTS: After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors. CONCLUSIONS: Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.

Path-level interpretation of Gaussian graphical models using the pair-path subscore.

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.

Metabolomic Profiles Associated With Incident Ischemic Stroke.

BACKGROUND AND OBJECTIVES: Women have higher lifetime risk of stroke than men, and metabolic factors seem more strongly associated with stroke for women than men. However, few studies in either men or women have evaluated metabolomic profiles and incident stroke. METHODS: We applied liquid chromatography-tandem mass spectrometry to measure 519 plasma metabolites in a discovery set of women in the Nurses' Health Study (NHS; 454 incident ischemic stroke cases, 454 controls) with validation in 2 independent, prospective cohorts: Prevención con Dieta Mediterránea (PREDIMED; 118 stroke cases, 791 controls) and Nurses' Health Study 2 (NHS2; 49 ischemic stroke cases, 49 controls). We applied logistic regression models with stroke as the outcome to adjust for multiple risk factors; the false discovery rate was controlled through the q value method. RESULTS: Twenty-three metabolites were significantly associated with incident stroke in NHS after adjustment for traditional risk factors (q < 0.05). Of these, 14 metabolites were available in PREDIMED and 3 were significantly associated with incident stroke: methionine sulfoxide, N6-acetyllysine, and sucrose (q < 0.05). In NHS2, one of the 23 metabolites (glucuronate) was significantly associated with incident stroke (q < 0.05). For all 4 metabolites, higher levels were associated with increased risk. These 4 metabolites were used to create a stroke metabolite score (SMS) in the NHS and tested in PREDIMED. Per unit of standard deviation of SMS, the odds ratio for incident stroke was 4.12 (95% confidence interval [CI] 2.26-7.51) in PREDIMED, after adjustment for risk factors. In PREDIMED, the area under the receiver operating characteristic curve (AUC) for the model including SMS and traditional risk factors was 0.70 (95% CI 0.75-0.79) vs the AUC for the model including the traditional risk factors only of 0.65 (95% CI 0.70-0.75), corresponding to a 5% improvement in risk prediction with SMS (p < 0.005). DISCUSSION: Metabolites associated with stroke included 2 amino acids, a carboxylic acid, and sucrose. A composite SMS including these metabolites was associated with ischemic stroke and showed improvement in risk prediction beyond traditional risk factors. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a SMS accurately predicts incident ischemic stroke risk.

Metabolomic Analysis of Coronary Heart Disease in an African American Cohort From the Jackson Heart Study.

Importance: African American individuals have disproportionate rates of coronary heart disease (CHD) but lower levels of coronary artery calcium (CAC), a marker of subclinical CHD, than non-Hispanic White individuals. African American individuals may have distinct metabolite profiles associated with incident CHD risk compared with non-Hispanic White individuals, and examination of these differences could highlight important processes that differ between them. Objectives: To identify novel biomarkers of incident CHD and CAC among African American individuals and to replicate incident CHD findings in a multiethnic cohort. Design, Setting, and Participants: This analysis targeted plasma metabolomic profiling of 2346 participants in the Jackson Heart Study (JHS), a prospective population-based cohort study that included 5306 African American participants who were examined at baseline (2000-2004) and 2 follow-up visits. Replication of CHD-associated metabolites was sought among 1588 multiethnic participants from the Women's Health Initiative (WHI), a prospective population-based multiethnic cohort study of 161 808 postmenopausal women who were examined at baseline (1991-1995) and ongoing follow-up visits. Regression analyses were performed for each metabolite to examine the associations with incident CHD and CAC scores. Data were collected from the WHI between 1994 and 2009 and from the JHS between 2000 and 2015. All data were analyzed from November 2020 to August 2021. Exposures: Plasma metabolites. Main Outcomes and Measures: Incident CHD was defined as definite or probable myocardial infarction or definite fatal CHD in both the JHS and WHI cohorts. In the JHS cohort, silent myocardial infarction between examinations (as determined by electrocardiography) and coronary revascularization were included in the incident CHD analysis. Coronary artery calcium was measured using a 16-channel computed tomographic system and reported as an Agatston score. Results: Among 2346 African American individuals in the JHS cohort, the mean (SD) age was 56 (13) years, and 1468 individuals (62.6%) were female. Among 1588 postmenopausal women in the WHI cohort, the mean (SD) age was 67 (7) years; 217 individuals (13.7%) self-identified as African American, 1219 (76.8%) as non-Hispanic White, and 152 (9.6%) as other races or ethnicities. In the fully adjusted model including 1876 individuals, 46 of 303 targeted metabolites were associated with incident CHD (false discovery rate q <0.100). Data for 32 of the 46 metabolites were available in the WHI cohort, and 13 incident CHD-associated metabolites from the JHS cohort were replicated in the WHI cohort. A total of 1439 participants from the JHS cohort with available CAC scores received metabolomic profiling. Nine metabolites were associated with CAC scores. Minimal overlap was found between the results from the incident CHD and CAC analyses, with only 3 metabolites shared between the 2 analyses. Conclusions and Relevance: This cohort study identified metabolites that were associated with incident CHD among African American individuals, including 13 incident CHD-associated metabolites that were replicated in a multiethnic population and 9 novel metabolites that included N-acylamides, leucine, and lipid species. These findings may help to elucidate common and distinct metabolic processes that may be associated with CHD among individuals with different self-identified race.

Plasma metabolomic profiles associated with chronic distress in women.

Several forms of chronic distress including anxiety and depression are associated with adverse cardiometabolic outcomes. Metabolic alterations may underlie these associations. Whether these forms of distress are associated with metabolic alterations even after accounting for comorbid conditions and other factors remains unclear. Using an agnostic approach, this study examines a broad range of metabolites in relation to chronic distress among women. For this cross-sectional study of chronic distress and 577 plasma metabolites, data are from different substudies within the Women's Health Initiative (WHI) and Nurses' Health Studies (NHSI, NHSII). Chronic distress was characterized by depressive symptoms and other depression indicators in the WHI and NHSII substudies, and by combined indicators of anxiety and depressive symptoms in the NHSI substudy. We used a two-phase discovery-validation framework, with WHI (N = 1317) and NHSII (N = 218) substudies in the discovery phase (identifying metabolites associated with distress) and NHSI (N = 558) substudy in the validation phase. A differential network analysis provided a systems-level assessment of metabolomic alterations under chronic distress. Analyses adjusted for potential confounders and mediators (demographics, comorbidities, medications, lifestyle factors). In the discovery phase, 46 metabolites were significantly associated with depression measures. In validation, six of these metabolites demonstrated significant associations with chronic distress after adjustment for potential confounders. Among women with high distress, we found lower gamma-aminobutyric acid (GABA), threonine, biliverdin, and serotonin and higher C16:0 ceramide and 3-methylxanthine. Our findings suggest chronic distress is associated with metabolomic alterations and provide specific targets for future study of biological pathways in chronic diseases.