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.

Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Metabolomic epidemiology offers insights into disease aetiology.

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

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.

An epidemiological introduction to human metabolomic investigations.

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

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.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.

The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.' In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).

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.

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.

A Metabolomics Analysis of Circulating Carotenoids and Breast Cancer Risk.

BACKGROUND: Higher circulating carotenoids are associated with lower breast cancer risk. The underlying biology remains under-explored. METHODS: We profiled 293 prediagnostic plasma metabolites in a nested case-control study (n = 887 cases) within the Nurses' Health Studies. Associations between circulating carotenoids and metabolites were identified using linear-mixed models (FDR ≤ 0.05), and we further selected metabolites most predictive of carotenoids with LASSO. Metabolic signatures for carotenoids were calculated as weighted sums of LASSO selected metabolites. We further evaluated the metabolic signatures in relation to breast cancer risk using conditional logistic-regression. RESULTS: We identified 48 to 110 metabolites associated with plasma levels of α-carotene, ß-carotene, ß-cryptoxanthin, estimated-vitamin-A-potential, lutein/zeaxanthin, and lycopene, which included primarily positively associated metabolites implicated in immune regulation (tryptophan), redox balance (plasmalogens, glutamine), epigenetic regulations (acetylated-/methylated-metabolites), and primarily inversely associated metabolites involved in ß-oxidation (carnitines; FDR ≤ 0.05). The metabolomic signatures derived for ß-carotene (Q4 vs. Q1 relative risk RR = 0.74, P trend = 0.02), and estimated-vitamin-A-potential (Q4 vs. Q1 RR = 0.74, P trend = 0.02)-measured ≥10 years before diagnosis-were associated with lower breast cancer risk. Modest attenuations of RR for measured levels of ß-carotene and estimated-vitamin-A-potential were seen when we adjusted for their corresponding metabolic signatures. CONCLUSIONS: Metabolites involved in immune regulation, redox balance, membrane signaling, and ß-oxidation were associated with plasma carotenoids. Although some metabolites may reflect shared common food sources or compartmental colocalization with carotenoids, others may signal the underlying pathways of carotenoids-associated lowered breast cancer risk. IMPACT: Consumption of carotenoid-rich diet is associated with a wide-range of metabolic changes which may help to reduce breast cancer risk.

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.

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.