The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes.

AIMS/HYPOTHESIS: Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. METHODS: We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. RESULTS: We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. CONCLUSIONS/INTERPRETATION: The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.

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.

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.

Effect of Dietary Choline Consumption on the Development of Urinary Urgency Incontinence in a Longitudinal Cohort of Women.

INTRODUCTION AND HYPOTHESIS: The objective of this study was to determine whether differences in the cumulative dietary intake of choline, is associated with the risk of developing urge urinary incontinence (UUI). METHODS: This was an analysis within the Nurses' Health Study (NHS) I and II. The main exposure was the cumulative daily intake for each choline-containing compound obtained from a detailed daily food frequency questionnaire. The primary outcome was UUI, defined as urine loss with a sudden feeling of bladder fullness or when a toilet is inaccessible, occurring >1/month. Cox proportional hazards regression models were used to calculate multivariate-adjusted relative risks and 95% confidence intervals (CIs) for the association between total choline and choline derivatives and risk of UUI. Fixed effects meta-analyses of results from NHSI and NHSII were performed for postmenopausal women only to obtain a pooled estimate of the impact of choline consumption on UUI. RESULTS: There were 33,273 participants in NHSI and 38,732 in NHSII who met all the criteria for inclusion in the analysis. The incidence of UUI was 9.41% (n=3,139) in NHSI and 4.25% (n=1,646) in NHSII. After adjusting for confounders choline was not found to be associated with UUI in postmenopausal women. However, in premenopausal women, relative to the lowest quartile, the highest quartile of consumption of total choline (aRR = 0.79, 95% CI: 0.64-0.99), free choline (aRR = 0.74, 95% CI: 0.58-0.94), and phosphocholine (aRR = 0.77, 95% CI: 0.61-0.96) were associated with a reduced risk of UUI. CONCLUSIONS: Increased dietary choline consumption was associated with a reduced risk of UUI among premenopausal women.

Benchmarking omics-based prediction of asthma development in children.

BACKGROUND: Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE: We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD: We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS: Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS: Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms.

Limited data exist on the performance of high-throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within-person variability over time. Thus, the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses' Health Studies and Health Professionals Follow-Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24-h or 48-h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1-year apart. When comparing samples processed immediately, 24-h, and 48-h later, 55% of assays had an ICC/r ≥ 0.75 and 87% had an ICC/r ≥ 0.40 in Olink compared to 44% with an ICC/r ≥ 0.75 and 72% with an ICC/r ≥ 0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r ≥ 0.40) in samples collected 1-year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r > 0.75) for 14.7% and poor (r < 0.40) for 44.8% of proteins. High-throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia.

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

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.

Plasma metabolomic profiles for colorectal cancer precursors in women.

How metabolome changes influence the early process of colorectal cancer (CRC) development remains unknown. We conducted a 1:2 matched nested case-control study to examine the associations of pre-diagnostic plasma metabolome (profiled using LC-MS) with risk of CRC precursors, including conventional adenomas (n = 586 vs. 1141) and serrated polyps (n = 509 vs. 993), in the Nurses' Health Study (NHS) and NHSII. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). We used the permutation-based Westfall and Young approach to account for multiple testing. Subgroup analyses were performed for advanced conventional adenomas (defined as at least one adenoma of ≥ 10 mm or with high-grade dysplasia, or tubulovillous or villous histology) and high-risk serrated polyps that were located in the proximal colon or with size of ≥ 10 mm. After multiple testing correction, among 207 metabolites, higher levels of C36:3 phosphatidylcholine (PC) plasmalogen were associated with lower risk of conventional adenomas, with the OR (95% CI) comparing the 90th to the 10th percentile of 0.62 (0.48-0.81); C54:8 triglyceride (TAG) was associated with higher risk of serrated polyps (OR = 1.79, 95% CI: 1.31-2.43), and phenylacetylglutamine (PAG) was associated with lower risk (OR = 0.57, 95% CI:0.43-0.77). PAG was also inversely associated with advanced adenomas (OR = 0.57, 95% CI: 0.36-0.89) and high-risk serrated polyps (OR = 0.54, 95% CI: 0.32-0.89), although the multiple testing-corrected p value was > 0.05. Our findings suggest potential roles of lipid metabolism and phenylacetylglutamine, a microbial metabolite, in the early stage of colorectal carcinogenesis, particularly for the serrated pathway.

Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data.

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

Deciphering lipid structures based on platform-independent decision rules.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

Low dose environmental radon exposure and breast tumor gene expression.

BACKGROUND: The International Agency for Research on Cancer classified radon and its decay-products as Group-1-human-carcinogens, and with the current knowledge they are linked specifically to lung cancer. Biokinetic models predict that radon could deliver a carcinogenic dose to breast tissue. Our previous work suggested that low-dose radon was associated with estrogen-receptor (ER)-negative breast cancer risk. However, there is limited research to examine the role of radon in breast cancer biology at the tissue level. We aim to understand molecular pathways linking radon exposure with breast cancer biology using transcriptome-wide-gene-expression from breast tumor and normal-adjacent tissues. METHODS: Our study included 943 women diagnosed with breast cancer from the Nurses' Health Study (NHS) and NHSII. We estimated cumulative radon concentration for each participant up-to the year of breast cancer diagnosis by linking residential addresses with a radon exposure model. Transcriptome-wide-gene-expression was measured with the Affymetrix-Glue-Human-Transcriptome-Array-3.0 and Human-Transcriptome-Array-2.0. We performed covariate-adjusted linear-regression for individual genes and further employed pathway-analysis. All analyses were conducted separately for tumor and normal-adjacent samples and by ER-status. RESULTS: No individual gene was associated with cumulative radon exposure in ER-positive tumor, ER-negative tumor, or ER-negative normal-adjacent tissues at FDR < 5%. In ER-positive normal-adjacent samples, PLCH2-reached transcriptome-wide-significance (FDR < 5%). Gene-set-enrichment-analyses identified 2-upregulated pathways (MAPK signaling and phosphocholine biosynthesis) enriched at FDR < 25% in ER-negative tumors and normal-adjacent tissues, and both pathways have been previously reported to play key roles in ionizing radiation induced tumorigenesis in experimental settings. CONCLUSION: Our findings provide insights into the molecular pathways of radon exposure that may influence breast cancer etiology.

Metabolome-Wide Association Study of the Relationship Between Habitual Physical Activity and Plasma Metabolite Levels.

We identified plasma metabolites associated with habitual physical activity among 5,197 US participants from the Nurses' Health Study (NHS), Nurses' Health Study II (NHS II), and the Health Professionals Follow-up Study (HPFS). Physical activity was assessed every 2-4 years via self-report questionnaires. Blood was collected in the NHS in 1989-1990, in NHS II during 1996-1999, and in the HPFS during 1993-1995. Metabolic profiling was conducted by liquid chromatography-mass spectrometry. Our study included 337 known metabolites, with 256 of them classified as lipids. We corrected for multiple testing by controlling the tail probability of the proportion of false positives (TPPFP) and accounted for correlated tests using bootstrapping. Physical activity was significantly associated with 20 metabolites after correction for multiple testing (TPPFP < 0.05), and positive associations were found for most of the metabolites, including 2 amino acids (citrulline and glycine), 4 cholesteryl esters (C18:2, C18:1, C16:0, C18:3), 8 phosphocholines (PCs) (C36:4 PC-A, C34:3 PC plasmalogen, C36:3 PC plasmalogen, C34:2 PC plasmalogen, C36:2 PC) and lysophosphatidylcholines (C18:2, C20:5, C18:1), and 3 phosphatidylethanolamines (PEs) (C38:3 PE plasmalogen) and lysophosphatidylethanolamines (C18:2, C18:1). We independently replicated the 20 metabolites among 2,305 women in the Women's Health Initiative using 1993 data, and half of the metabolites were replicated. Our study may help identify biomarkers of physical activity and provide insight into biological mechanisms underlying the beneficial effect of being physically active on cardiometabolic health.

Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists.

BACKGROUND: The application of metabolomics to epidemiologic studies is increasing. AIM OF REVIEW: Here, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research. KEY SCIENTIFIC CONCEPTS OF REVIEW: Many challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.

Identification of Plasma Lipid Metabolites Associated with Nut Consumption in US Men and Women.

BACKGROUND: Intake of nuts has been inversely associated with risk of type 2 diabetes and cardiovascular disease, partly through inducing a healthy lipid profile. How nut intake may affect lipid metabolites remains unclear. OBJECTIVE: The aim of this study was to identify the plasma lipid metabolites associated with habitual nut consumption in US men and women. METHODS: We analyzed cross-sectional data from 1099 participants in the Nurses' Health Study (NHS), NHS II, and Health Professionals Follow-up Study. Metabolic profiling was conducted on plasma by LC-mass spectrometry. Nut intake was estimated from food-frequency questionnaires. We included 144 known lipid metabolites that had CVs ≤25%. Multivariate linear regression was used to assess the associations of nut consumption with individual plasma lipid metabolites. RESULTS: We identified 17 lipid metabolites that were significantly associated with nut intake, based on a 1 serving (28 g)/d increment in multivariate models [false discovery rate (FDR) P value <0.05]. Among these species, 8 were positively associated with nut intake [C24:0 sphingomyelin (SM), C36:3 phosphatidylcholine (PC) plasmalogen-A, C36:2 PC plasmalogen, C24:0 ceramide, C36:1 PC plasmalogen, C22:0 SM, C34:1 PC plasmalogen, and C36:2 phosphatidylethanolamine plasmalogen], with changes in relative metabolite level (expressed in number of SDs on the log scale) ranging from 0.36 to 0.46 for 1 serving/d of nuts. The other 9 metabolites were inversely associated with nut intake with changes in relative metabolite level ranging from -0.34 to -0.44. In stratified analysis, 3 metabolites were positively associated with both peanuts and peanut butter (C24:0 SM, C24:0 ceramide, and C22:0 SM), whereas 6 metabolites were inversely associated with other nuts (FDR P value <0.05). CONCLUSIONS: A panel of lipid metabolites was associated with intake of nuts, which may provide insight into biological mechanisms underlying associations between nuts and cardiometabolic health. Metabolites that were positively associated with intake of nuts may be helpful in identifying potential biomarkers of nut intake.

Obesity-Related Metabolomic Profiles and Discrimination of Metabolically Unhealthy Obesity.

A particular subgroup of obese adults, considered as metabolically healthy obese (MHO), has a reduced risk of metabolic complications. However, the molecular basis contributing to this healthy phenotype remains unclear. The objective of this work was to identify obesity-related metabolite patterns differed between MHO and metabolically unhealthy obese (MUHO) groups and examine whether these patterns are associated with the development of cardiometabolic disorders in a sample of Iranian adult population aged 18-50 years. Valid metabolites were defined as metabolites that passed the quality control analysis of the study. In this case-control study, 104 valid metabolites of 107 MHO and 100 MUHO patients were separately compared to those of 78 normal-weight metabolically healthy (NWMH) adults. Multivariable linear regression was used to investigate all potential relations in the study. A targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry was employed to profile plasma metabolites. The study revealed that, after Bonferroni correction, branched-chain amino-acids, tyrosine, glutamic acid, diacyl-phosphatidylcholines C32:1 and C38:3 were directly and acyl-carnitine C18:2, acyl-lysophosphatidylcholines C18:1 and C18:2, and alkyl-lysophosphatidylcholines C18.0 were inversely associated with MHO phenotype. The same patterns were observed in MUHO patients except for the acyl-carnitine and lysophosphatidylcholine profiles where acyl-carnitine C3:0 and acyl-lysophosphatidylcholine C16:1 were higher and acyl-lysophosphatidylcholines C18:1, C18:2 were lower in this phenotype. Furthermore, proline, and diacyl-phosphatidylcholines C32:2 and C34:2 were directly and serine, asparagines, and acyl-alkyl-phosphatidylcholine C34:3 were negatively linked to MUHO group. Factors composed of amino acids were directly and those containing lysophosphatidylcholines were inversely related to cardiometabolic biomarkers in both phenotypes. Interestingly, the diacyl-phosphatidylcholines-containing factor was directly associated with cardiometabolic disorders in the MUHO group. A particular pattern of amino acids and choline-containing phospholipids may aid in the identification of metabolic health among obese patients.

Dimension reduction techniques for the integrative analysis of multi-omics data.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Molecular Profiling of Phagocytic Immune Cells in Anopheles gambiae Reveals Integral Roles for Hemocytes in Mosquito Innate Immunity.

The innate immune response is highly conserved across all eukaryotes and has been studied in great detail in several model organisms. Hemocytes, the primary immune cell population in mosquitoes, are important components of the mosquito innate immune response, yet critical aspects of their biology have remained uncharacterized. Using a novel method of enrichment, we isolated phagocytic granulocytes and quantified their proteomes by mass spectrometry. The data demonstrate that phagocytosis, blood-feeding, and Plasmodium falciparum infection promote dramatic shifts in the proteomic profiles of An. gambiae granulocyte populations. Of interest, large numbers of immune proteins were induced in response to blood feeding alone, suggesting that granulocytes have an integral role in priming the mosquito immune system for pathogen challenge. In addition, we identify several granulocyte proteins with putative roles as membrane receptors, cell signaling, or immune components that when silenced, have either positive or negative effects on malaria parasite survival. Integrating existing hemocyte transcriptional profiles, we also compare differences in hemocyte transcript and protein expression to provide new insight into hemocyte gene regulation and discuss the potential that post-transcriptional regulation may be an important component of hemocyte gene expression. These data represent a significant advancement in mosquito hemocyte biology, providing the first comprehensive proteomic profiling of mosquito phagocytic granulocytes during homeostasis blood-feeding, and pathogen challenge. Together, these findings extend current knowledge to further illustrate the importance of hemocytes in shaping mosquito innate immunity and their principal role in defining malaria parasite survival in the mosquito host.