Reproducibility of metabolomic profiles among men and women in 2 large cohort studies

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors

BACKGROUND

Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis.

METHODS

We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model.

RESULTS

In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant.

CONCLUSION

Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Untargeted metabolomics reveals the effects of pre-analytic storage on serum metabolite profiles from healthy cats

Untargeted metabolomics investigations have characterized metabolic disturbances associated with various diseases in domestic cats. However, the pre-analytic stability of serum metabolites in the species is unknown. Our objective was to compare serum metabolomes from healthy cats stored at -20°C for up to 12 months to samples stored at -80°C. Serum samples from 8 adult, healthy cats were stored at -20°C for 6 months, -20°C for 12 months, or -80°C for 12 months. Untargeted liquid chromatography-mass spectrometry was used to generate serum metabolite profiles containing relative abundances of 733 serum metabolites that were compared among storage conditions. Unsupervised analysis with principal component analysis and hierarchical clustering of Euclidian distances revealed separation of samples from individual cats regardless of storage condition. Linear mixed-effects models identified 75 metabolites that differed significantly among storage conditions. Intraclass correlation analysis (ICC) classified most serum metabolites as having excellent (ICC ≥ 0.9; 33%) or moderate (ICC 0.75-0.89; 33%) stability, whereas 13% had poor stability (ICC < 0.5). Biochemicals that varied significantly among storage conditions and classified with poor stability included glutathione metabolites, amino acids, gamma-glutamyl amino acids, and polyunsaturated fatty acids. The benzoate; glycine, serine and threonine; tryptophan; chemical (xenobiotics); acetylated peptide, and primary bile acid sub pathways were enriched among highly stable metabolites, whereas the monohydroxy fatty acid, polyunsaturated fatty, and monoacylglycerol sub-pathways were enriched among unstable metabolites. Our findings suggest that serum metabolome profiles are representative of the cat of origin, regardless of storage condition. However, changes in specific serum metabolites, especially glutathione, gamma-glutamyl amino acid, and fatty acid metabolites were consistent with increased sample oxidation during storage at -20°C compared with -80°C. By investigating the pre-analytic stability of serum metabolites, this investigation provides valuable insights that could aid other investigators in planning and interpreting studies of serum metabolomes in cats.

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 (eg, 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 and 1990 and incident diabetes was diagnosed between 1992 and 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% confidence intervals (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 <.0001). This association was moderately attenuated after additional adjustment for body mass index (comparable OR: 1.17; 95% CI: 1.02, 1.35; P-trend = .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 = .04).

CONCLUSION

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.

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.

Associations of birth weight, plasma metabolome in adulthood and risk of type 2 diabetes

AIMS

We aimed to examine the longitudinal associations of birth weight with plasma metabolites in adulthood, and further quantify the proportions of the links between birth weight and incident adult type 2 diabetes (T2D) that were mediated by plasma metabolites.

MATERIALS AND METHODS

A total of 62,033 participants with complete nuclear magnetic resonance metabolomics and birth weight data from the UK Biobank were included in this study. Linear regression was used to assess the associations between birth weight and metabolites. Cox regression was used to estimate hazard ratios for T2D associated with metabolites. We further performed mediation analyses to estimate the extent to which metabolites might mediate the association between birth weight and T2D risk.

RESULTS

Low birth weight was associated with the adverse metabolic responses across multiple metabolic pathways, including lipoprotein subclasses, amino acids, fatty acids (FA), and inflammation. Metabolites associated with higher birth weight tended to be associated with a lower risk of T2D (Pearson correlation coefficient: -0.85). A total of 62 metabolites showed statistically significant mediation effects in the protective association of higher birth weight and T2D risk, including large-sized very low-density lipoprotein particles and triglyceride concentrations as well as saturated, and monounsaturated FA and glycoprotein acetyls.

CONCLUSIONS

We identified a range of metabolites that reflect the adult metabolic response to birth weight, some of which might lie on the pathway between birth weight and adult T2D risk.

Metabolomic characterization of vigor to frailty among community-dwelling older Black and White men and women

Older women and Black individuals are more likely to experience frailty. A metabolomic characterization of frailty may help inform more effective interventions aimed at improving health, reducing disparities, and preventing frailty with aging. We sought to identify metabolites and pathways associated with vigor to frailty and determine whether associations differed by sex and/or race among n = 2189 older Black and White men and women from the Health, Aging, and Body Composition (Health ABC) study. Fasting plasma metabolites were measured using liquid chromatography-mass spectrometry. Vigor to frailty was based on weight change, physical activity, gait speed, grip strength, and usual energy. We used linear regression of a single metabolite on vigor to frailty, adjusting for age, sex, race, study site, and multiple comparisons using a Bonferroni correction. Among 500 metabolites, 113 were associated with vigor to frailty (p < 0.0001). Associations between metabolites and vigor to frailty did not differ significantly by race and/or sex. Lower amino acids, glycerophospholipids, sphingolipids, and dehydroepiandrosterone sulfate and higher acylcarnitines, fatty acids, amino acid derivatives, organic acids, carbohydrates, citric acid cycle metabolites, and trimethylamine oxide were associated with frailer scores. Pathway analyses identified the citric acid cycle as containing more frailty-associated metabolites than expected by chance (p = 0.00005). Calories and protein intake did not differ by vigor to frailty. Frailer Health ABC participants may have lower utilization of energy pathways, potentially as a result of less demand and less efficient utilization of similar amounts of nutrients when compared to more vigorous participants.

Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course

Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.

Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men

BACKGROUND

Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer.

METHODS

We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk.

FINDINGS

After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91).

INTERPRETATION

This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer.

FUNDING

National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].

Untargeted serum metabolomic profiles and breast density in young women

PURPOSE OF THE STUDY

Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women.

METHODS

In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q).

RESULTS

The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV.

CONCLUSION

Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Reliability of Time-Series Plasma Metabolome Data over 6 Years in a Large-Scale Cohort Study

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys-at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies.

Type 2 diabetes metabolomics score and risk of progression to type 2 diabetes among women with a history of gestational diabetes mellitus

BACKGROUND

Several metabolites are individually related to incident type 2 diabetes (T2D) risk. We prospectively evaluated a novel T2D-metabolite pattern with a risk of progression to T2D among high-risk women with a history of gestational diabetes mellitus (GDM).

METHODS

The longitudinal Nurses' Health Study II cohort enroled 116,429 women in 1989 and collected blood samples from 1996 to 1999. We profiled plasma metabolites in 175 incident T2D cases and 175 age-matched controls, all with a history of GDM before the blood draw. We derived a metabolomics score from 21 metabolites previously associated with incident T2D in the published literature by scoring according to the participants' quintile (1-5 points) of each metabolite. We modelled the T2D metabolomics score categorically in quartiles and continuously per 1 standard deviation (SD) with the risk of incident T2D using conditional logistic regression models adjusting for body mass index at the blood draw, and other established T2D risk factors.

RESULTS

The percentage of women progressing to T2D ranged from 10% in the bottom T2D metabolomics score quartile to 78% in the highest score quartile. Adjusting for established T2D risk factors, women in the highest quartile had more than a 20-fold greater diabetes risk than women in the lowest quartile (odds ratios [OR] = 23.1 [95% CI = 8.6, 62.1]; p for trend<0.001). The continuous T2D metabolomics score was strongly and positively associated with incident T2D (adjusted OR = 2.7 per SD [95% CI = 1.9, 3.7], p < 0.0001).

CONCLUSIONS

A pattern of plasma metabolites among high-risk women is associated with a markedly elevated risk of progression to T2D later in life.

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.

Association between plasma L-carnitine levels and mitochondrial DNA copy number

Mitochondria are key cytoplasmic organelles in eukaryotic cells that generate adenosine triphosphate (ATP) through the electron transport chain and oxidative phosphorylation. Mitochondrial DNA (mtDNA) copy number (mtDNAcn) is considered a biomarker for both mitochondrial quantity and function as well as cellular oxidative stress level. Previous epidemiologic findings revealed that weight gain, higher body mass index (BMI), smoking, and high insulinemic potential of lifestyle were associated with lower leukocyte mtDNAcn. Carnitines are a group of compounds that play a critical role in energy production. We quantified the associations of plasma L-carnitine levels with leukocyte mtDNAcn. We then examined the association between mtDNAcn and L-carnitine (HMDB0000062) in 538 U.S. men without cancers, diabetes, or cardiovascular disease at blood collection from the Health Professionals Follow-Up Study (HPFS). We found a significant inverse association between L-carnitine and mtDNAcn (ρ = -0.1, P = 0.02). This implies that the carnitine metabolic pathway may be associated with mitochondrial function and oxidative stress.

Early Detection of Ovarian Cancer

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

Plasma metabolomics supports non-fasted sampling for metabolic profiling across a spectrum of glucose tolerance in the Nile rat model for type 2 diabetes

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8-10-week-old male Nile rats. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

Dietary biomarkers-an update on their validity and applicability in epidemiological studies

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals

Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined.

Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A Population-Based, Prospective Study and Mendelian Randomization Analysis

OBJECTIVE

To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate.

METHODS

We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases.

RESULTS

Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched-chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10-32 ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk.

CONCLUSION

This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.

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.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

Contrasting Association of Maternal Plasma Biomarkers of Smoking and 1-Carbon Micronutrients with Offspring DNA Methylation: Evidence of Aryl Hydrocarbon Receptor Repressor Gene-Smoking-Folate Interaction

BACKGROUND

Maternal prenatal smoking is known to alter offspring DNA methylation (DNAm). However, there are no effective interventions to mitigate smoking-induced DNAm alteration.

OBJECTIVES

This study investigated whether 1-carbon nutrients (folate, vitamins B6, and B12) can protect against prenatal smoking-induced offspring DNAm alterations in the aryl hydrocarbon receptor repressor (AHRR) (cg05575921), GFI1 (cg09935388), and CYP1A1 (cg05549655) genes.

METHODS

This study included mother-newborn dyads from a racially diverse US birth cohort. The cord blood DNAm at the above 3 sites were derived from a previous study using the Illumina Infinium MethylationEPIC BeadChip. Maternal smoking was assessed by self-report and plasma biomarkers (hydroxycotinine and cotinine). Maternal plasma folate, and vitamins B6 and B12 concentrations were obtained shortly after delivery. Linear regressions, Bayesian kernel machine regression, and quantile g-computation were applied to test the study hypothesis by adjusting for covariables and multiple testing.

RESULTS

The study included 834 mother-newborn dyads (16.7% of newborns exposed to maternal smoking). DNAm at cg05575921 (AHRR) and at cg09935388 (GFI1) was inversely associated with maternal smoking biomarkers in a dose-response fashion (all P < 7.01 × 10-13). In contrast, cg05549655 (CYP1A1) was positively associated with maternal smoking biomarkers (P < 2.4 × 10-6). Folate concentrations only affected DNAm levels at cg05575921 (AHRR, P = 0.014). Regression analyses showed that compared with offspring with low hydroxycotinine exposure (<0.494) and adequate maternal folate concentrations (quartiles 2-4), an offspring with high hydroxycotinine exposure (≥0.494) and low folate concentrations (quartile 1) had a significant reduction in DNAm at cg05575921 (M-value, ß ± SE = -0.801 ± 0.117, P = 1.44 × 10-11), whereas adequate folate concentrations could cut smoking-induced hypomethylation by almost half. Exposure mixture models further supported the protective role of adequate folate concentrations against smoking-induced aryl hydrocarbon receptor repressor (AHRR) hypomethylation.

CONCLUSIONS

This study found that adequate maternal folate can attenuate maternal smoking-induced offspring AHRR cg05575921 hypomethylation, which has been previously linked to a range of pediatric and adult diseases.

Plasma metabolite profile for primary open-angle glaucoma in three US cohorts and the UK Biobank

Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide. Primary open-angle glaucoma is the most common form, and yet the etiology of this multifactorial disease is poorly understood. We aimed to identify plasma metabolites associated with the risk of developing POAG in a case-control study (599 cases and 599 matched controls) nested within the Nurses' Health Studies, and Health Professionals' Follow-Up Study. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 369 metabolites from 18 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 metabolites were measured in plasma samples from 2,238 prevalent glaucoma cases and 44,723 controls using NMR spectroscopy (Nightingale, Finland; version 2020). Here we show higher levels of diglycerides and triglycerides are adversely associated with glaucoma in all four cohorts, suggesting that they play an important role in glaucoma pathogenesis.

Gestational Diabetes Is Characterized by Decreased Medium-Chain Acylcarnitines and Elevated Purine Degradation Metabolites across Pregnancy: A Case-Control Time-Course Analysis

Gestational Diabetes Mellitus (GDM) results in complications affecting both mothers and their offspring. Metabolomic analysis across pregnancy provides an opportunity to better understand GDM pathophysiology. The objective was to conduct a metabolomics analysis of first and third trimester plasma samples to identify metabolic differences associated with GDM development. Forty pregnant women with overweight/obesity from a multisite clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 20; GDM group) were matched with those who did not develop GDM (n = 20; Non-GDM group). Plasma samples collected at the first (10-16 weeks) and third (28-35 weeks) trimesters were analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Cardiometabolic risk markers, dietary recalls, and physical activity metrics were also assessed. Four medium-chain acylcarnitines, lauroyl-, octanoyl-, decanoyl-, and decenoylcarnitine, significantly differed over the course of pregnancy in the GDM vs Non-GDM group in a group-by-time interaction (p < 0.05). Hypoxanthine and inosine monophosphate were elevated in the GDM group (p < 0.04). In both groups over time, bile acids and sorbitol increased while numerous acylcarnitines and α-hydroxybutyrate decreased (p < 0.05). Metabolites involved in fatty acid oxidation and purine degradation were altered across the first and third trimesters of GDM-affected pregnancies, providing insight into metabolites and metabolic pathways altered with GDM development.

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-β-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.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case-Control Studies

Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case-control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9-75%) and 28% (95% CI: 2-62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.

A prospective case-cohort analysis of plasma metabolites and breast cancer risk

BACKGROUND

Breast cancer incidence rates have not declined despite an improvement in risk prediction and the identification of modifiable risk factors, suggesting the need to identify novel risk factors and etiological pathways involved in this cancer. Metabolomics has emerged as a promising tool to find circulating metabolites associated with breast cancer risk.

METHODS

Untargeted metabolomic analysis was done on prediagnostic plasma samples from a case-cohort study of 1695 incident breast cancer cases and a 1983 women subcohort drawn from Cancer Prevention Study 3. The associations of 868 named metabolites (per one standard deviation increase) with breast cancer were determined using Prentice-weighted Cox proportional hazards regression modeling.

RESULTS

A total of 11 metabolites were associated with breast cancer at false discovery rate (FDR) < 0.05 with the majority having inverse association [ranging from RR = 0.85 (95% CI 0.80-0.92) to RR = 0.88 (95% CI 0.82-0.94)] and one having a positive association [RR = 1.14 (95% CI 1.06-1.23)]. An additional 50 metabolites were associated at FDR < 0.20 with inverse associations ranging from RR = 0.88 (95% CI 0.81-0.94) to RR = 0.91 (95% CI 0.85-0.98) and positive associations ranging from RR = 1.13 (95% CI 1.05-1.22) to RR = 1.11 (95% CI 1.02-1.20). Several of these associations validated the findings of previous metabolomic studies. These included findings that several progestogen and androgen steroids were associated with increased risk of breast cancer in postmenopausal women and four phospholipids, and the amino acids glutamine and asparagine were associated with decreased risk of this cancer in pre- and postmenopausal women. Several novel associations were also identified, including a positive association for syringol sulfate, a biomarker for smoked meat, and 3-methylcatechol sulfate and 3-hydroxypyridine glucuronide, which are metabolites of xenobiotics used for the production of pesticides and other products.

CONCLUSIONS

Our study validated previous metabolite findings and identified novel metabolites associated with breast cancer risk, demonstrating the utility of large metabolomic studies to provide new leads for understanding breast cancer etiology. Our novel findings suggest that consumption of smoked meats and exposure to catechol and pyridine should be investigated as potential risk factors for breast cancer.

Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20. Results: Four metabolites were inversely associated with risk of RCC during follow-up—C38:4 PI, C34:0 PC, C14:0 SM, and C16:1 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk—C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C38:4 PI, C14:0 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk. Conclusions: Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

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.

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.

Olive- and Coconut-Oil-Enriched Diets Decreased Secondary Bile Acids and Regulated Metabolic and Transcriptomic Markers of Brain Injury in the Frontal Cortexes of NAFLD Pigs

The objective of this study was to investigate the effect of dietary fatty acid (FA) saturation and carbon chain length on brain bile acid (BA) metabolism and neuronal number in a pig model of pediatric NAFLD. Thirty 20-day-old Iberian pigs, pair-housed in pens, were randomly assigned to receive one of three hypercaloric diets for 10 weeks: (1) lard-enriched (LAR; n = 5 pens), (2) olive-oil-enriched (OLI, n = 5), and (3) coconut-oil-enriched (COC; n = 5). Pig behavior and activity were analyzed throughout the study. All animals were euthanized on week 10 and frontal cortex (FC) samples were collected for immunohistochemistry, metabolomic, and transcriptomic analyses. Data were analyzed by multivariate and univariate statistics. No differences were observed in relative brain weight, neuronal number, or cognitive functioning between diets. Pig activity and FC levels of neuroprotective secondary BAs and betaine decreased in the COC and OLI groups compared with LAR, and paralleled the severity of NAFLD. In addition, OLI-fed pigs showed downregulation of genes involved in neurotransmission, synaptic transmission, and nervous tissue development. Similarly, COC-fed pigs showed upregulation of neurogenesis and myelin repair genes, which caused the accumulation of medium-chain acylcarnitines in brain tissue. In conclusion, our results indicate that secondary BA levels in the FCs of NAFLD pigs are affected by dietary FA composition and are associated with metabolic and transcriptomic markers of brain injury. Dietary interventions that aim to replace saturated FAs by medium-chain or monounsaturated FAs in high-fat hypercaloric diets may have a negative effect on brain health in NAFLD patients.

Reproducibility of the Blood and Urine Exposome: A Systematic Literature Review and Meta-Analysis

Endogenous and exogenous metabolite concentrations may be susceptible to variation over time. This variability can lead to misclassification of exposure levels and in turn to biased results. To assess the reproducibility of metabolites, the intraclass correlation coefficient (ICC) is computed. A literature search in three databases from 2000 to May 2021 was conducted to identify studies reporting ICCs for blood and urine metabolites. This review includes 192 studies, of which 31 studies are included in the meta-analyses. The ICCs of 359 single metabolites are reported, and the ICCs of 10 metabolites were meta-analyzed. The reproducibility of the single metabolites ranges from poor to excellent and is highly compound-dependent. The reproducibility of bisphenol A (BPA), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-2-ethylhexyl phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), methylparaben, and propylparaben is poor to moderate (ICC median: 0.32; range: 0.15-0.49), and for 25-hydroxyvitamin D [25(OH)D], it is excellent (ICC: 0.95; 95% CI, 0.90-0.99). Pharmacokinetics, mainly the half-life of elimination and exposure patterns, can explain reproducibility. This review describes the reproducibility of the blood and urine exposome, provides a vast dataset of ICC estimates, and hence constitutes a valuable resource for future reproducibility and clinical epidemiologic studies.

Prediagnostic Plasma Metabolomics and the Risk of Exfoliation Glaucoma

Purpose

The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG.

Methods

We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR).

Results

Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P < 0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR < 0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05).

Conclusions

In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.

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.

Plasma metabolite profiles related to plant-based diets and the risk of type 2 diabetes

AIMS/HYPOTHESIS

Plant-based diets, especially when rich in healthy plant foods, have been associated with a lower risk of type 2 diabetes. However, whether plasma metabolite profiles related to plant-based diets reflect this association was unknown. The aim of this study was to identify the plasma metabolite profiles related to plant-based diets, and to evaluate the associations between the identified metabolite profiles and the risk of type 2 diabetes.

METHODS

Within three prospective cohorts (Nurses' Health Study, Nurses' Health Study II and Health Professionals Follow-up Study), we measured plasma metabolites from 10,684 participants using high-throughput LC MS. Adherence to plant-based diets was assessed by three indices derived from the food frequency questionnaire: an overall Plant-based Diet Index (PDI), a Healthy Plant-based Diet Index (hPDI), and an Unhealthy Plant-based Diet Index (uPDI). Multi-metabolite profiles related to plant-based diet were identified using elastic net regression with a training/testing approach. The prospective associations between metabolite profiles and incident type 2 diabetes were evaluated using multivariable Cox proportional hazards regression. Metabolites potentially mediating the association between plant-based diets and type 2 diabetes risk were further identified.

RESULTS

We identified multi-metabolite profiles comprising 55 metabolites for PDI, 93 metabolites for hPDI and 75 metabolites for uPDI. Metabolite profile scores based on the identified metabolite profiles were correlated with the corresponding diet index (Pearson r = 0.33-0.35 for PDI, 0.41-0.45 for hPDI, and 0.37-0.38 for uPDI, all p<0.001). Metabolite profile scores of PDI (HR per 1 SD higher = 0.81 [95% CI 0.75, 0.88]) and hPDI (HR per 1 SD higher = 0.77 [95% CI 0.71, 0.84]) showed an inverse association with incident type 2 diabetes, whereas the metabolite profile score for uPDI was not associated with the risk. Mutual adjustment for metabolites selected in the metabolite profiles, including trigonelline, hippurate, isoleucine and a subset of triacylglycerols, attenuated the associations of diet indices PDI and hPDI with lower type 2 diabetes risk. The explainable proportion of PDI/hPDI-related diabetes risk by these metabolites ranged between 8.5% and 37.2% (all p<0.05).

CONCLUSIONS/INTERPRETATION

Plasma metabolite profiles related to plant-based diets, especially a healthy plant-based diet, were associated with a lower risk of type 2 diabetes among a generally healthy population. Our findings support the beneficial role of healthy plant-based diets in diabetes prevention and provide new insights for future investigation.

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.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

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.

Presurgical blood metabolites and risk of postsurgical pelvic pain in young patients with endometriosis

OBJECTIVE

To identify metabolites in presurgical blood associated with risk of persistent postsurgical pelvic pain 1 year after endometriosis surgery in adolescent and young adult patients.

DESIGN

Prospective observational study within the Women's Health Study: From Adolescence to Adulthood, a US-based longitudinal cohort of adolescents and women enrolled from 2012-2018.

SETTING

Two tertiary care hospitals.

PATIENT(S)

Laparoscopically confirmed endometriosis patients (n = 180) with blood collected before their endometriosis surgery. Of these, 77 patients additionally provided blood samples 5 weeks to 6 months after their surgery. We measured plasma metabolites using liquid chromatography tandem mass spectrometry, and a total of 390 known metabolites were included in our analysis.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Persistent postsurgical pelvic pain, defined as severe, life-impacting pelvic pain 1 year after endometriosis surgery.

RESULT(S)

Most patients (>95%) were at stage I/II of the revised American Society for Reproductive Medicine classification. Their average age at diagnosis was 18.7 years, with 36% reporting persistent postsurgical pelvic pain. Of the 21 metabolites in presurgical blood that were associated with risk of persistent postsurgical pelvic pain, 19 metabolites, which were mainly lipid metabolites, were associated with increased risk. Only 2 metabolites-pregnenolone sulfate (odds ratio = 0.64, 95% confidence interval = 0.44-0.92) and fucose (odds ratio = 0.69, 95% confidence interval = 0.47-0.97)-were associated with decreased risk. Metabolite set enrichment analysis revealed that higher levels of lysophosphatidylethanolamines (false discovery rate = 0.01) and lysophosphatidylcholines (false discovery rate = 0.01) in presurgical blood were associated with increased risk of persistent postsurgical pelvic pain.

CONCLUSION(S)

Our results suggest that dysregulation of multiple groups of lipid metabolites may play a role in the persistence of pelvic pain postsurgery among young endometriosis patients.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Plasma Metabolite Profiles of Red Meat, Poultry, and Fish Consumption, and Their Associations with Colorectal Cancer Risk

BACKGROUND

Red and processed meat consumption has been consistently associated with increased risk of colorectal cancer (CRC), but the association for fish intake is unclear. Evidence using objective dietary assessment approaches to evaluate these associations is sparse.

OBJECTIVES

We aim to investigate the plasma metabolite profiles related to red meat, poultry, and fish consumption and examine their associations with CRC risk.

METHODS

We measured plasma metabolites among 5269 participants from the Nurses' Health Study (NHS), NHSII, and Health Professionals Follow-Up study (HPFS). We calculated partial Spearman correlations between each metabolite and self-reported intake of seven red meat, poultry, and fish groups. Metabolite profile scores correlated to self-reported dietary intakes were developed using elastic net regression. Associations between self-reported intakes, metabolite profile scores, and subsequent CRC risk were further evaluated using conditional logistic regression among 559 matched (1:1) case-control pairs in NHS/HPFS and replicated among 266 pairs in Women's Health Study.

RESULTS

Plasma metabolites, especially highly unsaturated lipids, were differentially associated with red meat and fish groups. Metabolite profile scores for each food group were significantly correlated with the corresponding self-reported dietary intake. A higher dietary intake of processed red meat was associated with a higher risk of CRC (pooled OR per 1 SD, 1.15; 95% CI: 1.03, 1.29). In contrast, higher metabolite profile scores for all fish groups, not dietary intakes, were consistently associated with a lower CRC risk: the pooled OR per 1 SD was 0.86 (95% CI: 0.78, 0.96) for total fish, 0.86 (95% CI: 0.77, 0.96) for dark meat fish, and 0.87 (95% CI: 0.78, 0.97) for canned tuna fish. No significant associations were found for other food groups.

CONCLUSIONS

Red meat and fish intake exhibited systematically different plasma metabolite profiles. Plasma metabolite profile of fish intake was inversely associated with CRC risk.

Circulating metabolites as a concept beyond tumor biology determining disease recurrence after resection of colorectal liver metastasis

BACKGROUND

Micro-metastatic growth is considered the main source of early cancer recurrence. Nutritional and microenvironmental components are increasingly recognized to play a significant role in the liver. We explored the predictive potential of preoperative plasma metabolites for postoperative disease recurrence in colorectal cancer liver metastasis (CRCLM) patients.

METHODS

All included patients (n = 71) had undergone R0 liver resection for colorectal cancer liver metastasis in the years between 2012 and 2018. Preoperative blood samples were collected and assessed for 180 metabolites using a preconfigured mass-spectrometry kit (Biocrates Absolute IDQ p180 kit). Postoperative disease-free (DFS) and overall survival (OS) were prospectively recorded. Patients that recurred within 6 months after surgery were defined as "high-risk" and, subsequently, a three-metabolite model was created which can assess DFS in our collective.

RESULTS

Multiple lysophosphatidylcholines (lysoPCs) and phosphatidylcholines (PCs) significantly predicted disease recurrence within 6 months (strongest: PC aa C36:1 AUC = 0.83, p = 0.003, PC ae C34:0 AUC = 0.83, p = 0.004 and lysoPC a C18:1 AUC = 0.8, p = 0.006). High-risk patients had a median DFS of 183 days versus 522 days in low-risk population (p = 0.016, HR = 1.98 95% CI 1.16-4.35) with a 6 months recurrence rate of 47.6% versus 4.7%, outperforming routine predictors of oncological outcome.

CONCLUSION

Circulating metabolites identified CRCLM patients at highest risk for 6 months disease recurrence after surgery. Our data also suggests that circulating metabolites might play a significant pathophysiological role in micro-metastatic growth and concomitant early tumor recurrences after liver resection. However, the clinical applicability and performance of this proposed metabolomic concept needs to be independently validated in future studies.

Evaluation of heparinized syringes for measuring newborn metabolites in neonates with a central arterial line

Newborn metabolic screening is emerging as a novel method for predicting neonatal morbidity and mortality in neonates born very preterm (<32 weeks gestation). The purpose of our study was to determine if blood collected by an electrolyte-balanced dry lithium heparin syringe, as is routine for blood gas measurements, affects targeted metabolite and biomarker levels. Two blood samples (one collected with a heparinized syringe and the other with a non-heparinized syringe) were obtained at the same time from 20 infants with a central arterial line and tested for 49 metabolites and biomarkers using standard procedures for newborn screening. Overall, the median metabolite levels did not significantly differ by syringe type. However, there was wide variability, particularly for amino acids and immunoreactive trypsinogen, for individual paired samples and therefore, consideration should be given to sample collection when using these metabolites in prediction models of neonatal morbidity and mortality.

Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: Results from the Nurses' Health Study

BACKGROUND

Metabolomics profiles were consistently associated with type 2 diabetes (T2D) risk, but evidence on long-term metabolite changes and T2D incidence is lacking. We examined the associations of 10-year plasma metabolite changes with subsequent T2D risk.

METHODS

We conducted a nested T2D case-control study (n=244 cases, n=244 matched controls) within the Nurses' Health Study. Repeated metabolomics profiling (170 targeted metabolites) was conducted in participant blood specimens from 1989/1990 and 2000/2001, and T2D occurred between 2002 and 2008. We related 10-year metabolite changes (Δ-values) to subsequent T2D risk using conditional logistic models, adjusting for baseline metabolite levels and baseline levels and concurrent changes of BMI, diet quality, physical activity, and smoking status.

FINDINGS

The 10-year changes of thirty-one metabolites were associated with subsequent T2D risk (false discovery rate-adjusted p-values [FDR]<0.05). The top three high T2D risk-associated 10-year changes were (odds ratio [OR] per standard deviation [SD], 95%CI): Δisoleucine (2.72, 1.97-3.79), Δleucine (2.53, 1.86-3.47), and Δvaline (1.93, 1.52-2.44); other high-risk-associated metabolite changes included alanine, tri-/diacylglycerol-fragments, short-chain acylcarnitines, phosphatidylethanolamines, some vitamins, and bile acids (ORs per SD between 1.31and 1.82). The top three low T2D risk-associated 10-year metabolite changes were (OR per SD, 95% CI): ΔN-acetylaspartic acid (0.54, 0.42-0.70), ΔC20:0 lysophosphatidylethanolamine (0.68, 0.56-0.82), and ΔC16:1 sphingomyelin (0.68, 0.56-0.83); 10-year changes of other sphingomyelins, plasmalogens, glutamine, and glycine were also associated with lower subsequent T2D risk (ORs per SD between 0.66 and 0.78).

INTERPRETATION

Repeated metabolomics profiles reflecting the long-term deterioration of amino acid and lipid metabolism are associated with subsequent risk of T2D.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.