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

Metabolomic heterogeneity of ageing with ethnic diversity: a step closer to healthy ageing

INTRODUCTION

Outside of case-control settings, ethnicity specific changes in the human metabolome are understudied especially in community dwelling, ageing men. Characterising serum for age and ethnicity specific features can enable tailored therapeutics research and improve our understanding of the interplay between age, ethnicity, and metabolism in global populations.

OBJECTIVE

A metabolomics approach was adopted to profile serum metabolomes in middle-aged and elderly men of different ethnicities from the Northwest of England, UK.

METHODS

Serum samples from 572 men of White European (WE), South Asian (SA), and African-Caribbean (AC) ethnicities, ranging between 40 and 86 years were analysed. A combination of liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry (MS) was used to generate the metabolomic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) based classification models were built and validated using resampling via bootstrap analysis and permutation testing. Features were putatively annotated using public Human Metabolome Database (HMDB) and Golm Metabolite Database (GMD). Variable Importance in Projection (VIP) scores were used to determine features of interest, after which pathway enrichment analysis was performed.

RESULTS

Using profiles from our analysis we classify subjects by their ethnicity with an average correct classification rate (CCR) of 90.53% (LC-MS data) and 85.58% (GC-MS data). Similar classification by age (< 60 vs. ≥ 60 years) returned CCRs of 90.20% (LC-MS) and 71.13% (GC-MS). VIP scores driven feature selection revealed important compounds from putatively annotated lipids (subclasses including fatty acids and carboxylic acids, glycerophospholipids, steroids), organic acids, amino acid derivatives as key contributors to the classifications. Pathway enrichment analysis using these features revealed statistically significant perturbations in energy metabolism (TCA cycle), N-Glycan and unsaturated fatty acid biosynthesis linked pathways amongst others.

CONCLUSION

We report metabolic differences measured in serum that can be attributed to ethnicity and age in healthy population. These results strongly emphasise the need to consider confounding effects of inherent metabolic variations driven by ethnicity of participants in population-based metabolic profiling studies. Interpretation of energy metabolism, N-Glycan and fatty acid biosynthesis should be carefully decoupled from the underlying differences in ethnicity of participants.

Structural and social determinants of health: The multi-ethnic study of atherosclerosis

BACKGROUND

Researchers have increasingly recognized the importance of structural and social determinants of health (SSDOH) as key drivers of a multitude of diseases and health outcomes. The Multi-Ethnic Study of Atherosclerosis (MESA) is an ongoing, longitudinal cohort study of subclinical cardiovascular disease (CVD) that has followed geographically and racially/ethnically diverse participants starting in 2000. Since its inception, MESA has incorporated numerous SSDOH assessments and instruments to study in relation to CVD and aging outcomes. In this paper, we describe the SSDOH data available in MESA, systematically review published papers using MESA that were focused on SSDOH and provide a roadmap for future SSDOH-related studies.

METHODS AND FINDINGS

The study team reviewed all published papers using MESA data (n = 2,125) through January 23, 2023. Two individuals systematically reviewed titles, abstracts, and full text to determine the final number of papers (n = 431) that focused on at least one SSDOH variable as an exposure, outcome, or stratifying/effect modifier variable of main interest (discrepancies resolved by a third individual). Fifty-seven percent of the papers focused on racialized/ethnic groups or other macrosocial/structural factors (e.g., segregation), 16% focused on individual-level inequalities (e.g. income), 14% focused on the built environment (e.g., walking destinations), 10% focused on social context (e.g., neighborhood socioeconomic status), 34% focused on stressors (e.g., discrimination, air pollution), and 4% focused on social support/integration (e.g., social participation). Forty-seven (11%) of the papers combined MESA with other cohorts for cross-cohort comparisons and replication/validation (e.g., validating algorithms).

CONCLUSIONS

Overall, MESA has made significant contributions to the field and the published literature, with 20% of its published papers focused on SSDOH. Future SSDOH studies using MESA would benefit by using recently added instruments/data (e.g., early life educational quality), linking SSDOH to biomarkers to determine underlying causal mechanisms linking SSDOH to CVD and aging outcomes, and by focusing on intersectionality, understudied SSDOH (i.e., social support, social context), and understudied outcomes in relation to SSDOH (i.e., sleep, respiratory health, cognition/dementia).

Untargeted lipidomics reveals racial differences in lipid species among women

Understanding the biological mechanisms underlying racial differences in diseases is crucial to developing targeted prevention and treatment. There is, however, limited knowledge of the impact of race on lipids. To address this, we performed comprehensive lipidomics analyses to evaluate racial differences in lipid species among 506 non-Hispanic White (NHW) and 163 non-Hispanic Black (NHB) women. Plasma lipidomic profiling quantified 982 lipid species. We used multivariable linear regression models, adjusted for confounders, to identify racial differences in lipid species and corrected for multiple testing using a Bonferroni-adjusted p-value < 10-5. We identified 248 lipid species that were significantly associated with race. NHB women had lower levels of several lipid species, most notably in the triacylglycerols sub-pathway (N = 198 out of 518) with 46 lipid species exhibiting an absolute percentage difference ≥ 50% lower in NHB compared with NHW women. We report several novel differences in lipid species between NHW and NHB women, which may underlie racial differences in health and have implications for disease prevention.

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review

Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.

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.

Sex-Associated Metabolites and Incident Stroke, Incident Coronary Heart Disease, Hypertension, and Chronic Kidney Disease in the REGARDS Cohort

BACKGROUND

Sex disparities exist in cardiometabolic diseases. Metabolomic profiling offers insight into disease mechanisms, as the metabolome is influenced by environmental and genetic factors. We identified metabolites associated with sex and determined if sex-associated metabolites are associated with incident stoke, incident coronary heart disease, prevalent hypertension, and prevalent chronic kidney disease.

METHODS AND RESULTS

Targeted metabolomics was conducted for 357 metabolites in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) case-cohort substudy for incident stroke. Weighted logistic regression models were used to identify metabolites associated with sex in REGARDS. Sex-associated metabolites were replicated in the HyperGEN (Hypertension Genetic Epidemiology Network) and using the literature. Weighted Cox proportional hazard models were used to evaluate associations between metabolites and incident stroke. Cox proportional hazard models were used to evaluate associations between metabolites and incident coronary heart disease. Weighted logistic regression models were used to evaluate associations between metabolites and hypertension and chronic kidney disease. Fifty-one replicated metabolites were associated with sex. Higher levels of 6 phosphatidylethanolamines were associated with incident stroke. No metabolites were associated with incident coronary heart disease. Higher levels of uric acid and leucine and lower levels of a lysophosphatidylcholine were associated with hypertension. Higher levels of indole-3-lactic acid, 7 phosphatidylethanolamines, and uric acid, and lower levels of betaine and bilirubin were associated with chronic kidney disease.

CONCLUSIONS

These findings suggest that the sexual dimorphism of the metabolome may contribute to sex differences in stroke, hypertension, and chronic kidney disease.

Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls

AIMS

Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.

MATERIALS AND METHODS

We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis.

RESULTS

The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.

CONCLUSIONS

We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

Metabolomic profiles associated with physical activity in White and African American adult men

BACKGROUND

Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations.

OBJECTIVE

This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men.

METHODS

Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking.

RESULTS

Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups.

CONCLUSIONS

Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective

Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.

Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.

Metabolomics and Self-Reported Depression, Anxiety, and Phobic Symptoms in the VA Normative Aging Study

Traditional approaches to understanding metabolomics in mental illness have focused on investigating a single disorder or comparisons between diagnoses, but a growing body of evidence suggests substantial mechanistic overlap in mental disorders that could be reflected by the metabolome. In this study, we investigated associations between global plasma metabolites and abnormal scores on the depression, anxiety, and phobic anxiety subscales of the Brief Symptom Inventory (BSI) among 405 older males who participated in the Normative Aging Study (NAS). Our analysis revealed overlapping and distinct metabolites associated with each mental health dimension subscale and four metabolites belonging to xenobiotic, carbohydrate, and amino acid classes that were consistently associated across all three symptom dimension subscales. Furthermore, three of these four metabolites demonstrated a higher degree of alteration in men who reported poor scores in all three dimensions compared to men with poor scores in only one, suggesting the potential for shared underlying biology but a differing degree of perturbation when depression and anxiety symptoms co-occur. Our findings implicate pathways of interest relevant to the overlap of mental health conditions in aging veterans and could represent clinically translatable targets underlying poor mental health in this high-risk population.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limits applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of high-resolution mass spectrometry coupled with liquid chromatography peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R-CRAN repository at https://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.CSA . Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA .

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Human Metabolome Reference Database in a Biracial Cohort across the Adult Lifespan

As one of the OMICS in systems biology, metabolomics defines the metabolome and simultaneously quantifies numerous metabolites that are final or intermediate products and effectors of upstream biological processes. Metabolomics provides accurate information that helps determine the physiological steady state and biochemical changes during the aging process. To date, reference values of metabolites across the adult lifespan, especially among ethnicity groups, are lacking. The "normal" reference values according to age, sex, and race allow the characterization of whether an individual or a group deviates metabolically from normal aging, encompass a fundamental element in any study aimed at understanding mechanisms at the interface between aging and diseases. In this study, we established a metabolomics reference database from 20-100 years of age from a biracial sample of community-dwelling healthy men and women and examined metabolite associations with age, sex, and race. Reference values from well-selected healthy individuals can contribute to clinical decision-making processes of metabolic or related diseases.

Serum sphingolipid profiling as a novel biomarker for metabolic syndrome characterization

Background

Sphingolipids are components of cell membrane structure, but also circulate in serum and are essential mediators of many cellular functions. While ceramides have been proposed previously as a useful biomarker for cardiometabolic disease, the involvement of other sphingolipids is still controversial. The aim of this study was to investigate the cross-sectional association between blood sphingolipidomic profiles and metabolic syndrome (MetS) as well as other atherosclerotic risk factors in a large population-based study in the U.S.

Methods

Clinical data and serum sphingolipidomic profiling from 2,063 subjects who participated in the biomarker project of the Midlife in the United States (MIDUS) study were used.

Results

Consistent with previous reports, we found a positive association between most ceramide levels and obesity, atherogenic dyslipidemia, impaired glucose metabolism, and MetS prevalence. In contrast, most simple β-glycosphingolipids (i.e., hexosylceramides and lactosylceramides) were inversely associated with dysmetabolic biomarkers. However, this latter sphingolipid class showed a positive link with inflammatory and vascular damage-associated biomarkers in subjects with MetS. Through metabolic network analysis, we found that the relationship between ceramides and simple β-glycosphingolipids differed significantly not only according to MetS status, but also with respect to the participants' C-reactive protein levels.

Conclusion

Our findings suggest that a comprehensive sphingolipid profile is more informative about MetS than ceramides alone, and it may reveal new insights into the pathophysiology and further diabetic vs. cardiovascular risk in patients with MetS.