Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Relationship between circulating metabolites and diabetic retinopathy: a two-sample Mendelian randomization analysis

Diabetic retinopathy (DR) is the most frequent microvascular complication of diabetes mellitus, however, its underlying biological mechanisms remain poorly understood. We examined single nucleotide polymorphisms linked to 486 blood metabolites through extensive genome-wide association studies conducted on individuals of European ancestry. The FinnGen Biobank database served as a reference to define DR. Two-sample MR analysis was conducted to reveal the association between the levels of genetically predicted circulating metabolites and the susceptibility to DR. To validate the robustness of the obtained findings, sensitivity analyses with weighted median, weighted mode, and MR-Egger were conducted. 1-oleoylglycerophosphoethanolamine (odds ratio [OR] (OR per one standard deviation [SD] increase) = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 7.613E-07, PFDR = 6.849E-06), pyroglutamine (OR per one SD increase = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 8.31E-04, PFDR = 0.007), phenyllactate (PLA) (OR per one SD increase = 0.591; 95% confidence interval [CI] 0.418-0.836; P = 0.003, PFDR = 0.026), metoprolol acid metabolite (OR per one SD increase = 0.978; 95% confidence interval [CI] 0.962-0.993; P = 0.005, PFDR = 0.042), 10-undecenoate (OR per one SD increase = 0.788; 95% confidence interval [CI] 0.667-0.932; P = 0.005, PFDR = 0.049), erythritol (OR per one SD increase = 0.691; 95% confidence interval [CI] 0.513-0.932; P = 0.015, PFDR = 0.034), 1-stearoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.022, PFDR = 0.099), 1-arachidonoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.030, PFDR = 0.099) showed a significant causal relationship with DR and could have protective effects. stachydrine (OR per one SD increase = 1.146; 95% confidence interval [CI] 1.066-1.233; P = 2.270E-04, PFDR = 0.002), butyrylcarnitine (OR per one SD increase = 1.117; 95% confidence interval [CI] 1.023-1.219; P = 0.014, PFDR = 0.062), 5-oxoproline (OR per one SD increase = 1.569; 95% confidence interval [CI] 1.056-2.335; P = 0.026, PFDR = 0.082), and kynurenine (OR = 1.623; 95% CI 1.042-2.526; P = 0.041, PFDR = 0.097) were significantly associated with an increased risk of DR. This study identified metabolites have the potential to be considered prospective compounds for investigating the underlying mechanisms of DR and for selecting appropriate drug targets.

Metabolomic profiles of sleep-disordered breathing are associated with hypertension and diabetes mellitus development

Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.

The challenges of defining hormesis in epidemiological studies: The case of radiation hormesis

In the current radiation protection system, preventive measures and occupational exposure limits for controlling occupational exposure to ionizing radiation are based on the linear no-threshold extrapolation model. However, currently an increasing body of evidence indicates that this paradigm predicts very poorly biological responses in the low-dose exposure region. In addition, several in vitro and in vivo studies demonstrated the presence of hormetic dose response curves correlated to ionizing radiation low exposure. In this regard, it is noteworthy that also the findings of different epidemiological studies, conducted in different categories of occupationally exposed workers (e.g., healthcare, nuclear industrial and aircrew workers), observed lower rates of mortality and/or morbidity from cancer and/or other diseases in exposed workers than in unexposed ones or in the general population, then suggesting the possible occurrence of hormesis. Nevertheless, these results should be considered with caution since the identification of hormetic response in epidemiological studies is rather challenging because of a number of major limitations. In this regard, some of the most remarkable shortcomings found in epidemiological studies performed in workers exposed to ionizing radiation are represented by lack or inadequate definition of exposure doses, use of surrogates of exposure, narrow dose ranges, lack of proper control groups and poor evaluation of confounding factors. Therefore, considering the valuable role and contribution that epidemiological studies might provide to the complex risk assessment and management process, there is a clear and urgent need to overcome the aforementioned limits in order to achieve an adequate, useful and more real-life risk assessment that should also include the key concept of hormesis. Thus, in the present conceptual article we also discuss and provide possible approaches to improve the capacity of epidemiological studies to identify/define the hormetic response and consequently improve the complex process of risk assessment of ionizing radiation at low exposure doses.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Temperature differences between sites lead to altered phenylpropanoid metabolism in a varietal dependent manner

Elevated temperature has already caused a significant loss of wine growing areas and resulted in inferior fruit quality, particularly in arid and semi-arid regions. The existence of broad genetic diversity in V. vinifera is key in adapting viticulture to climate change; however, a lack of understanding on the variability in berry metabolic response to climate change remains a major challenge to build ad-hoc strategies for quality fruit production. In the present study, we examined the impact of a consistent temperature difference between two vineyards on polyphenol metabolism in the berries of 20 red V. vinifera cultivars across three consecutive seasons (2017-2019). The results emphasize a varietal specific response in the content of several phenylpropanoid metabolites; the interaction factor between the variety and the vineyard location was also found significant. Higher seasonal temperatures were coupled with lower flavonol and anthocyanin contents, but such reductions were not related with the level of expression of phenylpropanoid related genes. Hierarchical clustering analyses of the metabolic data revealed varieties with a location specific response, exceptional among them was Tempranillo, suggesting a greater susceptibility to temperature of this cultivar. In conclusion, our results indicate that the extensive genetic capacity of V. vinifera bears a significant potential to withstand temperature increase associated with climate change.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.

Metabolomic epidemiology offers insights into disease aetiology

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

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.

Untargeted Multiomics Approach Coupling Lipidomics and Metabolomics Profiling Reveals New Insights in Diabetic Retinopathy

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) which is the main cause of vision loss in the working-age population. Currently known risk factors such as age, disease duration, and hemoglobin A1c lack sufficient efficiency to distinguish patients with early stages of DR. A total of 194 plasma samples were collected from patients with type 2 DM and DR (moderate to proliferative (PDR) or control (no or mild DR) matched for age, gender, diabetes duration, HbA1c, and hypertension. Untargeted lipidomic and metabolomic approaches were performed. Partial-least square methods were used to analyze the datasets. Levels of 69 metabolites and 85 lipid species were found to be significantly different in the plasma of DR patients versus controls. Metabolite set enrichment analysis indicated that pathways such as metabolism of branched-chain amino acids (methylglutaryl carnitine p = 0.004), the kynurenine pathway (tryptophan p < 0.001), and microbiota metabolism (p-Cresol sulfate p = 0.004) were among the most enriched deregulated pathways in the DR group. Moreover, Glucose-6-phosphate (p = 0.001) and N-methyl-glutamate (p < 0.001) were upregulated in DR. Subgroup analyses identified a specific signature associated with PDR, macular oedema, and DR associated with chronic kidney disease. Phosphatidylcholines (PCs) were dysregulated, with an increase of alkyl-PCs (PC O-42:5 p < 0.001) in DR, while non-ether PCs (PC 14:0-16:1, p < 0.001; PC 18:2-14:0, p < 0.001) were decreased in the DR group. Through an unbiased multiomics approach, we identified metabolites and lipid species that interestingly discriminate patients with or without DR. These features could be a research basis to identify new potential plasma biomarkers to promote 3P medicine.

Data integration and analysis for circadian medicine

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.

'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map

BACKGROUND

Systematic evidence maps are increasingly used to develop chemical risk assessments. These maps can provide an overview of available studies and relevant study information to be used for various research objectives and applications. Environmental epidemiological studies that examine the impact of chemical exposures on various 'omic profiles in human populations provide relevant mechanistic information and can be used for benchmark dose modeling to derive potential human health reference values.

OBJECTIVES

To create a systematic evidence map of environmental epidemiological studies examining environmental contaminant exposures with 'omics in order to characterize the extent of available studies for future research needs.

METHODS

Systematic review methods were used to search and screen the literature and included the use of machine learning methods to facilitate screening studies. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were developed to identify and screen relevant studies. Studies that met the PECO criteria after full-text review were summarized with information such as study population, study design, sample size, exposure measurement, and 'omics analysis.

RESULTS

Over 10,000 studies were identified from scientific databases. Screening processes were used to identify 84 studies considered PECO-relevant after full-text review. Various contaminants (e.g. phthalate, benzene, arsenic, etc.) were investigated in epidemiological studies that used one or more of the four 'omics of interest: epigenomics, transcriptomics, proteomics, and metabolomics . The epidemiological study designs that were used to explore single or integrated 'omic research questions with contaminant exposures were cohort studies, controlled trials, cross-sectional, and case-control studies. An interactive web-based systematic evidence map was created to display more study-related information.

CONCLUSIONS

This systematic evidence map is a novel tool to visually characterize the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology and serves as a resource for investigators and allows for a range of applications in chemical research and risk assessment needs.

Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria

The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by ¹H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

A Novel Integrated Biomarker for Evaluation of Risk and Severity of Coronary Atherosclerosis, and Its Validation

Objective: To assess the feasibility of a combination of biochemical and imaging parameters for estimation of risk and severity of coronary atherosclerosis (CA), and to verify the created integrated biomarker (i-BIO) on independent cohort. Methods: Two cohorts of patients admitted to the hospital for coronary angiography and ultrasound carotid dopplerography were enrolled into the study (n = 205 and n = 216, respectively). The extent of CA was assessed by Gensini Score (GS). Results: According to GS, participants were distributed as follows: atherosclerosis-free (GS = 0), CA of any stage (GS > 0), subclinical CA (GS < 35), severe CA (GS ≥ 35). Based on the analysis of mathematical models, including biochemical and imaging parameters, we selected and combined the most significant variables as i-BIO. The ability of i-BIO to detect the presence and severity of CA was estimated using ROC-analysis with cut-off points determination. Risk of any CA (GS > 0) at i-BIO > 4 was 7.3 times higher than in those with i-BIO ≤ 4; risk of severe CA (GS ≥ 35) at i-BIO ≥ 9 was 3.1 times higher than at i-BIO < 9. Results on the tested cohort confirmed these findings. Conclusions: The i-BIO > 4 detected CA (GS > 0) with sensitivity of 87.9%, i-BIO ≥ 9 excluded patients without severe CA (GS < 35), specificity 79.8%. Validation of i-BIO confirmed the feasibility of i-BIO > 4 to separate patients with any CA with sensitivity 76.2%, and of i-BIO ≥ 9 to exclude atherosclerosis-free subjects with specificity of 84.0%.

A preliminary model of football-related neural stress that integrates metabolomics with transcriptomics and virtual reality

Research suggests contact sports affect neurological health. This study used permutation-based mediation statistics to integrate measures of metabolomics, neuroinflammatory miRNAs, and virtual reality (VR)-based motor control to investigate multi-scale relationships across a season of collegiate American football. Fourteen significant mediations (six pre-season, eight across-season) were observed where metabolites always mediated the statistical relationship between miRNAs and VR-based motor control (pSobelperm≤ 0.05; total effect > 50%), suggesting a hypothesis that metabolites sit in the statistical pathway between transcriptome and behavior. Three results further supported a model of chronic neuroinflammation, consistent with mitochondrial dysfunction: (1) Mediating metabolites were consistently medium-to-long chain fatty acids, (2) tricarboxylic acid cycle metabolites decreased across-season, and (3) accumulated head acceleration events statistically moderated pre-season metabolite levels to directionally model post-season metabolite levels. These preliminary findings implicate potential mitochondrial dysfunction and highlight probable peripheral blood biomarkers underlying repetitive head impacts in otherwise healthy collegiate football athletes.

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Permutation-based mediation statistics can be applied to multi-scale biology problems

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Fatty acids were a critical link between elevated miRNAs and motor control

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HAEs interacted with pre-season metabolite levels to model post-season levels

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Together, our observations point to brain-related mitochondrial dysfunction

Mitochondrial-derived peptides: New markers for cardiometabolic dysfunction

Great attention is being paid to the evaluation of new markers in blood circulation for the estimation of tissue metabolism disturbance. This endogenous disturbance may contribute to the onset and progression of cardiometabolic disease. In addition to their role in energy production and metabolism, mitochondria play a main function in cellular mechanisms, including apoptosis, oxidative stress and calcium homeostasis. Mitochondria produce mitochondrial-derived peptides that mediate the transcriptional stress response by translocating into the nucleus and interacting with deoxyribonucleic acid. This class of peptides includes humanin, mitochondrial open reading frame of the 12S ribosomal ribonucleic acid type c (MOTS-c) and small humanin-like peptides. Mitochondrial-derived peptides are regulators of metabolism, exerting cytoprotective effects through antioxidative stress, anti-inflammatory responses and antiapoptosis; they are emerging biomarkers reflecting mitochondrial function, and the circulating concentration of these proteins can be used to diagnose cardiometabolic dysfunction. The aims of this review are: (1) to describe the emerging role for mitochondrial-derived peptides as biomarkers; and (2) to discuss the therapeutic application of these peptides.

Metabolic Impairment in Coronary Artery Disease: Elevated Serum Acylcarnitines Under the Spotlights

Coronary artery disease (CAD) remains the leading cause of death worldwide. Expanding patients' metabolic phenotyping beyond clinical chemistry investigations could lead to earlier recognition of disease onset and better prevention strategies. Additionally, metabolic phenotyping, at the molecular species level, contributes to unravel the roles of metabolites in disease development. In this cross-sectional study, we investigated clinically healthy individuals (n = 116, 65% male, 70.8 ± 8.7 years) and patients with CAD (n = 54, 91% male, 67.0 ± 11.5 years) of the COmPLETE study. We applied a high-coverage quantitative liquid chromatography-mass spectrometry approach to acquire a comprehensive profile of serum acylcarnitines, free carnitine and branched-chain amino acids (BCAAs), as markers of mitochondrial health and energy homeostasis. Multivariable linear regression analyses, adjusted for confounders, were conducted to assess associations between metabolites and CAD phenotype. In total, 20 short-, medium- and long-chain acylcarnitine species, along with L-carnitine, valine and isoleucine were found to be significantly (adjusted p ≤ 0.05) and positively associated with CAD. For 17 acylcarnitine species, associations became stronger as the number of affected coronary arteries increased. This implies that circulating acylcarnitine levels reflect CAD severity and might play a role in future patients' stratification strategies. Altogether, CAD is characterized by elevated serum acylcarnitine and BCAA levels, which indicates mitochondrial imbalance between fatty acid and glucose oxidation.

Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Emerging role of metabolomics in protein conformational disorders

Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.

Integration of omics data to unravel root microbiome recruitment

The plant microbiome plays an essential role in supporting plant growth and health, but plant molecular mechanisms underlying its recruitment are still unclear. Multi-omics data integration methods can be used to unravel new signalling relationships. Here, we review the effects of plant genetics and root exudates on root microbiome recruitment, and discuss methodological advances in data integration approaches that can help us to better understand and optimise the crop-microbiome interaction for a more sustainable agriculture.

Building infrastructure at the National Cancer Institute to support metabolomic analyses in epidemiological studies

In recent years, metabolomic analyses have been increasingly performed in studies with an epidemiological design. Since 2012, the National Cancer Institute has recognized the importance of supporting these efforts by strategically building resources and infrastructure to move this area forward. This review outlines those efforts, including building infrastructure, leveraging existing resources, establishing the COnsortium of METabolomics Studies (COMETS), and improving rigor and reproducibility.

Metabolic View on Human Healthspan: A Lipidome-Wide Association Study

As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Gaining a deeper understanding of social determinants of preterm birth by integrating multi-omics data

In the US, high rates of preterm birth (PTB) and profound Black-White disparities in PTB have persisted for decades. This review focuses on the role of social determinants of health (SDH), with an emphasis on maternal stress, in PTB disparity and biological embedding. It covers: (1) PTB disparity in US Black women and possible contributors; (2) the role of SDH, highlighting maternal stress, in the persistent racial disparity of PTB; (3) epigenetics at the interface between genes and environment; (4) the role of the genome in modifying maternal stress-PTB associations; (5) recent advances in multi-omics studies of PTB; and (6) future perspectives on integrating multi-omics with SDH to elucidate the Black-White disparity in PTB. Available studies have indicated that neither environmental exposures nor genetics alone can adequately explain the Black-White PTB disparity. Preliminary yet promising findings of epigenetic and gene-environment interaction studies underscore the value of integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women. In an era of rapid advancements in biomedical sciences and technologies and a growing number of prospective birth cohort studies, we have unprecedented opportunities to advance this field and finally address the long history of health disparities in PTB. IMPACT: This review provides an overview of social determinants of health (SDH) with a focus on maternal stress and its role on Black-White disparity in preterm birth (PTB). It summarizes the available literature on the interplay of maternal stress with key biological layers (e.g., individual genome and epigenome in response to environmental stressors) and significant knowledge gaps. It offers perspectives that such knowledge may provide deeper insight into how SDH affects PTB and why some women are more vulnerable than others and underscores the critical need for integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women.

The CONSTANCES Cohort Biobank: An Open Tool for Research in Epidemiology and Prevention of Diseases

“General-purpose cohorts” in epidemiology and public health are designed to cover a broad scope of determinants and outcomes, in order to answer several research questions, including those not defined at study inception. In this context, the general objective of the CONSTANCES project is to set up a large population-based cohort that will contribute to the development of epidemiological research by hosting ancillary projects on a wide range of scientific domains, and to provide public health information. CONSTANCES was designed as a randomly selected sample of French adults aged 18–69 years at study inception; 202,045 subjects were included over an 8-year period. At inclusion, the selected participants are invited to attend one of the 24 participating Health Prevention Centers (HPCs) for a comprehensive health examination. The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an HPC. Procedures have been developed to use the national healthcare databases to allow identification and validation of diseases over the follow-up. The biological collection (serum, lithium heparinized plasma, EDTA plasma, urine and buffy coat) began gradually in June 2018. At the end of the inclusions, specimens from 83,000 donors will have been collected. Specimens are collected according to a standardized protocol, identical in all recruitment centers. All operations relating to bio-banking have been entrusted by Inserm to the Integrated Biobank of Luxembourg (IBBL). A quality management system has been put in place. Particular attention has been paid to the traceability of all operations. The nature of the biological samples stored has been deliberately limited due to the economic and organizational constraints of the inclusion centers. Some research works may require specific collection conditions, and can be developed on request for a limited number of subjects and in specially trained centers. The biological specimens that are collected will allow for a large spectrum of biomarkers studies and genetic and epigenetic markers through candidate or agnostic approaches. By linking the extensive data on personal, lifestyle, environmental, occupational and social factors with the biomarker data, the CONSTANCES cohort offers the opportunity to study the interplays between these factors using an integrative approach and state-of-the-art methods.

State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Advances in Cardiovascular Biomarker Discovery

Cardiovascular diseases are the leading causes of mortality worldwide. Among them, hypertension and its pathological complications pose a major risk for the development of other cardiovascular diseases, including heart failure and stroke. Identifying novel and early stage biomarkers of hypertension and other cardiovascular diseases is of paramount importance in predicting and preventing the major morbidity and mortality associated with these diseases. Biomarkers of such diseases or predisposition to their development are identified by changes in a specific indicator’s expression between healthy individuals and patients. These include changes in protein and microRNA (miRNA) levels. Protein profiling using mass spectrometry and miRNA screening utilizing microarray and sequencing have facilitated the discovery of proteins and miRNA as biomarker candidates. In this review, we summarized some of the different, promising early stage protein and miRNA biomarker candidates as well as the currently used biomarkers for hypertension and other cardiovascular diseases. Although a number of promising markers have been identified, it is unlikely that a single biomarker will unambiguously aid in the classification of these diseases. A multi-marker panel-strategy appears useful and promising for classifying and refining risk stratification among patients with cardiovascular disease.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

An Integrative Approach to Assessing Diet-Cancer Relationships

The relationship between diet and cancer is often viewed with skepticism by the public and health professionals, despite a considerable body of evidence and general consistency in recommendations over the past decades. A systems biology approach which integrates 'omics' data including metabolomics, genetics, metagenomics, transcriptomics and proteomics holds promise for developing a better understanding of how diet affects cancer and for improving the assessment of diet through biomarker discovery thereby renewing confidence in diet-cancer links. This review discusses the application of multi-omics approaches to studies of diet and cancer. Considerations and challenges that need to be addressed to facilitate the investigation of diet-cancer relationships with multi-omic approaches are also discussed.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Emerging Biomarkers for Prediction and Early Diagnosis of Necrotizing Enterocolitis in the Era of Metabolomics and Proteomics

Necrotizing Enterocolitis (NEC) is a catastrophic disease affecting predominantly premature infants and is characterized by high mortality and serious long-term consequences. Traditionally, diagnosis of NEC is based on clinical and radiological findings, which, however, are non-specific for NEC, thus confusing differential diagnosis of other conditions such as neonatal sepsis and spontaneous intestinal perforation. In addition, by the time clinical and radiological findings become apparent, NEC has already progressed to an advanced stage. During the last three decades, a lot of research has focused on the discovery of biomarkers, which could accurately predict and make an early diagnosis of NEC. Biomarkers used thus far in clinical practice include acute phase proteins, inflammation mediators, and molecules involved in the immune response. However, none has been proven accurate enough to predict and make an early diagnosis of NEC or discriminate clinical from surgical NEC or other non-NEC gastrointestinal diseases. Complexity of mechanisms involved in NEC pathogenesis, which remains largely poorly elucidated, could partly explain the unsatisfactory diagnostic performance of the existing NEC biomarkers. More recently applied technics can provide important insight into the pathophysiological mechanisms underlying NEC but can also aid the detection of potentially predictive, early diagnostic, and prognostic biomarkers. Progress in omics technology has allowed for the simultaneous measurement of a large number of proteins, metabolic products, lipids, and genes, using serum/plasma, urine, feces, tissues, and other biological specimens. This review is an update of current data on emerging NEC biomarkers detected using proteomics and metabolomics, further discussing limitations and future perspectives in prediction and early diagnosis of NEC.

The nuts and bolts of omics for the clinical allergist

OBJECTIVE

Omics, aka multi-omics, is an emerging area of research that is advancing the use of personalized medicine in clinical practice and is therefore relevant for the practicing allergist.

DATA SOURCES

We performed a literature search of a selection of scientific findings in omics and allergy, including variants that may be important to allergy outcomes in the genome, transcriptome, metabolome, microbiome, epigenome, and exposome, among others.

STUDY SELECTIONS

Basic science papers and review articles.

RESULTS

The use of multi-omic data in clinical practice is changing how clinicians treat their patients whereby more personalized approaches are becoming standard in medical practice and has the potential to transform the treatment of allergies.

CONCLUSION

Multi-omic data are relevant and will become increasingly important for the clinical allergist.

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.