Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

From Multimorbidity to Network Medicine in Patients with Rheumatic Diseases

The transition from a comorbidity-based to a multimorbidity-focused to ultimately a network medicine approach in people with rheumatic diseases might mark a significant shift in how we understand and manage these complex conditions. Multimorbidity expands on the concept of comorbidity by encompassing the presence of multiple diseases, which results in further individual and societal impacts. This approach, while valuable, often leads to fragmented care focused on individual diseases rather than the patient as a whole.Network medicine, on the other hand, offers a more integrated perspective. It is an emerging concept that leverages the understanding of biologic networks and their interactions within the human body to gain insights into disease mechanisms. In the context of rheumatic diseases, network medicine involves examining how different diseases interconnect and influence each other through shared pathways, genetic factors, and molecular mechanisms.This paradigm shift allows for a more holistic understanding in how we manage rheumatic diseases. For instance, rheumatic diseases such as rheumatoid arthritis and systemic lupus erythematosus are not just a collection of symptoms affecting various organs but are also interconnected through underlying systemic inflammatory processes, immune system dysregulation, and genetic predispositions.

Photoreceptor metabolic window unveils eye-body interactions

Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.

Atlas of the plasma proteome in health and disease in 53,026 adults

Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.

Genetically Confirmed Optimal Causal Association of Cerebrospinal Fluid Metabolites With Hemorrhagic Stroke

Hemorrhagic stroke (HS) mainly includes intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH), both of which seriously affect the patient's prognosis. Cerebrospinal fluid (CSF) metabolites and HS showed a link in observational studies. However, the causal association between them is not clear. We aimed to establish the optimal causality of CSF metabolites with HS. Mendelian randomization (MR) was employed to identify associations between CSF metabolites and different sources of HS. Univariable MR and false discovery rates (FDR) were used to identify initial causal associations. Linkage disequilibrium score regression determined genetic correlations. Multiple sensitive analyses ensured the reliability of the results. Multivariable MR and MR Bayesian Model Averaging were used to identify the optimal causal associations. The combined effects of metabolites and HS were assessed by meta-analyses. Pathway analyses were performed to identify potential pathways of action. Reverse MR was also conducted to identify reverse causal associations. Finally, Corresponding blood metabolites were used to explore the multiple roles of metabolites. We identified 20 CSF metabolites and six metabolic pathways associated with ICH; 15 CSF metabolites and three metabolic pathways associated with SAH. Nineteen and seven metabolites were causally associated with deep and lobar ICH, respectively. CSF levels of mannose (OR 0.63; 95% CI 0.45-0.88; Pcombined = 0.0059) and N-acetyltaurine (OR 0.68; 95% CI 0.47-0.98; Pcombined = 0.0395) may serve as the optimal exposures for ICH and SAH, respectively. Additionally, CSF ascorbic acid 3-sulfate levels significantly decrease the risk of deep ICH (OR 0.79; 95% CI 0.66-0.94; p = 0.0065; PFDR = 0.091). Supplemental analysis of blood metabolites suggested multiple roles for CSF and blood N-formylanthranilic acid and hippurate. There are significant causal associations between CSF metabolites and HS, which provides a further rationale for the prevention and monitoring of ICH and SAH.

miRNAome-metabolome wide association study reveals effects of miRNA regulation in eosinophilia and airflow obstruction in childhood asthma

BACKGROUND

There are important inter-relationships between miRNAs and metabolites: alterations in miRNA expression can be induced by various metabolic stimuli, and miRNAs play a regulatory role in numerous cellular processes, impacting metabolism. While both specific miRNAs and metabolites have been identified for their role in childhood asthma, there has been no global assessment of the combined effect of miRNAs and the metabolome in childhood asthma.

METHODS

We performed miRNAome-metabolome-wide association studies ('miR-metabo-WAS') in two childhood cohorts of asthma to evaluate the contemporaneous and persistent miRNA-metabolite associations: 1) Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (N = 1121); 2) the Childhood Asthma Management Program (CAMP) (NBaseline = 312 and NEnd of trial = 454). We conducted a meta-analysis of the two cohorts to identify common contemporaneous associations between CAMP and GACRS (false-discovery rate (FDR) = 0.05). We assessed persistent miRNA-metabolome associations using baseline miRNAs and metabolomic profiling in CAMP at the end of the trial. The relation between miRNAs, metabolites and clinical phenotypes, including airway hyper-responsiveness (AHR), peripheral blood eosinophilia, and airflow obstruction, were then assessed via. Mediation analysis with 1000 bootstraps at an FDR significance level of 0.05.

FINDINGS

The meta-analysis yielded a total of 369 significant contemporaneous associations, involving 133 miRNAs and 60 metabolites. We identified 13 central hub metabolites (taurine, 12,13-diHOME, sebacate, 9-cis-retinoic acid, azelate, asparagine, C5:1 carnitine, cortisol, 3-methyladipate, inosine, NMMA, glycine, and Pyroglutamic acid) and four hub miRNAs (hsa-miR-186-5p, hsa-miR-143-3p, hsa-miR-192-5p, and hsa-miR-223-3p). Nine of these associations, between eight miRNAs and eight metabolites, were persistent in CAMP from baseline to the end of trial. Finally, five central hub metabolites (9-cis-retinoic acid, taurine, sebacate, azelate, and 12,13-diHOME) were identified as primary mediators in over 100 significant indirect miRNA-metabolite associations, with a collective influence on peripheral blood eosinophilia, AHR, and airflow obstruction.

INTERPRETATION

The robust association between miRNAs and metabolites, along with the substantial indirect impact of miRNAs via 5 hub metabolites on multiple clinical asthma metrics, suggests important integrated effects of miRNAs and metabolites on asthma. These findings imply that the indirect regulation of metabolism and cellular functions by miRNA influences Th2 inflammation, AHR, and airflow obstruction in childhood asthma.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Identifying proteins and pathways associated with multimorbidity in 53,026 adults

BACKGROUND AND AIMS

Multimorbidity, the coexistence of multiple chronic diseases, is a rapidly expanding global health challenge, carrying profound implications for patients, caregivers, healthcare systems, and society. Investigating the determinants and drivers underlying multiple chronic diseases is a priority for disease management and prevention.

METHOD

This prospective cohort study analyzed data from the 53,026 participants in the UK Biobank from baseline (2006 to 2010) across 13.3 years of follow-up. Using Cox proportional hazards regression model, we characterized shared and unique associations across 38 incident outcomes (31 chronic diseases, 6 system mortality and all-cause mortality). Furthermore, ordinal regression models were used to assess the association between protein levels and multimorbidity (0-1, 2, 3-4, or ≥ 5 chronic diseases). Functional and tissue enrichment analysis were employed for multimorbidity-associated proteins. The upstream regulators of above proteins were identified.

RESULTS

We demonstrated 972 (33.3 %) proteins were shared across at least two incident chronic diseases after Bonferroni correction (P < 3.42 × 10-7, 93.3 % of those had consistent effects directions), while 345 (11.8 %) proteins were uniquely linked to a single chronic disease. Remarkably, GDF15, PLAUR, WFDC2 and AREG were positively associated with 20-24 incident chronic diseases (hazards ratios: 1.21-3.77) and showed strong associations with multimorbidity (odds ratios: 1.33-1.89). We further identified that protein levels are explained by common risk factors, especially renal function, liver function, inflammation, and obesity, providing potential intervention targets. Pathway analysis has underscored the pivotal role of the immune response, with the top three transcription factors associated with proteomics being NFKB1, JUN and RELA.

CONCLUSIONS

Our results enhance the understanding of the biological basis underlying multimorbidity, offering biomarkers for disease identification and novel targets for therapeutic intervention.

IgD-CD38br lymphocyte affect myocardial infarction by regulating the glycerol to palmitoylcarnitine (C16) ratio: A Mendelian randomization study

Myocardial infarction, a type of coronary artery disease, results from various factors such as genetic predisposition, lifestyle choices, and immune system regulation. The exact causal links between immune cells, plasma metabolites, and myocardial infarction are currently unclear. Therefore, our study employed the Mendelian randomization approach to explore these potential causal relationships. To investigate the impact of immune cells on the risk of myocardial infarction mediated by alterations in plasma metabolite levels, we employed the Mendelian randomization (MR) framework. Our analysis utilized 5 distinct MR techniques (inverse variance weighted [IVW], weighted median, MR-Egger, simple mode, and weighted mode) to evaluate causal relationships among 731 immune cell types, 1400 plasma metabolites, and myocardial infarction. Genetic instruments for immune cells and metabolites were identified using data from a meta-analysis of genome-wide association studies. Furthermore, sensitivity analyses were performed to verify the robustness of our results, identify potential heterogeneity, and examine possible pleiotropic effects. IVW results indicated that IgD-CD38br lymphocytes was a risk factor for myocardial infarction, whereas IgD-CD38br lymphocytes also acted as a protective factor against myocardial infarction. Additionally, the glycerol to palmitoylcarnitine (C16) ratio was identified as a protective factor for myocardial infarction. IgD-CD38br lymphocytes could exert a detrimental effect on myocardial infarction by negatively regulating the glycerol to palmitoylcarnitine (C16) ratio, with the mediation effect ratio being 9%. IgD-CD38br lymphocytes potentially increase the risk of myocardial infarction by negatively affecting the glycerol to palmitoylcarnitine (C16) ratio. This finding opens avenues for developing early diagnostic tools and targeted therapies for myocardial infarction.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

BACKGROUND

Diagnosing complex illnesses like Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is complicated due to the diverse symptomology and presence of comorbid conditions. ME/CFS patients often present with multiple health issues, therefore, incorporating comorbidities into research can provide a more accurate understanding of the condition's symptomatology and severity, to better reflect real-life patient experiences.

METHODS

We performed association studies and machine learning on 1194 ME/CFS individuals with blood plasma nuclear magnetic resonance (NMR) metabolomics profiles, and seven exclusive comorbid cohorts: hypertension (n = 13,559), depression (n = 2522), asthma (n = 6406), irritable bowel syndrome (n = 859), hay fever (n = 3025), hypothyroidism (n = 1226), migraine (n = 1551) and a non-diseased control group (n = 53,009).

RESULTS

We present a lipoprotein perspective on ME/CFS pathophysiology, highlighting gender-specific differences and identifying overlapping associations with comorbid conditions, specifically surface lipids, and ketone bodies from 168 significant individual biomarker associations. Additionally, we searched for, trained, and optimised a machine learning algorithm, resulting in a predictive model using 19 baseline characteristics and nine NMR biomarkers which could identify ME/CFS with an AUC of 0.83 and recall of 0.70. A multi-variable score was subsequently derived from the same 28 features, which exhibited ~2.5 times greater association than the top individual biomarker.

CONCLUSIONS

This study provides an end-to-end analytical workflow that explores the potential clinical utility that association scores may have for ME/CFS and other difficult to diagnose conditions.

Metabolomic signature of retinal ageing, polygenetic susceptibility, and major health outcomes

BACKGROUND/AIMS

To identify the metabolic underpinnings of retinal aging and examine how it is related to mortality and morbidity of common diseases.

METHODS

The retinal age gap has been established as essential aging indicator for mortality and systemic health. We applied neural network to train the retinal age gap among the participants in UK Biobank and used nuclear magnetic resonance (NMR) to profile plasma metabolites. The metabolomic signature of retinal ageing (MSRA) was identified using an elastic network model. Multivariable Cox regressions were used to assess associations between the signature with 12 serious health conditions. The participants in Guangzhou Diabetic Eye Study (GDES) cohort were analyzed for validation.

RESULTS

This study included 110 722 participants (mean age 56.5±8.1 years at baseline, 53.8% female), and 28 plasma metabolites associated with retinal ageing were identified. The MSRA revealed significant correlations with each 12 serious health conditions beyond traditional risk factors and genetic predispositions. Each SD increase in MSRA was linked to a 24%-76% higher risk of mortality, cardiovascular diseases, dementia and diabetes mellitus. MSRA showed dose-response relationships with risks of these diseases, with seven showing non-linear and five showing linear increases. Validation in the GDES further established the relation between retinal ageing-related metabolites and increased risks of cardiovascular and chronic kidney diseases (all p<0.05).

CONCLUSIONS

The metabolic connections between ocular and systemic health offer a novel tool for identifying individuals at high risk of premature ageing, promoting a more holistic view of human health.

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using 'omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.

Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study

BACKGROUND

Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes.

METHODS

Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies.

RESULTS

After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression.

CONCLUSIONS

The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Metabolite and protein associations with general health in the population-based CHRIS study

Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people's health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.

The association of a low-inflammatory diet with the trajectory of multimorbidity: a large community-based longitudinal study

BACKGROUND

A proinflammatory diet has been associated with a risk of individual chronic diseases, however, evidence on the association between inflammatory dietary patterns and the trajectory of chronic disease multimorbidity is sparse.

OBJECTIVES

We aimed to investigate the associations of a low-inflammatory diet with the multimorbidity trajectory.

METHODS

Within the UK Biobank, 102,424 chronic disease-free participants (mean age 54.7 ± 7.9 y, 54.8% female) were followed up to detect multimorbidity trajectory (annual change in the number of 59 chronic diseases). Baseline inflammatory diet index (IDI) and empirical dietary inflammatory pattern (EDIP) were separately calculated from the weighted sum of 32 posteriori-derived (15 anti-inflammatory) and 18 prior-defined (9 anti-inflammatory) food groups, and tertiled as low-, moderate-, and high-inflammatory diet. Data were analyzed using linear mixed effects model, Cox model, and Laplace regression with adjustment for potential confounders.

RESULTS

During the follow-up (median 10.23 y), 15,672 and 35,801 participants developed 1 and 2+ chronic conditions, respectively. Adherence to a low-inflammatory diet was associated with decreased multimorbidity risk (hazard ratio [HRIDI] = 0.84, 95% confidence interval [CI]: 0.81, 0.86; HREDIP = 0.91, 95% CI: 0.89, 0.94) and a slower multimorbidity accumulation (βIDI = -0.033, 95% CI: -0.036, -0.029; βEDIP = -0.006, 95% CI: -0.010, -0.003) compared with a high-inflammatory diet, especially in participants aged > 60 y (βIDI = -0.051, 95% CI: -0.059, -0.042; βEDIP = -0.020, 95% CI: -0.029, -0.012; both P-interactions < 0.05). The 50th percentile difference (95% CI) of chronic disease-free survival time was prolonged by 0.81 (0.64, 0.97) and 0.49 (0.34, 0.64) y for participants with a low IDI and EDIP, respectively. Higher IDI and EDIP were associated with the development of 4 and 3 multimorbidity clusters (especially for cardiometabolic diseases), respectively.

CONCLUSIONS

A low-inflammatory diet is associated with a lower risk and slower accumulation of multimorbidity (especially in participants aged > 60 y). A low-inflammatory diet may prolong chronic disease-free survival time.

Metabolomics revealed pharmacodynamic effects of aspirin and indobufen in patients after percutaneous transluminal angioplasty surgery

Introduction

Aspirin and indobufen are commonly used therapeutic drugs for the prevention of vascular restenosis (VR) after percutaneous transluminal angioplasty surgery. They both exhibited antiplatelet effects but molecular mechanisms underlying metabolic changes induced by them remain unclear.

Methods

In this study, we collected plasma samples from patients on aspirin medication (n = 5), patients on indobufen medication, patients with no medication after PTA, and healthy controls (CKs) (n = 5). Our investigation aimed to reveal the metabolic processes in patients during vascular restenosis and its amelioration through drug therapy using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Results

Our data showed significant alterations in amino acid and choline metabolism in patients without medication after PTA. Aspirin and indobufen were able to regulate these metabolic pathways to alleviate VR symptoms. We identified several characteristic amino acids, including pro-leu, L-citrulline, his-glu, and L-glutamate, as important biomarkers for VR assessment in patients without medication after PTA. A total of 17 and 4 metabolites involved in arginine and phenylalanine metabolism were specifically induced by aspirin and indobufen, respectively. Their expression levels were significantly regulated by aspirin or indobufen, nearly reaching normal levels.

Discussion

Taken together, our identification of metabolites involved in metabolic changes affected by aspirin and indobufen medication enhances the understanding of VR pathology after PTA. This may help identify early diagnostic biomarkers and therapeutic targets.

Comorbidities confound metabolomics studies of human disease

The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

Causal relationship between metabolites and embolic stroke: based on Mendelian randomization and metabolomics

Purpose

This research employed Mendelian randomization (MR) methods to explore whether metabolites are causally associated with embolic stroke of undetermined source (ESUS).

Methods

Genome-Wide Association Study (GWAS) data regarding metabolites and ESUS were downloaded from the database. Metabolites were employed as exposure factors, ESUS served as the outcome variable, and single nucleotide polymorphisms (SNPs) exhibiting significant association with ESUS were chosen as instrumental variables. The causal association between exposure factor metabolites and the outcome variable ESUS was assessed using two methods: MR-Egger regression and inverse variance-weighted (IVW) analysis.

Results

A causal relationship was observed between X-11593--O-methylascorbate* and ESUS, indicating a protective factor. Moreover, a causal relationship was identified between cholesterol esters in large very-low-density lipoprotein (VLDL), cholesterol esters in medium low-density lipoprotein (LDL), concentration of medium LDL particles, phospholipids in medium LDL, phenylalanine, total cholesterol in small LDL, total lipids in medium LDL and ESUS, representing risk factor. Funnel plots exhibited a symmetrical distribution of SNPs, while pleiotropic tests (p > 0.05) and leave-one-out tests indicated that the results were relatively stable.

Conclusion

Metabolites are causally associated with ESUS. LDL and VLDL-related metabolites are identified as risk factors for ESUS.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Metabolite, immunocyte phenotype, and lymphoma: a Mendelian randomization study

Background

Recent studies have confirmed that metabolites and immunocyte phenotype may be associated with the risk of lymphoma. However, the bidirectional causality between metabolites, immunocyte phenotype, disease risk, and whether immunity is an intermediate mediator between metabolism and lymphoma causality is still unclear.

Objective

To elucidate the causal relationship between metabolites, immune cell phenotypes, and lymphomas, we used two-sample Mendelian randomization (MR) and two-step MR analysis.

Methods

Applying large-scale genome-wide association studies (GWAS) pooled data, we selected 1400 metabolites and 731 immunocyte phenotypes with eight lymphoma subtypes for two-sample bi-directional MR analysis. In addition, we used two-step MR to quantify the proportion of metabolite effects on lymphomas mediated by immunocyte phenotype.

Results

This study yielded a bidirectional causal relationship between 17 metabolites and lymphoma and a bidirectional causal relationship between 12 immunocyte phenotypes and lymphoma. In addition, we found causal associations between metabolites and lymphomas, three groups of which were mediated by immunocyte phenotypes. Among them, CD27 on plasmablast/plasma cell (PB/PC) was a mediator of the positive association of arginine to glutamate ratio with chronic lymphocytic leukemia, with a mediator ratio of 14.60% (95% CI=1.29-28.00%, P=3.17 × 10-2). Natural killer (NK) cells as a percentage of all lymphocytes(NK %lymphocyte) was a mediator of the negative association of X-18922(unknown metabolite) levels with diffuse large B-cell lymphoma, with a mediation proportion of -8.940% (95% CI=-0.063-(-17.800) %, P=4.84 × 10-2). CD25 on IgD- CD24- B cell was the mediator of the positive association between X-24531(unknown metabolite) levels and diffuse large B-cell lymphoma, with a mediation proportion of 13.200% (95% CI=-0.156-26.200%, P=4.73 × 10-2).

Conclusion

In the present study, we identified a causal relationship between metabolites and lymphoma, in which immunocyte phenotypes as mediators are involved in only a minor part. The mediators by which most metabolites affect the risk of lymphoma development remain unclear and require further exploration in the future.

Cross omics risk scores of inflammation markers are associated with all-cause mortality: The Canadian Longitudinal Study on Aging

Inflammation is a critical component of chronic diseases, aging progression, and lifespan. Omics signatures may characterize inflammation status beyond blood biomarkers. We leveraged genetics (Polygenic-Risk-Score; PRS), metabolomics (Metabolomic-Risk-Score; MRS), and epigenetics (Epigenetic-Risk-Score; ERS) to build multi-omics-multi-marker risk scores for inflammation status represented by the level of circulating C-reactive protein (CRP), interleukin 6 (IL6), and tumor necrosis factor alpha (TNFa). We found that multi-omics risk-scores generally outperformed single-omics risk scores in prediction of all-cause mortality in the Canadian Longitudinal Study on Aging. Compared with circulating inflammation biomarkers, some multi-omics risk scores had a higher HR for all cause-mortality when including both score and circulating IL6 in the same model (1-SD IL6 MRS-ERS: HR=1.77 [1.15-2.72] vs. 1-SD circulating IL6 HR=1.11 [0.75,1.66]; 1-SD IL6 PRS-MRS: HR=1.32 [1.21,1.45] vs. 1-SD circulating IL6 HR=1.31 [1.12, 1.53]; 1-SD PRS-MRS-ERS: HR=1.62 [1.04, 2.53] vs. 1-SD circulating IL6: HR=1.16 [0.77, 1.74]). In the Nurses' Health Study (NHS), NHS II, and Health Professional Follow-up Study with available omics, 1-SD of IL6 PRS and 1-SD IL6 PRS-MRS had HR=1.13 [1.00,1.27] and HR=1.13 [1.01,1.27], among individuals >65years without mutual adjustment of the score and circulating IL6. Our study demonstrated that some multi-omics scores for inflammation markers may characterize important inflammation burden for an individual beyond those represented by blood biomarkers and improve our prediction capability for aging process and lifespan.

Genetically predicted metabolites mediate the association between lipidome and malignant melanoma of skin

Objective

To investigate the causal relationship between lipidome and malignant melanoma of skin (MMOS), while identifying and quantifying the role of metabolites as potential mediators.

Methods

A two-sample Mendelian randomization (MR) analysis of lipid species (n=7174) and MMOS was performed using pooled data from genome-wide association studies (GWAS). In addition, we quantified the proportion of metabolite-mediated lipidome effects on MMOS by two-step MR.

Results

This study identified potential causal relationships between 11 lipids and MMOS, and 40 metabolites and MMOS, respectively. Phosphatidylethanolamine (18:0_18:2) levels mined from 179 lipids by MR Analysis increased the risk of MMOS (OR: 1.962; 95%CI:1.298,2.964; P=0.001). There is no strong evidence for a relationship between genetically predicted MMOS and phosphatidylethanolamine (18:0_18:2) levels (P=0.628). The proportion of gene predictions for phosphatidylethanolamine (18:0_18:2) levels mediated by 1-stearoyl-(glycosylphosphatidylinositol) GPI (18:0) levels was 12.40%.

Conclusion

This study identifies 1-stearoyl-GPI (18:0) levels as a potential mediator that may mediate the causal relationship between phosphatidylethanolamine (18:0_18:2) levels and MMOS, This provides direction for the investigation of MMOS, but further research of other possible potential mediators is still needed.

Testing for a causal role of thyroid hormone measurements within the normal range on human metabolism and diseases: a systematic Mendelian randomization

BACKGROUND

Variation in thyroid function parameters within the normal range has been observationally associated with adverse health outcomes. Whether those associations reflect causal effects is largely unknown.

METHODS

We systematically tested associations between genetic differences in thyrotropin (TSH) and free thyroxine (FT4) within the normal range and more than 1100 diseases and more than 6000 molecular traits (metabolites and proteins) in three large population-based cohorts. This was performed by combining individual and summary level genetic data and using polygenic scores and Mendelian randomization (MR) methods. We performed a phenome-wide MR study in the OpenGWAS database covering thousands of complex phenotypes and diseases.

FINDINGS

Genetically predicted TSH or FT4 levels within the normal range were predominately associated with thyroid-related outcomes, like goitre. The few extra-thyroidal outcomes that were found to be associated with genetic liability towards high but normal TSH levels included atrial fibrillation (odds ratio = 0.92, p-value = 2.13 × 10-3), thyroid cancer (odds ratio = 0.57, p-value = 2.97 × 10-4), and specific biomarkers, such as sex hormone binding globulin (β = -0.046, p-value = 1.33 × 10-6) and total cholesterol (β = 0.027, p-value = 5.80 × 10-3).

INTERPRETATION

In contrast to previous studies that have described the association with thyroid hormone levels and disease outcomes, our genetic approach finds little evidence of an association between genetic differences in thyroid function within the normal range and non-thyroidal phenotypes. The association described in previous studies may be explained by reverse causation and confounding.

FUNDING

This research was funded by the Swiss National Science Foundation (P1BEP3_200041). The Fenland study (DOI 10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1, MC_PC_13046 and MC_UU_00006/1). The EPIC-Norfolk study (DOI 10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1, MC-UU_12015/1, MC_PC_13048 and MC_UU_00006/1).

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

Lipid metabolites are associated with the risk of osteoporotic fractures

We conducted this cross-sectional study to investigate the independent associations between lipid metabolites and osteoporotic fractures among participants aged 40-69 years from the UK Biobank. Serum lipid, lipoprotein levels and nuclear magnetic resonance (NMR) based metabolic biomarkers were measured at the baseline. We conducted multivariable logistic analyses to investigate potential independent associations between concentrations of lipid metabolites and osteoporotic fractures in both men and women. The odds ratios (ORs) for lipid metabolites were calculated based on their lowest tertile. Over a median follow-up period of 15 years, a total of 978 men and 4515 women were diagnosed with osteoporosis, whereas 138 men and 327 women encountered incident fractures. Statistically significant disparities were identified in NMR-based metabolic biomarkers among men and women with incident fractures compared to those without. Out of the 144 distinct lipid metabolites known, 35 exhibited significant associations with incident fractures in patients diagnosed with osteoporosis. Following the adjustment for confounding factors, degree of unsaturation (p = 0.0066) and docosahexaenoic acids (p = 0.0011) in male patients increased the risk of incident fractures. And high level of different metabolites of HDL (p = 0.0153), 3-Hydroxybutyrate (p = 0.0012) and Sphingomyelins (p = 0.0036) decreased the risk of incident fractures in female patients. This outcome indicates that these identified lipid metabolites may potentially have unique roles in independently contributing to the occurrence of osteoporotic fractures.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Comprehensive Clinical and Genetic Analyses of Circulating Bile Acids and Their Associations With Diabetes and Its Indices

Bile acids (BAs) are cholesterol-derived compounds that regulate glucose, lipid, and energy metabolism. Despite their significance in glucose homeostasis, the association between specific BA molecular species and their synthetic pathways with diabetes is unclear. Here, we used a recently validated, stable-isotope dilution, high-performance liquid chromatography with tandem mass spectrometry method to quantify a panel of BAs in fasting plasma from 2,145 study participants and explored structural and genetic determinants of BAs linked to diabetes, insulin resistance, and obesity. Multiple 12α-hydroxylated BAs were associated with diabetes (adjusted odds ratio [aOR] range, 1.3-1.9; P < 0.05 for all) and insulin resistance (aOR range, 1.3-2.2; P < 0.05 for all). Conversely, multiple 6α-hydroxylated BAs and isolithocholic acid (iso-LCA) were inversely associated with diabetes and obesity (aOR range, 0.3-0.9; P < 0.05 for all). Genome-wide association studies revealed multiple genome-wide significant loci linked with 9 of the 14 diabetes-associated BAs, including a locus for iso-LCA (rs11866815). Mendelian randomization analyses showed genetically elevated deoxycholic acid levels were causally associated with higher BMI, and iso-LCA levels were causally associated with reduced BMI and diabetes risk. In conclusion, comprehensive, large-scale, quantitative mass spectrometry and genetics analyses show circulating levels of multiple structurally specific BAs, especially DCA and iso-LCA, are clinically associated with and genetically linked to obesity and diabetes.

ARTICLE HIGHLIGHTS

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Mediated Mendelian randomization analysis to determine the role of immune cells in regulating the effects of plasma metabolites on childhood asthma

Childhood asthma is a chronic inflammatory disease of the airways, the pathogenesis of which involves multiple factors including genetic predisposition, environmental exposure, and immune system regulation. To date, the causal relationships between immune cells, plasma metabolites, and childhood asthma remain undetermined. Therefore, we aim to utilize the Mendelian randomization approach to assess the causal relationships among immune cells, plasma metabolites, and childhood asthma. This study employed the Mendelian randomization approach to investigate how immune cells influenced the risk of childhood asthma by modulating the levels of plasma metabolites. Five Mendelian randomization methods-inverse variance weighted, weighted median, Mendelian randomization-Egger, simple mode, and weighted mode-were utilized to explore the causal relationships among 731 types of immune cells, 1400 plasma metabolites, and childhood asthma. The instrumental variables for the 731 immune cells and 1400 plasma metabolites were derived from a genome-wide association study meta-analysis. Additionally, sensitivity analyses were conducted to examine the robustness of the results, potential heterogeneity, and pleiotropy. The inverse variance weighted results indicated that HLA DR on dendritic cells (DC) is a risk factor for childhood asthma (OR: 1.08, 95% CI: 1.02-1.14). In contrast, HLA DR on DC acts as a protective factor against elevated catechol glucuronide levels (OR: 0.94, 95% CI: 0.91-0.98), while catechol glucuronide levels themselves serve as a protective factor for childhood asthma (OR: 0.73, 95% CI: 0.60-0.89). Thus, HLA DR on DC can exert a detrimental effect on childhood asthma through the negative regulation of catechol glucuronide levels. The mediating effect was 0.018, accounting for a mediation effect proportion of 23.4%. This study found that HLA DR on DC can exert a risk effect on childhood asthma through the negative regulation of catechol glucuronide levels, providing new strategies for the prevention and treatment of childhood asthma and guiding future research and clinical practice.

Gut microbiota, plasma metabolites, and osteoporosis: unraveling links via Mendelian randomization

Objective

Osteoporosis, characterized by reduced bone density and heightened fracture risk, is influenced by genetic and environmental factors. This study investigates the interplay between gut microbiota, plasma metabolomics, and osteoporosis, identifying potential causal relationships mediated by plasma metabolites.

Methods

Utilizing aggregated genome-wide association studies (GWAS) data, a comprehensive two-sample Mendelian Randomization (MR) analysis was performed involving 196 gut microbiota taxa, 1,400 plasma metabolites, and osteoporosis indicators. Causal relationships between gut microbiota, plasma metabolites, and osteoporosis were explored.

Results

The MR analyses revealed ten gut microbiota taxa associated with osteoporosis, with five taxa positively linked to increased risk and five negatively associated. Additionally, 96 plasma metabolites exhibited potential causal relationships with osteoporosis, with 49 showing positive associations and 47 displaying negative associations. Mediation analyses identified six causal pathways connecting gut microbiota to osteoporosis through ten mediating relationships involving seven distinct plasma metabolites, two of which demonstrated suppression effects.

Conclusion

This study provides suggestive evidence of genetic correlations and causal links between gut microbiota, plasma metabolites, and osteoporosis. The findings underscore the complex, multifactorial nature of osteoporosis and suggest the potential of gut microbiota and plasma metabolite profiles as biomarkers or therapeutic targets in the management of osteoporosis.

Effects of diets on risks of cancer and the mediating role of metabolites

Research on the association between dietary adherence and cancer risk is limited, particularly concerning overall cancer risk and its underlying mechanisms. Using the UK Biobank data, we prospectively investigate the associations between adherence to a Mediterranean diet (MedDiet) or a Mediterranean-Dietary Approaches to Stop Hypertension Diet Intervention for Neurodegenerative Delay diet (MINDDiet) and the risk of overall and 22 specific cancers, as well as the mediating effects of metabolites. Here we show significant negative associations of MedDiet and MINDDiet adherence with overall cancer risk. These associations remain robust across 14 and 13 specific cancers, respectively. Then, a sequential analysis, incorporating Cox regression, elastic net and gradient boost models, identify 10 metabolites associated with overall cancer risk. Mediation results indicate that these metabolites play a crucial role in the association between adherence to a MedDiet or a MINDDiet and cancer risk, independently and cumulatively. These findings deepen our understanding of the intricate connections between diet, metabolites, and cancer development.

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability

BACKGROUND

Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking.

METHODS

In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque.

RESULTS

Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment.

CONCLUSIONS

This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

Raman-AFM-fluorescence-guided impact of linoleic and eicosapentaenoic acids on subcellular structure and chemical composition of normal and cancer human colon cells

The regular overconsumption of high-energy food (rich in lipids and sugars) results in elevated nutrient absorption in intestine and consequently excessive accumulation of lipids in many organs e.g.: liver, adipose tissue, muscles. In the long term this can lead to obesity and obesity-associated diseases e.g. type 2 diabetes, non-alcoholic fatty liver disease, cardiovascular disease, inflammatory bowel disease (IBD). In the presented paper based on RI data we have proved that Raman maps can be used successfully for subcellular structures visualization and analysis of fatty acids impact on morphology and chemical composition of human colon single cells - normal and cancer. Based on Raman data we have investigated the changes related to endoplasmic reticulum, mitochondria, lipid droplets and nucleus. Analysis of ratios calculated based on Raman bands typical for proteins (1256, 1656 cm-1), lipids (1304, 1444 cm-1) and nucleic acids (750 cm-1) has confirmed for endoplasmic reticulum the increased activity of this organelle in lipoproteins synthesis upon FAs supplementation; for LDs the changes of desaturation of accumulated lipids with the highest unsaturation level for CaCo-2 cells upon EPA supplementation; for mitochondria the stronger effect of FAs supplementation was observed for CaCo-2 cells confirming the increased activity of this organelle responsible for energy production necessary for tumor development; the weakest impact of FAs supplementation was observed for nucleus for both types of cells and both types of acids. Fluorescence imaging was used for the investigations of changes in LDs/ER morphology. Our measurements have shown the increased area of LDs/ER for CaCo-2 cancer cells, and the strongest effect was noticed for CaCo-2 cells upon EPA supplementation. The increased participation of lipid structures for all types of cells upon FAs supplementation has been confirmed also by AFM studies. The lowest YM values have been observed for CaCo-2 cells including samples treated with FAs.

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.

Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study

BACKGROUND

Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes.

METHODS

We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins.

FINDINGS

Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures.

INTERPRETATION

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings.

FUNDING

Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Associations of neighborhood sociodemographic environment with mortality and circulating metabolites among low-income black and white adults living in the southeastern United States

BACKGROUND

Residing in a disadvantaged neighborhood has been linked to increased mortality. However, the impact of residential segregation and social vulnerability on cause-specific mortality is understudied. Additionally, the circulating metabolic correlates of neighborhood sociodemographic environment remain unexplored. Therefore, we examined multiple neighborhood sociodemographic metrics, i.e., neighborhood deprivation index (NDI), residential segregation index (RSI), and social vulnerability index (SVI), with all-cause and cardiovascular disease (CVD) and cancer-specific mortality and circulating metabolites in the Southern Community Cohort Study (SCCS).

METHODS

The SCCS is a prospective cohort of primarily low-income adults aged 40-79, enrolled from the southeastern United States during 2002-2009. This analysis included self-reported Black/African American or non-Hispanic White participants and excluded those who died or were lost to follow-up ≤ 1 year. Untargeted metabolite profiling was performed using baseline plasma samples in a subset of SCCS participants.

RESULTS

Among 79,631 participants, 23,356 deaths (7214 from CVD and 5394 from cancer) were documented over a median 15-year follow-up. Higher NDI, RSI, and SVI were associated with increased all-cause, CVD, and cancer mortality, independent of standard clinical and sociodemographic risk factors and consistent between racial groups (standardized HRs among all participants were 1.07 to 1.20 in age/sex/race-adjusted model and 1.04 to 1.08 after comprehensive adjustment; all P < 0.05/3 except for cancer mortality after comprehensive adjustment). The standard risk factors explained < 40% of the variations in NDI/RSI/SVI and mediated < 70% of their associations with mortality. Among 1110 circulating metabolites measured in 1688 participants, 134 and 27 metabolites were associated with NDI and RSI (all FDR < 0.05) and mediated 61.7% and 21.2% of the NDI/RSI-mortality association, respectively. Adding those metabolites to standard risk factors increased the mediation proportion from 38.4 to 87.9% and 25.8 to 42.6% for the NDI/RSI-mortality association, respectively.

CONCLUSIONS

Among low-income Black/African American adults and non-Hispanic White adults living in the southeastern United States, a disadvantaged neighborhood sociodemographic environment was associated with increased all-cause and CVD and cancer-specific mortality beyond standard risk factors. Circulating metabolites may unveil biological pathways underlying the health effect of neighborhood sociodemographic environment. More public health efforts should be devoted to reducing neighborhood environment-related health disparities, especially for low-income individuals.

Decreased circulating IPA levels identify subjects with metabolic comorbidities: A multi-omics study

In recent years several experimental observations demonstrated that the gut microbiome plays a role in regulating positively or negatively metabolic homeostasis. Indole-3-propionic acid (IPA), a Tryptophan catabolic product mainly produced by C. Sporogenes, has been recently shown to exert either favorable or unfavorable effects in the context of metabolic and cardiovascular diseases. We performed a study to delineate clinical and multiomics characteristics of human subjects characterized by low and high IPA levels. Subjects with low IPA blood levels showed insulin resistance, overweight, low-grade inflammation, and features of metabolic syndrome compared to those with high IPA. Metabolomics analysis revealed that IPA was negatively correlated with leucine, isoleucine, and valine metabolism. Transcriptomics analysis in colon tissue revealed the enrichment of several signaling, regulatory, and metabolic processes. Metagenomics revealed several OTU of ruminococcus, alistipes, blautia, butyrivibrio and akkermansia were significantly enriched in highIPA group while in lowIPA group Escherichia-Shigella, megasphera, and Desulfovibrio genus were more abundant. Next, we tested the hypothesis that treatment with IPA in a mouse model may recapitulate the observations of human subjects, at least in part. We found that a short treatment with IPA (4 days at 20/mg/kg) improved glucose tolerance and Akt phosphorylation in the skeletal muscle level, while regulating blood BCAA levels and gene expression in colon tissue, all consistent with results observed in human subjects stratified for IPA levels. Our results suggest that treatment with IPA may be considered a potential strategy to improve insulin resistance in subjects with dysbiosis.

Reduced Taurine Serum Levels in Inflammatory Bowel Disease

Taurine is a semi-essential micronutrient that acts as an anti-inflammatory molecule. The oral administration of taurine to colitic mice attenuates ongoing mucosal inflammation. This study aimed to determine whether inflammatory bowel diseases (IBDs) are marked by changes in the circulating levels of taurine. We measured the serum concentrations of taurine in 92 IBD patients [46 with ulcerative colitis (UC) and 46 with Crohn's disease (CD)] and 33 healthy controls with a commercial ELISA kit. The taurine levels were significantly decreased in both patients with UC and patients with CD compared to the controls, while there was no difference between CD and UC. Taurine levels declined with age in healthy controls but not in IBDs. IBD patients younger than 50 years had levels of taurine reduced compared to their age-matched controls. In the IBD group, taurine levels were not influenced by the body mass index of the patients and the consumption of taurine-rich nutrients, while they were significantly reduced in UC patients with clinically active disease compared to those in clinical remission. These findings indicate that IBDs are marked by serum taurine deficiency, which would seem to reflect the activity of the disease, at least in UC.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

Effects of joint screening for prostate, lung, colorectal, and ovarian cancer - results from a controlled trial

Background

Although screening is widely used to reduce cancer burden, untargeted cancers are frequently missed after single cancer screening. Joint cancer screening is presumed as a more effective strategy to reduce overall cancer burden.

Methods

Gender-specific screening effects on PLCO cancer incidence, PLCO cancer mortality, all-neoplasms mortality and all-cause mortality were evaluated, and meta-analyses based on gender-specific screening effects were conducted to achieve the pooled effects. The cut-off value of time-dependent receiver-operating-characteristic curve of 10-year combined PLCO cancer risk was used to reclassify participants into low- and high-risk subgroups. Further analyses were conducted to investigate screening effects stratified by risk groups and screening compliance.

Results

After a median follow-up of 10.48 years for incidence and 16.85 years for mortality, a total of 5,506 PLCO cancer cases, 1,845 PLCO cancer deaths, 3,970 all-neoplasms deaths, and 14,221 all-cause deaths were documented in the screening arm, while 6,261, 2,417, 5,091, and 18,516 outcome-specific events in the control arm. Joint cancer screening did not significantly reduce PLCO cancer incidence, but significantly reduced male-specific PLCO cancer mortality (hazard ratio and 95% confidence intervals [HR(95%CIs)]: 0.88(0.82, 0.95)) and pooled mortality [0.89(0.84, 0.95)]. More importantly, joint cancer screening significantly reduced both gender-specific all-neoplasm mortality [0.91(0.86, 0.96) for males, 0.91(0.85, 0.98) for females, and 0.91(0.87, 0.95) for meta-analyses] and all-cause mortality [0.90(0.88, 0.93) for male, 0.88(0.85, 0.92) for female, and 0.89(0.87, 0.91) for meta-analyses]. Further analyses showed decreased risks of all-neoplasm mortality was observed with good compliance [0.72(0.67, 0.77) for male and 0.72(0.65, 0.80) for female] and increased risks with poor compliance [1.61(1.40, 1.85) for male and 1.30(1.13, 1.40) for female].

Conclusion

Joint cancer screening could be recommended as a potentially strategy to reduce the overall cancer burden. More compliance, more benefits. However, organizing a joint cancer screening not only requires more ingenious design, but also needs more attentions to the potential harms.

Trial registration

NCT00002540 (Prostate), NCT01696968 (Lung), NCT01696981 (Colorectal), NCT01696994 (Ovarian).

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Chronic disease and multimorbidity in the Chinese older adults' population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BACKGROUND

Owing to an increase in life expectancy, it is common for the older adults to suffer from chronic diseases that can result in disability and a low quality of life. This study aimed to explore the influence of chronic diseases and multimorbidities on activities of daily living (ADLs) and instrumental ADLs (IADLs) in an older Chinese population.

METHODS

Based on the Chinese Longitudinal Healthy Longevity Survey (2018), 9,155 older adults aged 65 years and above were included in the study. A self-administered questionnaire was used to collect information on demographic characteristics, chronic diseases, ADLs, and IADLs. The impact of factors affecting ADL and IADL impairment in older adults was analysed using binary logistic regression.

RESULTS

In total, 66.3% participants had chronic diseases. Hypertension, heart disease, arthritis, diabetes and cerebrovascular disease were among the top chronic diseases. Of these, 33.7% participants had multimorbidities. The most common combination of the two chronic diseases was hypertension and heart disease (11.2%), whereas the most common combination of the three chronic diseases was hypertension, heart disease, and diabetes (3.18%). After categorising the older adults into four age groups, dementia, visual impairment, and hearing impairment were found to be more prevalent with increasing age. The prevalence of hypertension, heart disease, cerebrovascular disease, gastrointestinal ulcers, arthritis and chronic nephritis gradually increased with age until the age of 75 years, peaked in the 75-84 years age group, and then showed a decreasing trend with age. Multimorbidity prevalence followed a similar pattern. Regression analysis indicated that the increase in age group and the number of chronic diseases independently correlated with impairments in ADL as well as IADL. Additionally, gender, physical activity, educational background, obesity, depressive symptoms, and falls also had an impact on ADLs or IADLs.

CONCLUSION

Chronic diseases and multimorbidities are common in older adults, and it is important to note that aging, multimorbidity, obesity, and unhealthy lifestyle choices may interfere with ADLs or IADLs in older adults. Therefore, it is imperative that primary healthcare providers pay special attention to older adults and improve screening for multimorbidity and follow-up needs.

Simultaneous Detection of Aldehyde Metabolites by Light-Assisted Ambient Ionization Mass Spectrometry

In the clinic, small-molecule metabolites (SMMs) in blood are highly convincing indicators for disease diagnosis, such as cancer. However, challenges still exist for detection of SMMs due to their low concentration and complicated components in blood. In this work, we report the design of a novel "selenium signature" nanoprobe (Se nanoprobe) for efficient identification of multiple aldehyde metabolites in blood. This Se nanoprobe consists of magnetic nanoparticles that can enrich aldehyde metabolites from a complex environment, functionalized with photosensitive "selenium signature" hydrazide molecules that can react with aldehyde metabolites. Upon irradiation with UV, the aldehyde derivatives can be released from the Se nanoprobe and further sprayed by mass spectrometry through ambient ionization (AIMS). By quantifying the selenium isotope distribution (MS/MS) from the derivatization product, accurate detection of several aldehyde metabolites, including valeraldehyde (Val), heptaldehyde (Hep), 2-furaldehyde (2-Fur), 10-undecenal aldehyde (10-Und), and benzaldehyde (Ben), is realized. This strategy reveals a new solution for quick and accurate cancer diagnosis in the clinic.

Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study

Background

The coexistence of coronary artery disease (CAD) and cognitive impairment has become a common clinical phenomenon. However, there is currently limited research on the etiology of this disease cluster, discovery of biomarkers, and identification of precise intervention targets.

Methods

We explored the causal connections between gut microbiota, blood metabolites, and the disease cluster of CAD combined with cognitive impairment through two-sample Mendelian randomization (TSMR). Additionally, we determine the gut microbiota and blood metabolites with the strongest causal associations using Bayesian model averaging multivariate Mendelian randomization (MR-BMA) analysis. Furthermore, we will investigate the mediating role of blood metabolites through a two-step Mendelian randomization design.

Results

We identified gut microbiota that had significant causal associations with cognitive impairment. Additionally, we also discovered blood metabolites that exhibited significant causal associations with both CAD and cognitive impairment. According to the MR-BMA results, the free cholesterol to total lipids ratio in large very low density lipoprotein (VLDL) was identified as the key blood metabolite significantly associated with CAD. Similarly, the cholesteryl esters to total lipids ratio in small VLDL emerged as the primary blood metabolite with a significant causal association with dementia with lewy bodies (DLB). For the two-step Mendelian randomization analysis, we identified blood metabolites that could potentially mediate the association between genus Butyricicoccus and CAD in the potential causal links.

Conclusion

Our study utilized Mendelian randomization (MR) to identify the gut microbiota features and blood metabolites characteristics associated with the disease cluster of CAD combined with cognitive impairment. These findings will provide a meaningful reference for the identification of biomarkers for the disease cluster of CAD combined with cognitive impairment as well as the discovery of targets for intervention to address the problems in the clinic.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Tryptophan metabolites and incident cardiovascular disease: The EPIC-Norfolk prospective population study

BACKGROUND AND AIMS

Cardiovascular disease (CVD) remains the largest cause of death globally due to various risk factors. One novel potential contributor to CVD might be the metabolism of the essential amino acid tryptophan (Trp), which through many pathways can produce immunomodulatory metabolites such as kynurenine, indole-3-propionate and serotonin. We aim to identify the metabolites with the strongest association with cardiovascular disease, utilizing a substantial and diverse cohort of individuals. In our pursuit of this aim, our primary focus is to validate and reinforce the findings from previous cross-sectional studies.

METHODS

We used the community-based EPIC-Norfolk cohort (46.3 % men, age 59.8 ± 9.0) with a median follow-up of 22.1 (17.6-23.3) years to study associations between the relative levels of Trp metabolites measured with untargeted metabolomics and incident development of CVD. Serum from n = 11,972 apparently healthy subjects was analysed, of which 6982 individuals had developed CVD at the end of follow-up. Cox proportional hazard models were used to study associations, adjusted for sex, age, conventional cardiovascular risk factors and CRP. All metabolites were Ln-normalised prior to analysis.

RESULTS

Higher levels of Trp were inversely associated with mortality (HR 0.73; CI 0.64-0.83) and fatal CVD (HR 0.76; CI 0.59-0.99). Higher levels of kynurenine (HR 1.33; CI 1.19-1.49) and the [Kynurenine]/[Tryptophan]-ratio (HR 1.24; CI 1.14-1.35) were associated with a higher incident development of CVD. Serotonin was not associated with overall CVD, but we did find associations for myocardial infarction and stroke. Adjustment for CRP did not yield any discernible differences in effect size.

CONCLUSIONS

Tryptophan levels were inversely correlated with CVD, while several of its major metabolites (especially kynurenine and serotonin) were positively correlated. These findings indicate that mechanistic studies are required to understand the role of Trp metabolism in CVD with the goal to identify new therapeutic targets.

Plasma proteomic profiles predict individual future health risk

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.