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

Histological and genetic features and therapeutic responses of lung cancers explored via the global analysis of their metabolome profile

BACKGROUND

Lung cancer is the deadliest disease globally, with more than 120,000 diagnosed cases and more than 75,000 deaths annually in Japan. Several treatment options for advanced lung cancer are available, and the discovery of biomarkers will be useful for personalized medicine. Using metabolome analysis, we aimed to identify biomarkers for diagnosis and treatment response by examining the changes in metabolites associated with lung cancer progression.

METHODS

Plasma samples from patients with recurrent or metastatic non-small cell lung carcinomas diagnosed at Tohoku University Hospital between 2019 and 2024 were used in this study. Metabolomic analysis was performed using the Biocrates Life Sciences MxP Quant 500 kit. Multivariate, principal component, and orthogonal partial least squares discriminant analyses were performed.

RESULTS

The triglyceride and phosphatidylcholine concentrations were higher in the patients with early than in those with advanced lung adenocarcinomas. However, the cholesterol ester concentrations were higher for the patients with advanced lung cancer. The concentrations of hexosylceramide were higher in patients with early lung adenocarcinoma than in those with squamous cell carcinoma. Relative to epidermal growth factor receptor (EGFR)-mutation negative cases, the EGFR-mutation positive cases showed marked differences between the ceramide and triglyceride concentrations. For the best therapeutic effect of EGFR-TKI treatment, the hexosylceramide (HexCer) (d18:1/24:0), ceramide (Cer) (d18:2/22:0), and ceramide (Cer) (d18:2/24:0) concentrations were higher for the stable and progressive disease groups. The concentrations of phosphatidylcholine (PC) ae C42:2, sphingomyelin (SM) C24:1, and lysophosphatidylcholine (lysoPC) a C18:2 were higher in the partial response group treated with immune checkpoint inhibitors and chemotherapy.

CONCLUSION

Metabolomic analysis may be useful for the diagnosis and treatment of lung cancer and may provide clues for new therapeutic strategies. PC ae C42:2, SM C24:1, and lysoPC a C18:2 can serve as predictive biomarkers for monitoring the therapeutic effects of the combination of immune checkpoint inhibitors and chemotherapy.

Metabolite signatures of chronological age, aging, survival, and longevity

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study

Studying relationships between longitudinal changes in omics variables and risks of events requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study focusing on understudied associations of longitudinal changes in lysophosphatidylcholines (LPC) with mortality and aging-related outcomes (23 LPC species, 5,790 measurements of each in 4,011 participants, 1,431 of whom died during follow-up). JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest effect size observed for LPC 15:0/0:0 (hazard ratio: 0.715, 95% CI (0.649, 0.788)). SPM applications to LPC 15:0/0:0 revealed how the association found in JM reflects underlying aging-related processes: decline in robustness to deviations from optimal LPC levels, better ability of males' organisms to return to equilibrium LPC levels (which are higher in females), and increasing gaps between the optimum and equilibrium levels leading to increased mortality risks with age. Our results support LPC as a biomarker of aging and related decline in robustness/resilience, and call for further exploration of factors underlying age-dynamics of LPC in relation to mortality and diseases.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Comparison of maternal venous blood metabolomics collected as dried blood spots, dried blood microsamplers, and plasma for integrative environmental health research

Use of capillary blood devices for exposome research can deepen our understanding of the intricate relationship between environment and health, and open up new avenues for preventive and personalized medicine, particularly for vulnerable populations. While the potential of these whole blood devices to accurately measure chemicals and metabolites has been demonstrated, how untargeted metabolomics data from these samplers can be integrated with previous and ongoing environmental health studies that have used conventional blood collection approaches is not yet clear. Therefore, we performed a comprehensive comparison between relative-quantitative metabolite profiles measured in venous blood collected with dried whole blood microsamplers (DBM), dried whole blood spots (DBS), and plasma from 54 mothers in an ethnically diverse population. We determined that a majority of the 309 chemicals and metabolites showed similar median intensity rank, moderate correlation, and moderate agreement between participant-quantiled intraclass correlation coefficients (ICCs) for pair-wise comparisons among the three biomatrices. In particular, whole blood sample types, DBM and DBS, were in highest agreement across metabolite comparison metrics, followed by metabolites measured in DBM and plasma, and then metabolites measured in DBS and plasma. We provide descriptive characteristics and measurement summaries as a reference database. This includes unique metabolites that were particularly concordant or discordant in pairwise comparisons. Our results demonstrate that the range of metabolites from untargeted metabolomics data collected with DBM, DBS, and plasma provides biologically relevant information for use in independent exposome investigations. However, before meta-analysis with combined datasets are performed, robust statistical approaches that integrate untargeted metabolomics data collected on different blood matrices need to be developed.

Plasma metabolomic markers underlying skeletal muscle mitochondrial function relationships with cognition and motor function

BACKGROUND

Lower skeletal muscle mitochondrial function is associated with future cognitive impairment and mobility decline, but the biological underpinnings for these associations are unclear. We examined metabolomic markers underlying skeletal muscle mitochondrial function, cognition and motor function.

METHODS

We analysed data from 560 participants from the Baltimore Longitudinal Study of Aging (mean age: 68.4 years, 56% women, 28% Black) who had data on skeletal muscle oxidative capacity (post-exercise recovery rate of phosphocreatine, kPCr) via 31P magnetic resonance spectroscopy and targeted plasma metabolomics using LASSO model. We then examined which kPCr-related markers were also associated with cognition and motor function in a larger sample (n = 918, mean age: 69.4, 55% women, 27% Black).

RESULTS

The LASSO model revealed 24 metabolites significantly predicting kPCr, with the top 5 being asymmetric dimethylarginine, lactic acid, lysophosphatidylcholine a C18:1, indoleacetic acid and triacylglyceride (17:1_34:3), also significant in multivariable linear regression. The kPCr metabolite score was associated with cognitive or motor function, with 2.5-minute usual gait speed showing the strongest association (r = 0.182). Five lipids (lysophosphatidylcholine a C18:1, phosphatidylcholine ae C42:3, cholesteryl ester 18:1, sphingomyelin C26:0, octadecenoic acid) and 2 amino acids (leucine, cystine) were associated with both cognitive and motor function measures.

CONCLUSION

Our findings add evidence to the hypothesis that mitochondrial function is implicated in the pathogenesis of cognitive and physical decline with aging and suggest that targeting specific metabolites may prevent cognitive and mobility decline through their effects on mitochondria. Future omics studies are warranted to confirm these findings and explore mechanisms underlying mitochondrial dysfunction in aging phenotypes.

An exploratory metabolomic comparison of participants with fast or absent functional progression from 2CARE, a randomized, double-blind clinical trial in Huntington's disease

Huntington's disease (HD) is increasingly recognized for diverse pathology outside of the nervous system. To describe the biology of HD in relation to functional progression, we previously analyzed the plasma and CSF metabolome in a cross-sectional study of participants who had various degrees of functional impairment. Here, we carried out an exploratory study in plasma from HD individuals over a 3-year time frame to assess whether differences exist between those with fast or absent clinical progression. There were more differences in circulating metabolite levels for fast progressors compared to absent progressors (111 vs 20, nominal p < 0.05). All metabolite changes in faster progressors were decreases, whereas some metabolite concentrations increased in absent progressors. Many of the metabolite levels that decreased in the fast progressors were higher at Screening compared to absent progressors but ended up lower by Year 3. Changes in faster progression suggest greater oxidative stress and inflammation (kynurenine, diacylglycerides, cysteine), disturbances in nitric oxide and urea metabolism (arginine, citrulline, ornithine, GABR), lower polyamines (putrescine and spermine), elevated glucose, and deficient AMPK signaling. Metabolomic differences between fast and absent progressors suggest the possibility of predicting functional decline in HD, and possibly delaying it with interventions to augment arginine, polyamines, and glucose regulation.