Innovating Metabolic Biomarkers for Hyperpolarized NMR

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

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A neuroimaging investigation into the role of peripheral metabolic biomarkers in the anticipation of reward in alcohol use

Drug and Alcohol Dependence ◽

10.1016/j.drugalcdep.2021.108638 ◽

2021 ◽

Vol 221 ◽

pp. 108638

Author(s):

Monica L. Faulkner ◽

Reza Momenan ◽

Lorenzo Leggio

Keyword(s):

Alcohol Use ◽

Metabolic Biomarkers

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Influences of Recreational Tennis-Playing Exercise Time on Cardiometabolic Health Parameters in Healthy Elderly: The ExAMIN AGE Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031255 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1255

Author(s):

Hsiao-Han Chao ◽

Yi-Hung Liao ◽

Chun-Chung Chou

Keyword(s):

Insulin Resistance ◽

Arterial Stiffness ◽

Ankle Brachial Index ◽

Density Lipoprotein ◽

Activity Level ◽

Lipoprotein Cholesterol ◽

Cardiometabolic Health ◽

Model Assessment ◽

Healthy Elderly ◽

Metabolic Biomarkers

Background: Aging and chronic degeneration are the primary threats to cardiometabolic health in elderly populations. Regular appropriate exercise would benefit the advanced aging population. Purpose: This study investigates whether the degree of weekly tennis participation exhibits differences in primary cardiometabolic parameters, including arterial stiffness, inflammation, and metabolic biomarkers in elderly tennis players. Methods: One hundred thirty-five long-term participants in elder tennis (>50 years old) were initially screened. Twenty-six eligible and voluntary subjects were divided into high tennis time group (HT) (14 ± 1.3 h/week) and low tennis time group (LT) (4.5 ± 0.7 h/week) by stratification analysis based on the amount of tennis playing activity time. The brachial-ankle pulse wave velocity (baPWV), blood pressure, ankle-brachial index (ABI), blood metabolic biomarkers, and insulin resistance were measured to compare the difference between HT and LT groups. Results: The baPWV was significantly lower in the HT group than that in the LT group (1283.92 ± 37.01 vs. 1403.69 ± 53.71 cm/s, p < 0.05). We also found that the HT insulin-resistant homeostasis model assessment (HOMA-IR) was significantly lower than that of LT (1.41 ± 0.11 vs. 2.27 ± 0.48 μIU/mL, p < 0.05). However, the blood lipid biomarkers (glucose, cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride) were not statistical different between HT and LT groups (p > 0.05). Conclusion: We demonstrated that under the condition of similar daily physical activity level, elderly with a higher time of tennis-playing (HT group) exhibited relatively lower arterial stiffness (lower PWV) and lower insulin resistance compared to those with lower time tennis-playing (LT).

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Non-invasive plasma glycomic and metabolic biomarkers of post-treatment control of HIV

Nature Communications ◽

10.1038/s41467-021-24077-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Leila B. Giron ◽

Clovis S. Palmer ◽

Qin Liu ◽

Xiangfan Yin ◽

Emmanouil Papasavvas ◽

...

Keyword(s):

Treatment Interruption ◽

Post Treatment ◽

Machine Learning Algorithms ◽

Virologic Suppression ◽

Hiv Latency ◽

Viral Rebound ◽

Treatment Control ◽

Non Invasive ◽

Metabolic Biomarkers

AbstractNon-invasive biomarkers that predict HIV remission after antiretroviral therapy (ART) interruption are urgently needed. Such biomarkers can improve the safety of analytic treatment interruption (ATI) and provide mechanistic insights into the host pathways involved in post-ART HIV control. Here we report plasma glycomic and metabolic signatures of time-to-viral-rebound and probability-of-viral-remission using samples from two independent cohorts. These samples include a large number of post-treatment controllers, a rare population demonstrating sustained virologic suppression after ART-cessation. These signatures remain significant after adjusting for key demographic and clinical confounders. We also report mechanistic links between some of these biomarkers and HIV latency reactivation and/or myeloid inflammation in vitro. Finally, machine learning algorithms, based on selected sets of these biomarkers, predict time-to-viral-rebound with 74% capacity and probability-of-viral-remission with 97.5% capacity. In summary, we report non-invasive plasma biomarkers, with potential functional significance, that predict both the duration and probability of HIV remission after treatment interruption.

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Diagnostic Power of Circulatory Metabolic Biomarkers as Metabolic Syndrome Risk Predictors in Community-Dwelling Older Adults in Northwest of England (A Feasibility Study)

Nutrients ◽

10.3390/nu13072275 ◽

2021 ◽

Vol 13 (7) ◽

pp. 2275

Author(s):

Razieh Hassannejad ◽

Hamsa Sharrouf ◽

Fahimeh Haghighatdoost ◽

Ben Kirk ◽

Farzad Amirabdollahian

Keyword(s):

Risk Factors ◽

Older Adults ◽

Metabolic Syndrome ◽

Roc Analysis ◽

Cross Validation ◽

Community Dwelling ◽

Tnf Α ◽

Diagnostic Power ◽

Metabolic Biomarkers ◽

Community Dwelling Older Adults

Background: Metabolic Syndrome (MetS) is a cluster of risk factors for diabetes and cardiovascular diseases with pathophysiology strongly linked to aging. A range of circulatory metabolic biomarkers such as inflammatory adipokines have been associated with MetS; however, the diagnostic power of these markers as MetS risk correlates in elderly has yet to be elucidated. This cross-sectional study investigated the diagnostic power of circulatory metabolic biomarkers as MetS risk correlates in older adults. Methods: Hundred community dwelling older adults (mean age: 68.7 years) were recruited in a study, where their blood pressure, body composition and Pulse Wave Velocity (PWV) were measured; and their fasting capillary and venous blood were collected. The components of the MetS; and the serum concentrations of Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), Plasminogen Activator Inhibitor-I (PAI-I), Leptin, Adiponectin, Resistin, Cystatin-C, C-Reactive Protein (CRP), insulin and ferritin were measured within the laboratory, and the HOMA1-IR and Atherogenic Index of Plasma (AIP) were calculated. Results: Apart from other markers which were related with some cardiometabolic (CM) risk, after Bonferroni correction insulin had significant association with all components of Mets and AIP. These associations also remained significant in multivariate regression. The multivariate odds ratio (OR with 95% confidence interval (CI)) showed a statistically significant association between IL-6 (OR: 1.32 (1.06–1.64)), TNF-α (OR: 1.37 (1.02–1.84)), Resistin (OR: 1.27 (1.04–1.54)) and CRP (OR: 1.29 (1.09–1.54)) with MetS risk; however, these associations were not found when the model was adjusted for age, dietary intake and adiposity. In unadjusted models, insulin was consistently statistically associated with at least two CM risk factors (OR: 1.33 (1.16–1.53)) and MetS risk (OR: 1.24 (1.12–1.37)) and in adjusted models it was found to be associated with at least two CM risk factors and MetS risk (OR: 1.87 (1.24–2.83) and OR: 1.25 (1.09–1.43)) respectively. Area under curve (AUC) for receiver operating characteristics (ROC) demonstrated a good discriminatory diagnostics power of insulin with AUC: 0.775 (0.683–0.866) and 0.785 by cross validation and bootstrapping samples for at least two CM risk factors and AUC: 0.773 (0.653–0.893) and 0.783 by cross validation and bootstrapping samples for MetS risk. This was superior to all other AUC reported from the ROC analysis of other biomarkers. Area under precision-recall curve for insulin was also superior to all other markers (0.839 and 0.586 for at least two CM risk factors and MetS, respectively). Conclusion: Fasting serum insulin concentration was statistically linked with MetS and its risk, and this link is stronger than all other biomarkers. Our ROC analysis confirmed the discriminatory diagnostic power of insulin as CM and MetS risk correlate in older adults.

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