Plasmatic Hippuric Acid as a Hallmark of Frailty in an Italian Cohort: The Mediation Effect of Fruit-Vegetable Intake.

Frailty syndrome is an age-related condition involving a loss of resilience, susceptibility to adverse health outcomes, and poor quality of life. This study was conducted in the framework of InveCe.Ab, an ongoing longitudinal population-based study. Plasma from 130 older individuals (older adults aged 76-78 years) was analyzed and validated (on 303 participants) using mass spectrometry-based metabolomics approaches. Equivalence tests showed that metabolites from the central cellular metabolic pathways were equivalent in frail and fit participants. Hippuric acid was the only cometabolite that distinguished fit from frail older adults. Logistic regression analysis indicated that high hippuric acid levels are significantly associated with a reduction of the risk of frailty after 4 years. Mediation analysis using a Frailty Index, hippuric acid, and fruit-vegetable intake supported the role of fruit-vegetable consumption in the hippuric acid relationship with the Frailty Index. These data point to low plasma hippuric acid as a plausible hallmark of frailty status, associated with lower fruit-vegetable intakes.

Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease's complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell's physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification. METHODS: We analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1G93A and TDP-43Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis. RESULTS: Our procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs. CONCLUSIONS: Our analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.

Glutaminase Inhibition on NSCLC Depends on Extracellular Alanine Exploitation.

Non-small-cell lung cancer (NSCLC) cell lines vary in their sensitivity to glutaminase inhibitors, so it is important to identify the metabolic assets underling their efficacy in cancer cells. Even though specific genetic lesions such as in KRAS and LKB1 have been associated with reliance on glutamine for their metabolic needs, we found no distinction between glutaminase inhibitor CB-839 sensitivity and resistant phenotypes in NSCLC cells with or without these genetic alterations. We demonstrated the close relationship between environmental alanine uptake and catabolism. This response depended on the individual cell's ability to employ alanine aminotransferase (GPT2) to compensate the reduced glutamate availability. It may, therefore, be useful to determine GPT2 levels to predict which NSCLC patients would benefit most from glutaminase inhibitor treatment.