Literature DB >> 30232035

Correlation between chronological and physiological age of males from their multivariate urinary endogenous steroid profile and prostatic carcinoma-induced deviation.

Eleonora Amante1, Eugenio Alladio1, Alberto Salomone2, Marco Vincenti3, Federico Marini4, Giorgio Alleva5, Stefano De Luca5, Francesco Porpiglia5.   

Abstract

The biosynthesis of endogenous androgenic anabolic steroids (EAAS) in males varies with age. Knowledge of the general urinary EAAS profile's dependence from aging - not reported up to now - may represents a prerequisite for its exploitation in the screening and diagnostic support for several pathologies. Extended urinary EAAS profiles were obtained from healthy and pathological individuals, using a GC-MS method which was fully validated by a stepwise, analyst-independent scheme. Seventeen EAAS and five of their concentration ratios were determined and investigated using multivariate statistical methods. A regression model based on Kernel partial least squares algorithm was built to correlate the chronological age of healthy male individuals with their "physiological age" as determined from their urinary EAAS profile. Strong correlation (R2 = 0.75; slope = 0.747) and good prediction ability of the real chronological age was inferred from EAAS data. In contrast, patients with recent diagnosis (not pharmacologically treated) of prostatic carcinoma (PCa) exhibited a comprehensive EAAS profile with strong negative deviation from the model, corresponding a younger predicted age. This result is possibly related to the activation of anomalous steroid biosynthesis induced from PCa. Over a restricted 60-80 years-old population, PLS-discriminant analysis (DA) was used to distinguish healthy subjects from patients with untreated PCa. PLS-DA yielded excellent discrimination (sensitivity and specificity >90%) between healthy and pathological individuals. This proof-of-concept study provides a preliminary evaluation of multivariate DA on wide EAAS profiles as a screening method to distinguish PCa from non-pathological conditions, overcoming the potentially interfering effect of ageing.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  GC–MS; Kernel-PLS (K-PLS) regression; Physiological age; Prostatic carcinoma (PCa); Urinary steroid profile (USP)

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Year:  2018        PMID: 30232035     DOI: 10.1016/j.steroids.2018.09.007

Source DB:  PubMed          Journal:  Steroids        ISSN: 0039-128X            Impact factor:   2.668


  3 in total

1.  Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models.

Authors:  Eleonora Amante; Alberto Salomone; Eugenio Alladio; Marco Vincenti; Francesco Porpiglia; Rasmus Bro
Journal:  Molecules       Date:  2019-08-22       Impact factor: 4.411

2.  Experimental and statistical protocol for the effective validation of chromatographic analytical methods.

Authors:  Eugenio Alladio; Eleonora Amante; Cristina Bozzolino; Fabrizio Seganti; Alberto Salomone; Marco Vincenti; Brigitte Desharnais
Journal:  MethodsX       Date:  2020-05-16

3.  Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia.

Authors:  Eleonora Amante; Andrea Cerrato; Eugenio Alladio; Anna Laura Capriotti; Chiara Cavaliere; Federico Marini; Carmela Maria Montone; Susy Piovesana; Aldo Laganà; Marco Vincenti
Journal:  Sci Rep       Date:  2022-03-14       Impact factor: 4.379

  3 in total

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