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

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Graphical abstractHighlightsAn efficient GC–MS method is validated for a panel of 18 urinary steroids.A Kernel-PLS algorithm investigates the age dependence of the steroid panel.The age-regression model does not hold for subjects affected by prostate carcinoma.PLS-DA on steroid panel discriminates healthy from pathological individuals.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.

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