A multivariate approach to the description of patient populations. An example of the analysis of the hormone profiles of patients with advanced prostate cancer.
In view of the multifactorial nature of the endocrine dysfunctions that may develop during prostate cancer and the unsuitability of the most widely used statistical methods to study such dysfunction, we have in the present study examined the relationships among 17 biological variables in 26 patients with advanced prostate cancer by two complementary multivariate methods, correspondence factorial analysis (CFA) and a hierarchical automatic classification procedure. The 17 variables included 14 hormones, their precursors or metabolites [LH, FSH, estradiol (E2), testosterone, dihydrotestosterone (DHT), androstenedione (A), androstenediol (Ediol), dehydroepiandrosterone (DHA), DHA-sulphate (DS), cortisol (CORT), 17 alpha-hydroxyprogesterone (17-OH-PROG), pregnenolone (PREG), 17 alpha-hydroxypregnenolone (17-OH-PREG), and androstanediol glucuronide (ADG)], one plasma binding protein, namely, sex-hormone-binding protein (SHBG) and two tumour markers, prostatic acid phosphatase (PAP) and prostate-specific antigen (PSA). The originality of these multivariate methods is that they do not preselect a dependent variable nor perform two-by-two correlations as in stepwise multiple regression analysis but describe the patient population by extracting layers of correlations (from strong to weak) from amid confounding variables. Compared to principal component analysis which is based on covariance, CFA, based on the chi 2-metric, enables the licit representation of both tests and patients on the same factorial maps. From an examination of proximity among variables, it is possible to deduce which tests are related, which groups of patients have similar hormone profiles, and which tests vary most in which patients. The most discriminant factors in this particular population of patients were PSA and PAP levels, which were, however, not strongly correlated and were apparently selectively associated with certain hormones. PAP seemed the more pathological marker; PSA was somewhat anticorrelated to the adrenal androgen (DHA and DS) and PREG levels. The hormones with the lowest variance were A, Ediol and CORT reflecting their key roles in metabolism. A number of patients were hypogonadic. SHBG levels were not closely related to total T levels but anticorrelated with ADG suggesting that, in the patients concerned, SHBG decreases the bioavailable T fraction. There was no correlation between ADG and precursor hormones (PREG, DHA, DS) but a slight anticorrelation between these precursors and DHT. Therefore the source of ADG in these patients does not seem to be increased levels of precursor hormones nor of DHT but increased peripheral tissue metabolism of androgens. In future, descriptive multivariate analyses of large patient cohorts should help to define subpopulations with distinctive hormone profiles for prospective clinical studies.
Ojasoo
T
T
CNRS URA 09, Université Pierre et Marie Curie, Paris, France.
Fiet
J
J
Raynaud
J P
JP
Doré
J C
JC
D006728:Hormones / Q000097:blood*; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D011471:Prostatic Neoplasms / Q000097:blood* / Q000503:physiopathology
D006728:Hormones