Biological markers and possibilities for predicting therapeutic results in schizophrenia: A methodological contribution

• Published in: Progress in neuro-psychopharmacology & biological psychiatry Vol. 26; no. 4; pp. 683 - 691
• Main Authors: Češková, Eva, Drybčák, Pavel, Lorenc, Miroslav
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01-05-2002; Elsevier
• Subjects: Adult; Biological and medical sciences; Biological markers; Biomarkers - blood; Computer tomography; Cortisolemia; Discriminant Analysis; Electroencephalography - statistics & numerical data; Forecasting; Humans; Male; Medical sciences; Middle Aged; Neuropharmacology; Pharmacology. Drug treatments; Prediction of treatment response; Psycholeptics: tranquillizer, neuroleptic; Psychology. Psychoanalysis. Psychiatry; Psychopharmacology; Schizophrenia; Schizophrenia - blood; Schizophrenia - diagnostic imaging; Schizophrenia - drug therapy; Tomography, X-Ray Computed - statistics & numerical data; GH; ICD-10; Cortisolemia; CGI; DST; Schizophrenia; S.D; Computer tomography; PRL; CT; HVA; QEEG; CHPS; PANSS; Biological markers; NES; Prediction of treatment response; CGI I; Human; Steroid hormone; Neuroleptic; Psychotropic; Prediction; Biological marker; Non response; Cortisol; Glucocorticoid; Psychosis; Response; Chemotherapy; Treatment; Investigation method; Adrenal hormone
• ISSN: 0278-5846; 1878-4216
• DOI: 10.1016/S0278-5846(01)00318-9

The aim of the research was to select some easily available and easily replicable biological markers that could be used as predictors of both acute and long-term therapy. A selection of state markers (structural computer tomography [CT] parameters, psychological parameters, tests concentrating on attention and memory, “soft signs”), trait markers (quantified electroencephalograph [QEEG], cortisolemia, prolactinemia [PRL], and their changes) and clinical symptoms (Positive and Negative Syndrome Scale [PANSS], Clinical Global Impression [CGI]) were determined in 52 hospitalised schizophrenic patients showing either an acute episode or an exacerbation. The evaluation was repeated after one year of outpatient therapy. The parameters studied so far are not sufficiently specific and sensitive; therefore, the prediction cannot be based on a single parameter. In order to overcome this shortcoming, we carried out the analysis by means of multidimensional statistics, applying discriminant analysis. The results of both a broader and a more specific approach are stated in the paper. In all the used examples of discriminant analysis, the optimum discriminator is cortisolemia or its changes after administering dexamethasone and structural CT parameters. The results indicate that combinations of vulnerability markers and state markers (cortisolemia) may be of predictive value and are compatible with the vulnerability–stress ethiopathogenetic model of schizophrenia.