Institute for Response-Genetics (e.V.)Chairman: Prof. Dr. Hans H. StassenPsychiatric Hospital (KPPP), University of Zurich |
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Predicting Response to TreatmentCurrent treatment options for patients with depression or schizophrenia, all exhibit quite modest and insufficient response rates, mainly because there is no causal therapy or medication. Relying on multidimensional "gene vectors", this study used multilayer neural nets (NNs) to separate treatment responders from non-responders. We recruited 902 inpatients with an ICD-10 diagnosis of either schizophrenic ("F2 patients") or depressive disorders ("F3 patients"). The study assessed today's acute inpatient treatment regimens regarding time course of recovery, efficacy of treatment, and adverse side effects with up to 8 repeated measurements. The genotyping included 100 candidate genes with genotypic patterns computed from 549 Single Nucleotide Polymorphisms (SNPs). Biological PredispositionThe multidimensional "gene vector" approach was found to be a powerful method to detail the complex structures of SNP data that are not detectable by conventional association methods. Molecular-genetic neural nets enabled the reliable classification of patients regarding response to treatment: F3 patients with 82.6% correctly classified "responders", 100% correctly classified "non-responders", and a 35.7% overlap with F2 patients; and F2 patients with 94.7% correctly classified "responders", 100% correctly classified "non-responders", and a 29.6% overlap with F3 patients. Irregularities in the genotypic patterns of certain mutation-prone genes were related not only to treatment response, but also to psychiatric vulnerabilities and chronically elevated IgM levels. Correlations between the NN classifier genes caused a certain redundancy, which made it unlikely that there is a direct causal link between genes, treatment response, psychiatric vulnerabilities and elevated IgM levels, since then several genes would have to overlap in their causal effects. Diagnoses-crossing VulnerabilitiesThe discovery of mutation-prone NN classifier genes that reproducibly separated treatment responders from non-responders will provide new insights into the pathogenesis of major psychiatric disorders, and might even lead to more causality-oriented treatments. Overlaps between diagnostic subgroups on the genotype level suggested that (1) diagnoses-crossing vulnerabilities are likely involved in the pathogenesis of major psychiatric disorders; (2) clinically defined diagnoses may not constitute etiological entities. References
Stassen HH, Bachmann S, Bridler R, Cattapan K, Seifritz E. Polypharmacy in Psychiatry and
Weight Gain: Longitudinal Study of 832 Patients Hospitalized for Depression or Schizophrenia,
along with Data of 3,180 Students from Europe, the U.S., South America, and China.
Eur Arch Psychiatry Clin Neurosci. 2024; https://doi.org/10.1007/s00406-024-01767-2
(Epub ahead of print)
[get the article]
Stassen HH, Bachmann S, Bridler R, Cattapan K, Hartmann AM, Rujescu D, Seifritz E, Weisbrod M,
Scharfetter C: Genetic Determinants of Antidepressant and Antipsychotic Drug Response:
A molecular-genetic study of 902 patients over 6 weeks. Psychiatry Res. 2024 [submitted for publication]
Stassen HH, Bachmann S, Bridler R, Cattapan K, Hartmann AM, Rujescu D, Seifritz E, Weisbrod M,
Scharfetter C. Analysis of genetic diversity in patients with major psychiatric disorders
versus healthy controls: A molecular-genetic study of 1698 subjects genotyped for 100 candidate
genes (549 SNPs). Psychiatry Res. 2024; 333: 115720. doi: 10.1016/j.psychres.2024.115720
[get the article]
Greil W, de Bardeci M, Müller-Oerlinghausen B, Nievergelt N, Stassen HH, Hasler G, Erfurth A,
Cattapan K, Rüther E, Seifert J, Toto S, Bleich S, Schoretsanitis G. Controversies regarding
lithium-associated weight gain: case-control study of real-world drug safety data.
Int J Bipolar Disord. 2023; 11(1): 34. doi: 10.1186/s40345-023-00313-8
[get the article]
de Bardeci M, Greil W, Stassen H, Willms J, Köberle U, Bridler R, Hasler G, Kasper S, Rüther E,
Bleich S, Toto S, Grohmann R, Seifert J. Dear Doctor Letters regarding citalopram and escitalopram:
guidelines vs real-world data. Eur Arch Psychiatry Clin Neurosci. 2023; 273(1): 65-74
[get the article]
Stassen HH, Bachmann S, Bridler R, Cattapan K, Herzig D, Schneeberger A, Seifritz E:
Detailing the Effects of Polypharmacy in Psychiatry: Longitudinal Study of 320 Patients
Hospitalized for Depression or Schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2022;
272(4): 603-619
[get the article]
Greil W, de Bardeci M, Seifert J, Bernegger X, Cattapan K, Stassen HH, Wagner AL, Sieberer M,
Grohmann R, Toto S: Treatment of depression: Are psychotropic drugs appropriately dosed in women
and in the elderly? Dosages of psychotropic drugs by sex and age in routine clinical practice.
Hum Psychopharmacol. 2022; 37(1): e2809
[get the article]
Stassen HH, Bachmann S, Bridler R, Cattapan K, Herzig D, Schneeberger A, Seifritz E:
Inflammatory Processes linked to Major Depression & Schizophrenic Disorders and the Effects
of Polypharmacy in Psychiatry: Evidence from a longitudinal Study of 279 Patients under
Therapy. Eur Arch Psychiatry Clin Neurosci. 2021; 271(3): 507-520
[get the article]
Pollak TA, Lennox B, Müller S, Benros ME, Prüss H, Tebartz van Elst L, Klein H, Steiner J,
Frodl T, Bogerts B, Tian L, Groc L, Hasan A, Baune BT, Endres D, Haroon E, Yolken R,
Benedetti F, Halaris A, Meyer J, Stassen HH, Leboyer M, Fuchs D, Otto M, Brown DA, Vincent A,
Najjar S, Bechter K: An international consensus on an approach to the diagnosis and
management of psychosis of suspected autoimmune origin: the concept of autoimmune psychosis.
Lancet Psychiatry 2020; 7(1): 93-108
Zhang M, Bridler R, Mohr C, Moragrega I, Sun N, Xu Z, Yang Z, Possenti M, Stassen HH:
Early Detection of the Risk of Developing Psychiatric Disorders: A Study of 461 Chinese University
Students under Chronic Stress. Psychopathology 2019; 52(6): 367-377
[get the article]
Bhake R, Kluckner V, Stassen HH, Russell GM, Leendertz J, Stevens K, Linthorst ACE,
Lightman S: Continuous Free Cortisol Profiles – Circadian Rhythms in Healthy Men.
J Clinical Endocrinology & Metabolism 2019; 104(12): 5935-5947
Stassen HH: Heterogeneity of schizophrenic disorders and link to chronically elevated IgM
values. Neurology, psychiatry and brain research 2018; 29: 23-24
Braun S, Bridler R, Müller N, Schwarz MJ, Seifritz E, Weisbrod M, Zgraggen A, Stassen HH:
Inflammatory Processes and Schizophrenia: Two Independent Lines of Evidence from a Study
of Twins Discordant and Concordant for Schizophrenic Disorders. Eur Arch Psychiatry Clin
Neurosci 2017; 267: 377-389
[get the article]
Stassen HH, Delfino JP, Kluckner VJ, Lott P, Mohr C: Vulnerabilität und psychische Erkrankung.
Swiss Archives of Neurology and Psychiatry 2014; 165(5): 152-157
Drago A, Giegling I, Schäfer M, Hartmann AM, Friedl M, Konte B, Möller HJ, De Ronchi D, Stassen HH,
Serretti A, Rujescu D: AKAP13, CACNA1, GRIK4 and GRIA1 genetic variations may be associated with
haloperidol efficacy during acute treatment. Eur Neuropsychopharmacol. 2013; 23(8): 887-894
Giegling I, Balzarro B, Porcelli S, Schäfer M, Hartmann AM, Friedl M, Konte B, Krämer P, Möller HJ,
De Ronchi D, Stassen HH, Serretti A, Rujescu D: Influence of ANKK1 and DRD2 polymorphisms in
response to haloperidol. Eur Arch Psychiatry Clin Neurosci. 2013; 263(1): 65-74
Drago A, Giegling I, Schäfer M, Hartmann AM, Möller HJ, De Ronchi D, Stassen HH, Serretti A,
Rujescu D: No association of a set of candidate genes on haloperidol side effects.
PLoS One. 2012; 7(10): e44853
Giegling I, Drago A, Schäfer M, Hartmann AM, Sander T, Toliat MR, Möller HJ, De Ronchi D,
Stassen HH, Rujescu D, Serretti A: Lack of association between 71 variations located in
candidate genes and response to acute haloperidol treatment. Psychopharmacology 2011; 214(3):
719-728
Giegling I, Drago A, Dolzan V, Plesnicar BK, Schäfer M, Hartmann AM, Sander T, Toliat MR,
Möller HJ, Stassen HH, Rujescu D, Serretti A: Glutamatergic gene variants impact the clinical
profile of efficacy and side effects of haloperidol. Pharmacogenet Genomics. 2011; 21(4): 206-216
Gravemann S, Schnipper N, Meyer H, Vaya A, Nowaczyk MJM, Rajab A, Hofmann WK, Salewsky B,
Tönnies H, Neitzel H, Stassen HH, Sperling K, Hoffmann K: Dosage effect of zero to three
functional LBR-genes in vivo and in vitro. Nucleus 2010; 1(2): 1-12
Hoffmann K, Planitz C, Rüschendorf F, Müller-Myhsok B, Stassen HH, Lucke B, Mattheisen M,
Stumvoll M, Bochmann R, Zschornack G, Wienker TF, Nürnberg P, Reis A, Luft FC, Lindner TH: A
novel locus for arterial hypertension on chromosome 1p36 maps to a metabolic syndrome trait
cluster in the Sorbs, a Slavic population isolate in Germany. J Hypertens 2009; 27: 983-990
Stassen HH, Hoffmann K, Scharfetter C: The Difficulties of Reproducing Conventionally Derived
Results through 500k-Chip Technology. BMC Genet Proc. 2009; 3 Suppl 7: S66
Tadic A, Rujescu D, Dahmen N, Stassen HH, Muller MJ, Kohnen R, Szegedi A: Association
Analysis between Variants of the Interleukin-1? and the Interleukin-1 Receptor Antagonist
Gene and Antidepressant Treatment Response in Major Depression. Neuropsychiatr Dis Treat
2008; 4(1): 269-276
Stassen HH, Angst J, Hell D, Scharfetter C, Szegedi A: Is there a common resilience mechanism
underlying antidepressant drug response? Evidence from 2'848 patients. J Clin Psychiatry 2007;
68(8): 1195-1205
Tadic A, Rujescu D, Müller MJ, Kohnen R, Stassen HH, Dahmen N, Szegedi A: A monoamine
oxidase B gene variant and short-term antidepressant treatment response. Prog
Neuropsychopharmacol Biol Psychiatry. 2007; 31(7): 1370-1377
Tadic A, Müller MJ, Rujescu D, Kohnen R, Stassen HH, Dahmen N, Szegedi A: The MAOA
T941G polymorphism and short-term treatment response to mirtazapine and paroxetine in
major depression. Am J Med Genet B Neuropsychiatr Genet. 2007; 144(3): 325-331
Stassen HH, Szegedi A, Scharfetter C: Modeling Activation of Inflammatory Response System.
A Molecular-Genetic Neural Network Analysis. BMC Proceedings 2007, 1 (Suppl 1): S61, 1-6
Berger M, Stassen HH, Köhler K, Krane V, Mönks D, Wanner C, Hoffmann K, Hoffmann MM, Zimmer M,
Bickeböller H, Lindner TH: Hidden population substructures in an apparently homogeneous
population bias association studies. Eur J Hum Genetics 2006; 14: 236-244
Stassen HH, Angst J, Scharfetter C, Szegedi A: Therapie mit Antidepressiva: Erfolg von
genetischen Faktoren abhängig? Leading Opinions, Neurologie & Psychiatrie 2005; 6: 25-27
Szegedi A, Rujescu D, Tadic A, Müller MJ, Ralf Kohnen R, Stassen HH, Dahmen N: The
catechol-O-methyltransferase Val108/158Met-polymorphism affects short-term treatment response
to mirtazapine, but not to paroxetine in Major Depression. Pharmacogenomics 2005; 5(1): 49-53
Stassen HH, Bridler R, Hell D, Weisbrod M, Scharfetter C: Ethnicity-independent genetic basis
of functional psychoses. A Genotype-to-phenotype approach. Am J Med Genetics B 2004; 124:
101-112
Stassen HH, Hoffmann K, Scharfetter C: Similarity by state/descent and genetic vector spaces:
Analysis of a longitudinal family study. Genetic Analysis Workshop 13: Analysis of longitudinal
family data for complex diseases and related risk factors. BMC Genet 2003; 4, S59: 1-6
Stassen HH, Scharfetter C: Oligogenic approaches to the predisposition of asthma in ethnically
diverse populations. Genetic Analysis Workshop 12: Analysis of genetic and environmental factors
in common diseases. Genetic Epidemiology 2001; 21(1): 284-289
Hoffmann K, Stassen HH, Reis A: Genkartierung in Isolatpopulationen. Medizinische Genetik 2000;
12,4: 428-437
Stassen HH, Bridler R, Hägele S, Hergersberg M, Mehmann B, Schinzel A, Weisbrod M, Scharfetter C:
Schizophrenia and smoking: evidence for a common neurobiological basis?
Am J Med Genetics B 2000; 96: 173-177
Stassen HH and Scharfetter C: Integration of genetic maps by polynomial transformations.
Am J Med Genetics B 2000; 96: 108-113
Stassen HH, Begleiter H, Porjesz B, Rice J, Scharfetter C, Reich T: Structural decomposition of
genetic diversity in families with alcohol dependence. Genetic Analysis Workshop 11: Analysis
of genetic and environmental factors in common diseases. Genetic Epidemiology 1999; 17:
325-330
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Principal schema of a multilayer Neural Net (NN) where response to treatment and unwanted side effects (output) is predicted from multiple gene vectors (input) connected to each other by complex interactions via one or more "hidden" layer(s). The NN algorithm iteratively constructs a model that is simultaneously fitted to the observed data of all patients. The achievable goodness of fit depends on the information included, the quality of underlying data, and the number of intermediate layers implemented to model nonlinear interactions. |
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