Lipid disturbances induced by psychotropic drugs: clinical and genetic predictors for early worsening of lipid levels and new-onset dyslipidaemia in Swiss psychiatric samples
Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland; and Les Toises Psychiatry and Psychotherapy Center, Lausanne, Switzerland
Marie Sadler
Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland; and Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
Franziska Gamma
Les Toises Psychiatry and Psychotherapy Center, Lausanne, Switzerland
Martin Preisig
Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
Hélène Richard-Lepouriel
Unit of Mood Disorders, Department of Psychiatry, Geneva University Hospital, Geneva, Switzerland
Armin von Gunten
Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Switzerland
Philippe Conus
Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Switzerland
Kerstin Jessica Plessen
Division of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
Zoltan Kutalik
Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland; and Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
Chin B. Eap
Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland; Center for Research and Innovation in Clinical Pharmaceutical Sciences, University of Lausanne, Lausanne, Switzerland; School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; and Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
Background Early worsening of plasma lipid levels (EWL; ≥5% change after 1 month) induced by at-risk psychotropic treatments predicts considerable exacerbation of plasma lipid levels and/or dyslipidaemia development in the longer term. Aims We aimed to determine which clinical and genetic risk factors could predict EWL. Method Predictive values of baseline clinical characteristics and dyslipidaemia-associated single nucleotide polymorphisms (SNPs) on EWL were evaluated in a discovery sample (n = 177) and replicated in two samples from the same cohort (PsyMetab; n1 = 176; n2 = 86). Results Low baseline levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C) and triglycerides, and high baseline levels of high-density lipoprotein cholesterol (HDL-C), were risk factors for early increase in total cholesterol (P = 0.002), LDL-C (P = 0.02) and triglycerides (P = 0.0006), and early decrease in HDL-C (P = 0.04). Adding genetic parameters (n = 17, 18, 19 and 16 SNPs for total cholesterol, LDL-C, HDL-C and triglycerides, respectively) improved areas under the curve for early worsening of total cholesterol (from 0.66 to 0.91), LDL-C (from 0.62 to 0.87), triglycerides (from 0.73 to 0.92) and HDL-C (from 0.69 to 0.89) (P ≤ 0.00003 in discovery sample). The additive value of genetics to predict early worsening of LDL-C levels was confirmed in two replication samples (P ≤ 0.004). In the combined sample (n ≥ 203), adding genetics improved the prediction of new-onset dyslipidaemia for total cholesterol, LDL-C and HDL-C (P ≤ 0.04). Conclusions Clinical and genetic factors contributed to the prediction of EWL and new-onset dyslipidaemia in three samples of patients who started at-risk psychotropic treatments. Future larger studies should be conducted to refine SNP estimates to be integrated into clinically applicable predictive models.
