title: Physiogenomic analysis of CYP450 drug metabolism correlates dyslipidemia with pharmacogenetic functional status in psychiatric patients.
abstract: OBJECTIVE
  To investigate associations between novel human cytochrome P450 (CYP450) combinatory (multigene) and substrate-specific drug metabolism indices, and elements of metabolic syndrome, such as low density lipoprotein cholesterol (LDLc), high density lipoprotein cholesterol (HDLc), triglycerides and BMI, using physiogenomic analysis.
  
  
  METHODS
  CYP2C9, CYP2C19 and CYP2D6 genotypes and clinical data were obtained for 150 consecutive, consenting hospital admissions with a diagnosis of major depressive disorder and who were treated with psychotropic medications. Data analysis compared clinical measures of LDLc, HDLc, triglyceride and BMI with novel combinatory and substrate-specific CYP450 drug metabolism indices.
  
  
  RESULTS
  We found that a greater metabolic reserve index score is related to lower LDLc and higher HDLc, and that a greater metabolic alteration index score corresponds with higher LDLc and lower HLDc values. We also discovered that the sertraline drug-specific indices correlated with cholesterol and triglyceride values.
  
  
  CONCLUSIONS
  Overall, we demonstrated how a multigene approach to CYP450 genotype analysis yields more accurate and significant results than single-gene analyses. Ranking the individual with respect to the population represents a potential tool for assessing risk of dyslipidemia in major depressive disorder patients who are being treated with psychotropics. In addition, the drug-specific indices appear useful for modeling a variable of potential relevance to an individual's risk of drug-related dyslipidemia.
authors.affiliation: Genomas Inc., Hartford, CT 06106, USA. gruano@harthosp.org
authors.forename: Gualberto
authors.initials: G
authors.lastname: Ruaño
authors.forename: David
authors.initials: D
authors.lastname: Villagra
authors.forename: Bonnie
authors.initials: B
authors.lastname: Szarek
authors.forename: Andreas
authors.initials: A
authors.lastname: Windemuth
authors.forename: Mohan
authors.initials: M
authors.lastname: Kocherla
authors.forename: Krystyna
authors.initials: K
authors.lastname: Gorowski
authors.forename: Christopher
authors.initials: C
authors.lastname: Berrezueta
authors.forename: Harold I
authors.initials: HI
authors.lastname: Schwartz
authors.forename: John
authors.initials: J
authors.lastname: Goethe
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001189:Aryl Hydrocarbon Hydroxylases / Q000235:genetics / Q000378:metabolism; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D015331:Cohort Studies; D065731:Cytochrome P-450 CYP2C19; D065729:Cytochrome P-450 CYP2C9; D019389:Cytochrome P-450 CYP2D6 / Q000235:genetics / Q000378:metabolism; D003577:Cytochrome P-450 Enzyme System / Q000235:genetics / Q000378:metabolism*; D003865:Major Depressive Disorder / Q000150:complications / Q000188:drug therapy / Q000378:metabolism; D050171:Dyslipidemias / Q000150:complications / Q000378:metabolism*; D005260:Female; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D011619:Psychotropic Drugs / Q000378:metabolism* / Q000627:therapeutic use; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D011619:Psychotropic Drugs; D014280:Triglycerides; D003577:Cytochrome P-450 Enzyme System; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; D001189:Aryl Hydrocarbon Hydroxylases; C045793:CYP2C19 protein, human; D065731:Cytochrome P-450 CYP2C19; D019389:Cytochrome P-450 CYP2D6