Characterizing AntiDepressant Use
One in five people will be diagnosed with major depressive disorder (MDD) in their lifetime, and approximately one third of those are refractory to treatment. A range of pharmacological and psychological treatments for depression exist, but MDD is primarily managed with antidepressants and Cognitive Behavioural Therapy (CBT). However, across these treatment types, outcomes are variable and many individuals do not experience any remission of symptoms1-3 or cease treatment due to adverse side-effects4.
Several clinical, treatment and patient characteristics have been associated with poor depression treatment response or adverse side-effects. These include factors such as: a longer duration of illness5,6, earlier age of onset7, greater symptom severity8, lower levels of patient engagement9, lower educational attainment10,11, and childhood maltreatment7. However, despite identifying a large number of factors associated with treatment outcomes, prediction of individual treatment response is still poor12,13 with most variables having a have low sensitivity to distinguish between those who will and will not improve from certain types of treatment.
It is hypothesized that a proportion of individual differences in treatment response, much like responses to other environmental experiences, is due to a heritable component14, but collecting data from family studies to estimate this is difficult (studies estimating heritability are reviewed in14).
Analysis of genome-wide genetic data has estimated that 20-42% of the variance in remission was found to be attributable to common genetic variation (SNP-based heritability). Notably, these estimates are derived from a meta-analytic sample, but still the total sample size was ~5,000 individuals15,16. Despite these sizable estimates, genome-wide association study analyses have had limited success in identifying associated genetic variants, suggesting that treatment response is likely a complex polygenic trait. Notably, these studies have meta-analysed data from multiple cohorts where the response to treatment phenotypes may not be well matched. Larger study samples with response to treatment defined in a standardised way are needed.
Data sets informative for the investigation of the genetic contribution to anti-depressant response are limited worldwide. The large sample size, detailed phenotyping, family structure, genome-wide genetic data, and the linkage to clinical records through Pharmlines make the Lifelines cohort a unique resource to investigate the genetic contribution to response to antidepressants.
Relevant Prior work from Lifelines:
Van Loo17 has studied the Lifelines cohort in detail for depression phenotypes N= 146,315 subjects five current (as opposed to lifetime) internalizing disorders – major depression (2.0%), dysthymia (1.0%), generalized anxiety disorder (3.7%),social phobia (0.8%), and panic disorder (0.2%). Lifetime depression has only be assessed in a subset of Lifelines participants and so the Pharmlines database which tracks pharmaceutical dispense data will be most useful for this study.
Relevant Prior work from Pharmlines:
The PharmLines assessment of the concordance of self-report and pharmaceutical dispense data for Anatomical Therapeutic Class (ATC) N06 (which includes antidepressants) was relatively high (kappa 0.79), and the kappa was also 0.79 for the specific SSRI drug of paroxetine18.
A Pharmlines study19 (led by co-applicant Prof Hak) focused on the SSRI (es)citalopram investigating gene and haplotype interactions for the drug metabolism genes CYP2C19 and CYP3A4. Drug-drug interactions (which we note as a complicating factor). At that time only 15K Lifelines participants had genotype data, with 316 (es)citalopram users. The study showed The CYP2C19 genotype IM/PM and CYP3A4 NM genotype combination increased risks of switching and/or dose reduction (OR: 2.75, 95% CI: 1.03–7.29), although the sample size was too small to draw strong conclusions.
In this study, the index date for an individual was the first date of (es)citalopram use recorded in the PharmLines database.
Drug switching was defined as patients having an early discontinuation of (es)citalopram as well as the prescription of another antidepressant, regardless of the class, within 120 days after the index date.
Dose adjustment was defined as having a dose reduction or a dose elevation for at least 25% of the first dose within 90 days after the index date.
Early discontinuation was defined as discontinuing the prescription of (es)citalopram within 90 days after the index date, having no further re-prescription of (es)citalopram for at least 180 days after the stop date as well as no switching.
SSRI drug modulators and clinically relevant CYP2C19/3A4/2D6 modulators was based on Commentaren Medicatiebewaking (Health Base, NL) and the Flockhart tableTM (see their Supplementary S1).
Prior work by the investigators.
Wray (together with collaborators eg Brittany Mitchell) have used the Australian Genetics of Depression Study20 (~12K MDD cases with GWAS data) to identify participants whose pharmaceutical benefits scheme records showing
a) a high number of repeated prescriptions of the same antidepressants (with focus on SSRI & SNRI)
b) many different types of antidepressants (and self-report of advice for ECT – electroconvulsive therapy associated with treatment resistance and/or lithium prescription associated with bipolar depression).
AGDS participants have also provided self-report data on side effects and response. We are developing a questionnaire to probe the specific domains in which anti-depressants work.
The definition of the phenotype criteria is an active area of research, and once derived will be used in genetic analyses.