Retinal and Choroidal Vascular Metrics Predict Cerebral Atrophy and Cognitive Impairment in Noninfarcted Large Artery Stenosis.

Alongside an increased prevalence of anxiety and major depressive disorder (MDD) in the past decade [, ], rates of antidepressant use have risen, particularly in recent years [, ]. Antidepressant use in the United States is now widespread: more than 1 in 10 adults and adolescents reported recent antidepressant use – a rate that climbs to more than 1 in 5 among women over forty [, ]. The majority (62%) of individuals starting antidepressant monotherapy are prescribed selective serotonin reuptake inhibitors (SSRIs) []. Remission rates following first-line SSRI treatment for MDD are low with less than half of patients responding, and only 36.8% achieving remission []. Within a year of starting SSRI treatment, roughly 80% of patients switch treatments or discontinue pharmacological treatment altogether – only 20% persist for a year with their first SSRI []. After up to four treatment re-evaluations (i.e. opportunities to switch treatments), closer to 70–90% of patients receiving treatment may ultimately achieve remission [], typically requiring a lengthy trial-and-error process. Among patients treated with SSRIs, side effects are a frequent reason to switch treatments []. In short, finding an appropriate antidepressant remains a challenge for a large portion of the US population. Considering the effects of MDD on quality of life [] and its significant economic impact, with costs in the United States exceeding 380 billion USD (2023) annually [], novel approaches to help patients and physicians find appropriate treatments more quickly are needed. Genetics-guided treatment using pharmacogenetic testing is one area of potential promise for antidepressant prescribing. Knowing if a patient has relevant genetic variants and choosing treatments and dosing based on predicted CYP2C19 metabolizer status has shown promise in improving response rates, remission rates [], and time to titration []. While randomized clinical trials typically find a consistent, positive effect of pharmacogenetics-guided MDD treatment on the odds of remission, obtaining sufficient power to detect statistically significant effects remains an issue, particularly over longer treatment periods, where data availability is more limited [, ]. Previous studies have been underpowered to detect associations between CYP2C19 metabolizer status and specific SSRI-related side effects. In large genotyped or sequenced cohorts, such as UK Biobank, data on SSRI-related side effects and efficacy remain sparse. Recent studies have typically pooled side effects to maximize statistical power, oftentimes still not detecting effects of CYP2C19 genotype (or the metabolizer status predicted from it) on drug response. Wong et al. [] observed no associations between CYP2C19 status and escitalopram discontinuation, and no associations with citalopram that survived multiple testing correction. Other recent work does not appear powered to detect associations between CYP2C19 metabolizer status and citalopram or escitalopram side effects (when pooled), or with the efficacy of any single SSRI [].