Associations of Antipsychotic Doses with Dysregulated Rest-Activity Rhythms in People with Chronic Schizophrenia

Abstract

<h3>Introduction</h3> Peoplewith schizophrenia (PwS) have a shortened lifespan by 15 to 20 years, primarily due to metabolic disturbances and cardiovascular disease. Increased metabolic risk in PwS is partly due to sedentary lifestyles, unhealthy diets, inadequate healthcare, and side effects from antipsychotics. Dysregulated rest-activity rhythms (RARs) are a key risk factor for metabolic health that are common yet understudied in PwS and could be a potential treatment target. This study aims to objectively assess and compare RAR variables in PwS and a non-psychiatric comparison group (NCs). We also examined how RAR variables were associated with sociodemographic factors and daily antipsychotic dose. We hypothesized that PwS would have more dysregulated RAR measures than NCs. Among PwS, we explored how antipsychotic dose was associated with RAR variables. <h3>Methods</h3> Studyparticipants included 26 PwS (DSM-IV-TR criteria) and 36 NCs from the greater San Diego Area. We collected sociodemographic variables, clinical data (e.g., antipsychotic dose as defined by the WHO, symptom severity, physical well-being measured by the 36-Item Short Form Survey or SF-36), and subjective sleep quality (Pittsburgh Sleep Quality Index or PSQI). Participants wore wrist-worn actigraphy devices for up to 7 days to record 24-hour sleep-wake activity patterns. We analyzed the raw data using non-parametric and extended-cosine modeling approaches (RAR variables included: interdaily stability [IS], interdaily variability [IV], relative amplitude [RA], most active 10 hours [M10], least active 5 hours [L5], peak activity time [acrophase], active period start-time [t-left], period start-time [t-right], and relative width of active to rest periods [alpha]). We compared sociodemographic, physical well-being, and subjective sleep between PwS and NCs using independent sample t-tests and chi-square tests. Among the PwS group, we used general linear models to examine associations of antipsychotic doses, age, race, sex, and education with RAR variables. <h3>Results</h3> Meanage and % female of PwS and NCs were similar to each other (51.4 years vs. 53.0 years and 50% vs. 53.8%, respectively). PwS were 34.6% of non-Caucasian ethnicity, and PwS were 19.4% of non-Caucasian ethnicity. PwS had worse physical well-being (p=0.004) and poorer sleep quality (p=0.004) compared to NCs. Among PwS, 91.3% of the group was currently taking atypical antipsychotic medications. The PwS group had significantly higher alpha (mean -0.3 [SD 0.3] vs. mean 0.8 [SD 0.2], t(60) = 3.03, p=0.004, d=0.75), lower RA (mean 0.8 [SD 0.3] vs. mean 0.9 [SD 0.2], t(60) = -2.18, p=0.03, d=-0.53), and lower M10 (mean 2160 [SD 816] vs. mean 2650 [SD 673], t(60) = -2.58, p=0.01, d=-0.65), compared to the NCs. AmongPwS, female sex was associated with lower alpha (B=0.262, p=0.024, η_p²=0.038) and having earlier t_right (B=-2.905, p=0.029, η_p²=0.216). Among PwS, Caucasian ethnicity was associated with higher RA (B=0.15, p=0.028, η_p²=0.22) and lower L5 (B=-272.814, p=0.015, η_p²=0.263). Higher antipsychotic doses were associated with lower IS (B=-0.027, p=0.036, η_p²=0.203). <h3>Conclusions</h3> Wefound that RAR variables differed between PwS and NCs, and that among PwS, higher antipsychotic doses were associated with less day-to-day stability of RARs. This finding is consistent with two other studies that showed antipsychotic exposure may be related to decreased motor activity. The impact of antipsychotics on dysregulated RARs suggests a possible mechanistic pathway for antipsychotic-mediated metabolic risk. Limitations of the study include the modest sample size and predominant use of atypical antipsychotic medications. Further understanding of the impact of antipsychotic medications on RARs could help to develop and tailor interventions to mitigate antipsychotic-related side effects and improve health among PwS. <h3>This research was funded by</h3> This work was supported, in part, by the National Institute of Mental Health [an R01 grant (R01MH094151-01 to Dilip Jeste [PI]), and a K23 grant (K23 MH119375-01 to EEL [PI], National Institute on Aging T35 grant AG26757 (PI: Dilip V. Jeste, MD and Alison Moore, MD, MPH), and National Institutes of Health Grant NIH UL1TR001442 of CTSA (PI: Gary Firestein, MD), as well as the Stein Institute for Research on Aging at the University of California San Diego. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Aside from the funding sources listed above, the authors have no conflicts of interest to report.