Glutathione (GSH), a key antioxidant, has been increasingly studied in relation to neurocognition and clinical severity in psychosis. Previous evidence suggests that alterations in GSH levels may contribute to cognitive deficits, symptom severity, and overall functional outcomes. This meta-analysis aims to synthesize available correlations between GSH measures—derived from both neuroimaging (MRS) and peripheral blood samples—and a range of cognitive domains as well as global clinical impressions in individuals with psychosis.
We focus on the following cognitive domains and related outcomes:
Imaging-Based Domains: - Ideational Fluency - Processing Speed - Verbal Memory - Functioning
Total GSH (GSHt) Domains: - Executive Function - Global Cognitive Score - Processing Speed - Cognitive Flexibility - Working Memory - Clinical Global Impression (CGI) - Functioning
Reduced GSH (GSHr) Domains: - Working Memory - Global Cognitive Score - Executive Function - Verbal Memory - Processing Speed - Clinical Global Impression (CGI)
By integrating data from multiple studies, we aim to identify which domains show consistent associations with GSH levels and to what extent these associations may inform our understanding of underlying oxidative stress mechanisms in psychosis.
Studies reporting correlation coefficients between GSH (imaging- or blood-derived) and cognitive or clinical outcomes in psychosis populations were included. Correlation coefficients (r) and sample sizes (n) were extracted. Fisher’s z-transformation was used to normalize correlation coefficients for meta-analysis.
metafor package in R.tidyverse for data manipulation.metafor for meta-analysis computations.fisherz <- function(r) {
0.5 * log((1+r)/(1-r))
}
fisherz2r <- function(z) {
(exp(2*z)-1)/(exp(2*z)+1)
}
prep_data <- function(df) {
df %>%
mutate(zi = fisherz(r),
vi = 1/(n-3))
}
run_meta <- function(df, domain_name) {
df_prep <- prep_data(df)
if(nrow(df_prep) < 2) {
warning(paste("Only one study or no data for", domain_name))
return(NULL)
}
res <- rma(yi = zi, vi = vi, data = df_prep, method = "REML")
pred <- predict(res, transf = fisherz2r)
I2 <- max(0, 100 * (res$tau2 / (res$tau2 + median(1/df_prep$vi))))
tibble(
Domain = domain_name,
k = res$k,
Pooled_r = round(pred$pred, 3),
CI_LB = round(pred$ci.lb, 3),
CI_UB = round(pred$ci.ub, 3),
p_value = round(res$pval,4),
I2 = paste0(round(I2,1), "%")
)
}(Ensure the data below is accurate and uses periods instead of commas. The data comes from the previously determined tables.)
# Imaging-Based Domains
imaging_ideational <- data.frame(
authors = c("Matsuzawa et al. 2008","Coughlin et al. 2021"),
r = c(0.21,0.61),
n = c(36,16)
)
imaging_processing <- data.frame(
authors = c("Matsuzawa et al. 2008","Coughlin et al. 2021"),
r = c(-0.14,0.26),
n = c(36,16)
)
imaging_verbal <- data.frame(
authors = c("Matsuzawa et al. 2008","Coughlin et al. 2021"),
r = c(0.18,0.12),
n = c(36,16)
)
imaging_functioning <- data.frame(
authors = c("Lesh et al. 2021","Mackinley et al. 2022","Ravanfar et al. 2022"),
r = c(0.185,0.04,0.452),
n = c(33,53,12)
)
# GSHt (Total)
gsht_executive <- data.frame(
authors = c("Martinez-Cengotitobengoa et al. 2012","Gonzalez-Liencres et al. 2014","Coughlin et al. 2021","Gares-Caballer et al. 2022"),
r = c(0.072,0.171,0.45,0.40),
n = c(28,41,24,30)
)
gsht_global <- data.frame(
authors = c("Nucifora et al. 2017","Coughlin et al. 2021","Gares-Caballer et al. 2022"),
r = c(0.245,0.57,0.34),
n = c(51,24,30)
)
gsht_processing <- data.frame(
authors = c("Gonzalez-Liencres et al. 2014","Coughlin et al. 2021","Gares-Caballer et al. 2022","Lin et al. 2023","Lin et al. 2023"),
r = c(-0.119,0.41,0.21,-0.172,0.045),
n = c(41,24,30,92,219)
)
gsht_flexibility <- data.frame(
authors = c("Gonzalez-Liencres et al. 2014","Coughlin et al. 2021","Lin et al. 2023","Lin et al. 2023"),
r = c(0.2,-0.06,-0.059,0.054),
n = c(41,24,92,219)
)
gsht_workingmem <- data.frame(
authors = c("Lin et al. 2023","Lin et al. 2023"),
r = c(-0.094,-0.027),
n = c(92,219)
)
gsht_cgi <- data.frame(
authors = c("Raffa et al. 2009","Gares-Caballer et al. 2022"),
r = c(-0.28,-0.08),
n = c(88,30)
)
gsht_functioning <- data.frame(
authors = c("Lin et al. 2023","Lin et al. 2023"),
r = c(-0.107,0.076),
n = c(92,219)
)
# GSHr (Reduced)
gshr_workingmem <- data.frame(
authors = c("Cruz et al. 2021","Piatoikina et al. 2021"),
r = c(-0.041,-0.003),
n = c(85,125)
)
gshr_global <- data.frame(
authors = c("Guidara et al. 2020","Cruz et al. 2021"),
r = c(0.118,-0.092),
n = c(66,85)
)
gshr_executive <- data.frame(
authors = c("Cruz et al. 2021","Piatoikina et al. 2021"),
r = c(-0.114,0.043),
n = c(85,125)
)
gshr_verbal <- data.frame(
authors = c("Cruz et al. 2021","Piatoikina et al. 2021"),
r = c(0,0.02),
n = c(85,125)
)
gshr_processing <- data.frame(
authors = c("Cruz et al. 2021","Piatoikina et al. 2021"),
r = c(0.038,0.03),
n = c(85,125)
)
gshr_cgi <- data.frame(
authors = c("Raffa et al. 2009","Ballesteros et al. 2013"),
r = c(-0.32,0.208), # Assumed corrected correlation
n = c(88,54)
)domain_list <- list(
"Imaging-Ideational Fluency" = imaging_ideational,
"Imaging-Processing Speed" = imaging_processing,
"Imaging-Verbal Memory" = imaging_verbal,
"Imaging-Functioning" = imaging_functioning,
"GSHt-Executive" = gsht_executive,
"GSHt-Global Cognitive Score" = gsht_global,
"GSHt-Processing Speed" = gsht_processing,
"GSHt-Cognitive Flexibility" = gsht_flexibility,
"GSHt-Working Memory" = gsht_workingmem,
"GSHt-CGI" = gsht_cgi,
"GSHt-Functioning" = gsht_functioning,
"GSHr-Working Memory" = gshr_workingmem,
"GSHr-Global Cognitive Score" = gshr_global,
"GSHr-Executive Function" = gshr_executive,
"GSHr-Verbal Memory" = gshr_verbal,
"GSHr-Processing Speed" = gshr_processing,
"GSHr-CGI" = gshr_cgi
)
results <- bind_rows(lapply(names(domain_list), function(dn) {
df <- domain_list[[dn]]
run_meta(df, dn)
}))
results %>%
kable("html", caption="Meta-Analysis Results for All Cognitive Domains") %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped","hover"))| Domain | k | Pooled_r | CI_LB | CI_UB | p_value | I2 |
|---|---|---|---|---|---|---|
| Imaging-Ideational Fluency | 2 | 0.392 | -0.063 | 0.712 | 0.0889 | 0.3% |
| Imaging-Processing Speed | 2 | 0.005 | -0.360 | 0.370 | 0.9783 | 0.1% |
| Imaging-Verbal Memory | 2 | 0.163 | -0.124 | 0.425 | 0.2642 | 0% |
| Imaging-Functioning | 3 | 0.134 | -0.073 | 0.330 | 0.2034 | 0% |
| GSHt-Executive | 4 | 0.264 | 0.084 | 0.427 | 0.0044 | 0% |
| GSHt-Global Cognitive Score | 3 | 0.355 | 0.157 | 0.526 | 0.0006 | 0% |
| GSHt-Processing Speed | 5 | 0.032 | -0.145 | 0.206 | 0.7267 | 0.1% |
| GSHt-Cognitive Flexibility | 4 | 0.035 | -0.067 | 0.137 | 0.5003 | 0% |
| GSHt-Working Memory | 2 | -0.047 | -0.158 | 0.065 | 0.4154 | 0% |
| GSHt-CGI | 2 | -0.233 | -0.399 | -0.052 | 0.0119 | 0% |
| GSHt-Functioning | 2 | 0.002 | -0.172 | 0.177 | 0.9791 | 0% |
| GSHr-Working Memory | 2 | -0.018 | -0.154 | 0.118 | 0.7940 | 0% |
| GSHr-Global Cognitive Score | 2 | 0.004 | -0.199 | 0.207 | 0.9664 | 0% |
| GSHr-Executive Function | 2 | -0.023 | -0.174 | 0.129 | 0.7669 | 0% |
| GSHr-Verbal Memory | 2 | 0.012 | -0.125 | 0.148 | 0.8643 | 0% |
| GSHr-Processing Speed | 2 | 0.033 | -0.104 | 0.169 | 0.6351 | 0% |
| GSHr-CGI | 2 | -0.067 | -0.536 | 0.433 | 0.8035 | 0.2% |
Selected Results:
GSHt-Executive (k=4): Pooled r=0.264,
CI[0.084,0.427], p=0.0044
Suggests a small-to-moderate positive association between total GSH and
executive functioning.
GSHt-Global Cognitive Score (k=3): Pooled
r=0.355, CI[0.157,0.526], p=0.0006
Indicates a moderate positive correlation between total GSH and overall
cognitive performance.
GSHt-CGI (k=2): Pooled r=-0.233,
CI[-0.399,-0.052], p=0.0119
Higher GSHt appears modestly related to lower illness severity as
measured by CGI.
All other domains show non-significant results, with confidence intervals overlapping zero and p-values > 0.05. Heterogeneity (I²) is minimal across all analyses, indicating relatively consistent effects within each domain’s included studies.
The findings from this meta-analysis highlight a few key points:
Significant Positive Correlations with Cognitive Outcomes
(GSHt):
Total GSH (GSHt) levels show a notable positive association with
executive function and global cognitive performance. This suggests that
individuals with higher GSHt may exhibit better cognitive functioning,
potentially reflecting the role of oxidative balance in cognition for
those with psychosis.
Association with Clinical Severity
(GSHt-CGI):
The negative correlation with CGI indicates that higher GSHt might be
related to slightly lower overall clinical severity. While the effect is
modest, it points towards the possibility that improving systemic
antioxidant levels could have some beneficial effects on clinical
status.
Non-Significant Relationships for Other
Domains:
Most domains, including imaging-based cognitive outcomes and measures
linked to reduced GSH (GSHr), did not demonstrate statistically
significant correlations. This could indicate that the relationship
between GSH and cognition/clinical outcomes is more specific to certain
GSH measures (e.g., total GSH) or certain cognitive domains (e.g.,
executive function, global cognition).
Minimal Heterogeneity:
The low I² values suggest little variability among studies within each
domain’s analysis. This consistency increases confidence in the observed
patterns, though the small number of studies in some domains reduces
statistical power.
These results provide preliminary evidence that total GSH levels (GSHt) correlate positively with executive functioning and overall cognitive performance and are modestly associated with lower clinical severity (CGI) in psychosis. In contrast, no strong evidence emerged for other domains or for imaging-based GSH measures. Further research with larger sample sizes and standardized methodologies is warranted to clarify these relationships and potentially guide novel therapeutic strategies targeting oxidative stress in psychosis.