Biok study: biomarker analysis
Stephen Murata
12/15/2021
1 Sample characteristics (raw variables)
compareGroups::descrTable(~.
, Biok_vert_df_raw,
hide.no = '0',
show.p.overall = FALSE,
include.label = TRUE)##
## --------Summary descriptives table ---------
##
## _____________________________________________________________
## [ALL] N
## N=218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 218
## male 82 (37.6%)
## female 136 (62.4%)
## age 44.3 (12.9) 218
## BMI 28.6 (5.58) 218
## race: 218
## asian 3 (1.38%)
## black 4 (1.83%)
## white 211 (96.8%)
## patientno 1016 (1365) 218
## Site_Location: 218
## Johns Hopkins 22 (10.1%)
## Univeristy of Michigan 69 (31.7%)
## Mayo Clinic 114 (52.3%)
## Pine Rest 13 (5.96%)
## infusionno: 218
## BL 73 (33.5%)
## 1st 72 (33.0%)
## 3rd 73 (33.5%)
## Blood_Draw_Event: 218
## Acute Infusion #1 Baseline 100 49 (22.5%)
## Acute Infusion #1 Baseline 40 24 (11.0%)
## Acute Infusion #1 Stop 100 48 (22.0%)
## Acute Infusion #1 Stop 40 24 (11.0%)
## Acute Infusion #3 Stop 100 28 (12.8%)
## Acute Infusion #3 Stop 40 45 (20.6%)
## Batch_Number 3.51 (1.71) 218
## BSS_Score 4.57 (6.82) 213
## MADRS_Score 17.0 (10.6) 217
## Remission: 215
## No remission 77 (35.8%)
## Remitter 138 (64.2%)
## TRP_nM 25449 (5942) 218
## five_HT_nM 205 (284) 218
## KYN_nM 917 (244) 218
## three_HK_nM 16.2 (7.44) 218
## KYNA_nM 19.5 (7.68) 218
## PIC_nM 19.2 (13.5) 218
## Quin_nM 146 (43.0) 218
## AA_nM 5.95 (3.12) 218
## KYN_TRP_ratio 0.04 (0.01) 218
## KYN_SER_ratio 33.2 (72.1) 218
## QUIN_PIC_ratio 9.14 (4.39) 218
## QUIN_KYNA_ratio 8.30 (3.40) 218
## threeHK_KYN_ratio 0.02 (0.01) 218
## threeHK_KYNA_ratio 0.91 (0.44) 218
## IL1B_pg_mL 0.28 (0.47) 42
## IL1B_pg_mL_LLOD 0.34 (0.19) 218
## IL2_pg_mL 1.27 (6.00) 152
## IL2_pg_mL_LLOD 0.92 (5.03) 218
## IL4_pg_mL 0.09 (0.03) 217
## IL4_pg_mL_LLOD 0.09 (0.03) 218
## IL6_pg_mL 1.04 (0.58) 218
## IL8_pg_mL 4.36 (1.66) 218
## IL10_pg_mL 0.64 (1.95) 218
## IL12p70_pg_mL 21.8 (186) 217
## IL12p70_pg_mL_LLOD 21.7 (185) 218
## IL13_pg_mL 5.07 (11.5) 215
## IL13_pg_mL_LLOD 5.01 (11.4) 218
## TNFa_pg_mL 1.44 (0.46) 218
## IFNy_pg_mL 6.97 (5.50) 218
## CRP_ng_mL 2835 (4774) 218
## NIC_nM 41.4 (340) 218
## NIC_nM_LLOD 41.4 (340) 218
## NTA_nM 306 (153) 218
## SAA_ng_mL 2870 (2145) 218
## VCAM_1_ng_mL 309 (67.9) 218
## ICAM_1_ng_mL 308 (82.3) 218
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯2 Sample missingness counts
missingTable(compareGroups(infusionno~., data=Biok_vert_df_raw))##
## --------Missingness table by 'infusionno'---------
##
## _____________________________________________________________
## BL 1st 3rd p.overall
## N=73 N=72 N=73
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## age 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## BMI 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## race 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## patientno 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Site_Location 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Blood_Draw_Event 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Sample_ID 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Batch_Number 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## BSS_Score 0 (0.00%) 2 (2.78%) 3 (4.11%) 0.291
## MADRS_Score 0 (0.00%) 0 (0.00%) 1 (1.37%) 1.000
## Remission 1 (1.37%) 1 (1.39%) 1 (1.37%) 1.000
## TRP_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## five_HT_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## three_HK_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYNA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## PIC_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## Quin_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## AA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_TRP_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## KYN_SER_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## QUIN_PIC_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## QUIN_KYNA_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## threeHK_KYN_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## threeHK_KYNA_ratio 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL1B_pg_mL 57 (78.1%) 61 (84.7%) 58 (79.5%) 0.565
## IL1B_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL2_pg_mL 28 (38.4%) 20 (27.8%) 18 (24.7%) 0.168
## IL2_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL4_pg_mL 0 (0.00%) 1 (1.39%) 0 (0.00%) 0.330
## IL4_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL6_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL8_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL10_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL12p70_pg_mL 0 (0.00%) 1 (1.39%) 0 (0.00%) 0.330
## IL12p70_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IL13_pg_mL 1 (1.37%) 2 (2.78%) 0 (0.00%) 0.327
## IL13_pg_mL_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## TNFa_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## IFNy_pg_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## CRP_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NIC_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NIC_nM_LLOD 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## NTA_nM 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## SAA_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## VCAM_1_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## ICAM_1_ng_mL 0 (0.00%) 0 (0.00%) 0 (0.00%) .
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯3 Outcome inspection - Depressive severity (MADRS_Score)
mu <- ddply(Biok_vert_df, "infusionno", summarise, grp.mean=mean(MADRS_Score))
ggplot(Biok_vert_df, aes(x=MADRS_Score))+
geom_histogram(color="black", fill="orange")+
facet_grid(infusionno ~ .)+
theme(legend.position="none")+
geom_vline(data=mu, aes(xintercept=grp.mean, color=infusionno),linetype="dashed")+
labs(title="Distribution of MADRS total score by Ketamine infusion (timepoint)", x="Depressive severity (MADRS)", y="Count")+
theme_gray()
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = as.numeric(infusionno), y = MADRS_Score)) +
geom_boxplot(aes(group = infusionno), lwd=1.25, fatten=1, outlier.shape = "triangle", outlier.size = 3 ) +
ggtitle("Depression by Ketamine Infusion (timeseries)")+
geom_line(aes(group=patientno, color=Remission)) +
geom_point(aes(color=Remission))+
labs(x = "infusionno") +
scale_x_continuous(breaks = 1:3)
my_comparisons <- list( c("BL", "1st"), c("1st", "3rd"), c("BL", "3rd") )
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = infusionno, y = MADRS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", ref.group="BL", comparisons=my_comparisons)+
ggpubr::stat_compare_means(method="anova", label.y=70)
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = Remission, y = MADRS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~infusionno)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", label.y=50)
4 Outcome inspection - suicidality (BSS_Score)
mu <- ddply(Biok_vert_df, "infusionno", summarise,grp.mean=mean(log(BSS_Score)))
ggplot(Biok_vert_df, aes(x=log(BSS_Score)))+
geom_histogram(color="black", fill="orange")+
facet_grid(infusionno ~ .)+
theme(legend.position="none")+
geom_vline(data=mu, aes(xintercept=grp.mean, color=infusionno),linetype="dashed")+
labs(title="Distribution of BSS total score by Ketamine infusion (timepoint)", x="Suicidal severity (BSS)", y="Count")+
theme_gray()
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = infusionno, y = BSS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~Remission)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means(method="t.test", ref.group="BL", comparisons=my_comparisons)+
ggpubr::stat_compare_means( label.y=40)
Biok_vert_df %>%
filter(!is.na(Remission)) %>%
ggplot(aes(x = Remission, y = BSS_Score))+
geom_boxplot(aes(fill=Remission))+
geom_jitter(width = 0.1)+
facet_wrap(~infusionno)+
theme_bw()+
theme(legend.position = "none")+
ggpubr::stat_compare_means( label.y=30)
5 Q-Q Plots: biomarker (reduced model, untransformed)
for (x in Biok_biomarkers) {
LM1 <- lme4::lmer(substitute(i ~ sex+age+BMI+race+(1|patientno), list(i = as.name(x))), data = Biok_vert_df)
car::qqPlot(residuals(LM1), main=x)
}
6 Q-Q Plots: biomarker (log transformed) reduced models
qq_df_log<-Biok_vert_df %>% select(all_of(Biok_biomarkers_log)) %>% select(-NIC_nM_log) %>% names
for (x in qq_df_log) {
LM2 <- lm(substitute(i ~ sex+age+BMI+race, list(i = as.name(x))), data = Biok_vert_df)
car::qqPlot(residuals(LM2), main=x)
}
Despite log transform, the following biomarkers remain abnormally distributed: 5HT, KYN/5HT, IL1B, IL2, IL4, IL6, IL10, IL12, IL13, IFNy, CRP Note: Nic_nM_log was removed due to undefined with log Robust regressions may be necessary downstream…
7 Univariate screen (baseline) by dichotomous outcome
compareGroups::descrTable(Remission~.
, Vert,
include.miss=TRUE,
hide.no = '0',
subset=infusionno=="BL",
show.ratio = TRUE)##
## --------Summary descriptives table by 'Remission'---------
##
## ___________________________________________________________________________________
## No remission Remitter OR p.ratio p.overall
## N=26 N=46
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 0.431
## male 7 (26.9%) 18 (39.1%) Ref. Ref.
## female 19 (73.1%) 28 (60.9%) 0.58 [0.19;1.64] 0.312
## age 43.4 (14.1) 44.3 (12.6) 1.01 [0.97;1.04] 0.778 0.788
## BMI 28.6 (6.61) 28.8 (5.08) 1.01 [0.92;1.10] 0.904 0.913
## race: 1.000
## asian 0 (0.00%) 1 (2.17%) Ref. Ref.
## black 1 (3.85%) 1 (2.17%) . [.;.] .
## white 25 (96.2%) 44 (95.7%) . [.;.] .
## patientno 987 (1379) 1046 (1388) 1.00 [1.00;1.00] 0.861 0.863
## infusionno: BL 26 (100%) 46 (100%) Ref. Ref. .
## MADRS_Score 29.3 (5.91) 27.2 (5.68) 0.94 [0.86;1.02] 0.152 0.157
## BSS_Score 9.08 (7.65) 7.78 (7.85) 0.98 [0.92;1.04] 0.494 0.498
## TRP_nM_log 10.2 (0.20) 10.2 (0.24) 0.26 [0.03;2.64] 0.253 0.228
## five_HT_nM_log 4.86 (1.62) 4.36 (1.33) 0.78 [0.55;1.11] 0.166 0.191
## KYN_nM_log 6.89 (0.23) 6.84 (0.27) 0.46 [0.07;3.16] 0.429 0.411
## three_HK_nM_log 2.78 (0.36) 2.78 (0.37) 0.98 [0.26;3.64] 0.971 0.971
## KYNA_nM_log 2.93 (0.39) 2.96 (0.39) 1.27 [0.36;4.46] 0.705 0.709
## PIC_nM_log 2.89 (0.41) 2.92 (0.55) 1.11 [0.42;2.94] 0.830 0.820
## Quin_nM_log 4.92 (0.28) 4.97 (0.30) 1.95 [0.36;10.6] 0.437 0.437
## AA_nM_log 1.57 (0.29) 1.82 (0.47) 6.32 [1.34;29.8] 0.020 0.005
## KYN_TRP_ratio_log -3.36 (0.28) -3.34 (0.24) 1.24 [0.19;8.16] 0.825 0.835
## KYN_SER_ratio_log 2.03 (1.72) 2.48 (1.37) 1.23 [0.88;1.71] 0.228 0.262
## QUIN_PIC_ratio_log 2.03 (0.42) 2.06 (0.56) 1.12 [0.43;2.88] 0.817 0.805
## QUIN_KYNA_ratio_log 1.99 (0.36) 2.01 (0.36) 1.16 [0.30;4.49] 0.826 0.829
## threeHK_KYN_ratio_log -4.10 (0.26) -4.06 (0.29) 1.88 [0.31;11.5] 0.493 0.486
## threeHK_KYNA_ratio_log -0.14 (0.48) -0.18 (0.37) 0.79 [0.25;2.55] 0.697 0.722
## IL1B_pg_mL_log -1.51 (1.36) -2.39 (1.30) 0.51 [0.16;1.66] 0.261 0.305
## IL1B_pg_mL_LLOD_log -1.07 (0.31) -1.12 (0.04) 0.14 [0.00;13.4] 0.395 0.371
## IL2_pg_mL_log -0.63 (1.25) -0.95 (0.75) 0.69 [0.34;1.39] 0.301 0.352
## IL2_pg_mL_LLOD_log -1.27 (1.28) -1.44 (0.87) 0.86 [0.54;1.37] 0.517 0.563
## IL4_pg_mL_log -2.48 (0.39) -2.49 (0.31) 0.88 [0.21;3.73] 0.863 0.874
## IL4_pg_mL_LLOD_log -2.48 (0.39) -2.49 (0.31) 0.88 [0.21;3.73] 0.863 0.874
## IL6_pg_mL_log -0.08 (0.48) -0.08 (0.45) 0.98 [0.34;2.84] 0.968 0.969
## IL8_pg_mL_log 1.36 (0.51) 1.52 (0.33) 2.61 [0.75;9.07] 0.132 0.179
## IL10_pg_mL_log -1.04 (0.25) -0.95 (0.83) 1.24 [0.57;2.69] 0.592 0.499
## IL12p70_pg_mL_log -0.88 (0.39) -0.84 (1.32) 1.04 [0.65;1.65] 0.885 0.856
## IL12p70_pg_mL_LLOD_log -0.88 (0.39) -0.84 (1.32) 1.04 [0.65;1.65] 0.885 0.856
## IL13_pg_mL_log 1.25 (0.19) 1.38 (0.54) 4.40 [0.35;55.9] 0.253 0.157
## IL13_pg_mL_LLOD_log 1.25 (0.19) 1.32 (0.66) 1.30 [0.48;3.46] 0.606 0.511
## TNFa_pg_mL_log 0.31 (0.19) 0.36 (0.28) 2.17 [0.29;16.1] 0.447 0.405
## IFNy_pg_mL_log 1.77 (0.54) 1.80 (0.56) 1.12 [0.46;2.73] 0.806 0.808
## CRP_ng_mL_log 7.24 (1.80) 6.93 (1.35) 0.87 [0.63;1.21] 0.405 0.448
## NIC_nM_log -0.24 (1.90) . (.) . [.;.] . .
## NIC_nM_LLOD_log -0.24 (1.90) -0.33 (1.33) 0.96 [0.71;1.31] 0.820 0.839
## NTA_nM_log 5.62 (0.57) 5.70 (0.56) 1.32 [0.56;3.12] 0.533 0.543
## SAA_ng_mL_log 7.86 (0.76) 7.61 (0.79) 0.65 [0.34;1.24] 0.194 0.192
## VCAM_1_ng_mL_log 5.71 (0.22) 5.73 (0.22) 1.33 [0.15;11.8] 0.796 0.799
## ICAM_1_ng_mL_log 5.77 (0.26) 5.69 (0.25) 0.30 [0.04;2.14] 0.232 0.240
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Findings: anthranilic acid (baseline) is higher in remitters compared to non-remitters
8 Univariate screen (infusion #3) by dichotomous outcome
compareGroups::descrTable(Remission~.
, Vert,
include.miss=TRUE,
hide.no = '0',
subset=infusionno=="3rd")##
## --------Summary descriptives table by 'Remission'---------
##
## __________________________________________________________
## No remission Remitter p.overall
## N=26 N=46
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 0.526
## male 8 (30.8%) 19 (41.3%)
## female 18 (69.2%) 27 (58.7%)
## age 44.1 (14.1) 44.7 (12.4) 0.858
## BMI 28.8 (6.58) 28.7 (5.03) 0.937
## race: 0.595
## asian 0 (0.00%) 1 (2.17%)
## black 1 (3.85%) 0 (0.00%)
## white 25 (96.2%) 45 (97.8%)
## patientno 987 (1379) 1045 (1387) 0.865
## infusionno: 3rd 26 (100%) 46 (100%) .
## MADRS_Score 17.3 (6.14) 4.20 (2.70) <0.001
## BSS_Score 4.60 (6.29) 0.61 (2.18) 0.005
## TRP_nM_log 10.1 (0.27) 10.1 (0.22) 0.461
## five_HT_nM_log 4.85 (1.69) 3.97 (1.43) 0.029
## KYN_nM_log 6.79 (0.23) 6.76 (0.27) 0.550
## three_HK_nM_log 2.70 (0.30) 2.69 (0.33) 0.965
## KYNA_nM_log 2.83 (0.40) 2.91 (0.38) 0.416
## PIC_nM_log 2.86 (0.52) 2.81 (0.40) 0.706
## Quin_nM_log 4.91 (0.28) 4.98 (0.30) 0.279
## AA_nM_log 1.60 (0.36) 1.64 (0.43) 0.684
## KYN_TRP_ratio_log -3.31 (0.30) -3.30 (0.25) 0.886
## KYN_SER_ratio_log 1.94 (1.77) 2.79 (1.45) 0.043
## QUIN_PIC_ratio_log 2.05 (0.55) 2.17 (0.47) 0.348
## QUIN_KYNA_ratio_log 2.07 (0.43) 2.07 (0.35) 0.981
## threeHK_KYN_ratio_log -4.10 (0.23) -4.06 (0.32) 0.613
## threeHK_KYNA_ratio_log -0.14 (0.45) -0.22 (0.37) 0.429
## IL1B_pg_mL_log -1.69 (0.34) -1.65 (1.17) 0.936
## IL1B_pg_mL_LLOD_log -1.13 (0.01) -1.08 (0.32) 0.276
## IL2_pg_mL_log -0.73 (1.24) -1.04 (0.68) 0.337
## IL2_pg_mL_LLOD_log -1.22 (1.27) -1.27 (0.74) 0.855
## IL4_pg_mL_log -2.44 (0.29) -2.46 (0.28) 0.840
## IL4_pg_mL_LLOD_log -2.44 (0.29) -2.46 (0.28) 0.840
## IL6_pg_mL_log -0.05 (0.46) -0.17 (0.39) 0.281
## IL8_pg_mL_log 1.36 (0.31) 1.31 (0.36) 0.546
## IL10_pg_mL_log -0.95 (0.29) -0.94 (0.71) 0.917
## IL12p70_pg_mL_log -0.86 (0.37) -0.81 (1.29) 0.814
## IL12p70_pg_mL_LLOD_log -0.86 (0.37) -0.81 (1.29) 0.814
## IL13_pg_mL_log 1.26 (0.22) 1.37 (0.53) 0.224
## IL13_pg_mL_LLOD_log 1.26 (0.22) 1.37 (0.53) 0.224
## TNFa_pg_mL_log 0.29 (0.20) 0.34 (0.29) 0.390
## IFNy_pg_mL_log 1.76 (0.52) 1.81 (0.45) 0.693
## CRP_ng_mL_log 7.06 (1.73) 6.86 (1.30) 0.597
## NIC_nM_log -0.15 (1.83) -0.47 (1.11) 0.425
## NIC_nM_LLOD_log -0.15 (1.83) -0.47 (1.11) 0.425
## NTA_nM_log 5.56 (0.61) 5.49 (0.54) 0.619
## SAA_ng_mL_log 7.79 (0.77) 7.63 (0.73) 0.395
## VCAM_1_ng_mL_log 5.71 (0.22) 5.69 (0.25) 0.723
## ICAM_1_ng_mL_log 5.75 (0.24) 5.64 (0.27) 0.102
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Findings: at post-infusion #3, remitters show lower [MADRS, BSS, and serotonin (5HT)] and higher KYN/5HT ratio compared to non-remitters
9 Univariate screen (non-remitters) by infusion timepoint
compareGroups::descrTable(infusionno~.
, Vert,
include.miss=TRUE,
hide.no = '0',
ref="BL",
subset=Remission=="No remission")##
## --------Summary descriptives table by 'infusionno'---------
##
## ________________________________________________________________________
## BL 1st 3rd p.overall
## N=26 N=25 N=26
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 0.918
## male 7 (26.9%) 8 (32.0%) 8 (30.8%)
## female 19 (73.1%) 17 (68.0%) 18 (69.2%)
## age 43.4 (14.1) 43.6 (14.2) 44.1 (14.1) 0.983
## BMI 28.6 (6.61) 28.4 (6.44) 28.8 (6.58) 0.980
## race: 1.000
## black 1 (3.85%) 1 (4.00%) 1 (3.85%)
## white 25 (96.2%) 24 (96.0%) 25 (96.2%)
## patientno 987 (1379) 1026 (1393) 987 (1379) 0.993
## Remission: No remission 26 (100%) 25 (100%) 26 (100%) .
## MADRS_Score 29.3 (5.91) 18.7 (7.66) 17.3 (6.14) <0.001
## BSS_Score 9.08 (7.65) 5.70 (7.35) 4.60 (6.29) 0.072
## TRP_nM_log 10.2 (0.20) 10.1 (0.19) 10.1 (0.27) 0.034
## five_HT_nM_log 4.86 (1.62) 4.65 (1.45) 4.85 (1.69) 0.877
## KYN_nM_log 6.89 (0.23) 6.76 (0.26) 6.79 (0.23) 0.154
## three_HK_nM_log 2.78 (0.36) 2.68 (0.33) 2.70 (0.30) 0.486
## KYNA_nM_log 2.93 (0.39) 2.87 (0.31) 2.83 (0.40) 0.670
## PIC_nM_log 2.89 (0.41) 2.81 (0.38) 2.86 (0.52) 0.811
## Quin_nM_log 4.92 (0.28) 4.91 (0.27) 4.91 (0.28) 0.976
## AA_nM_log 1.57 (0.29) 1.65 (0.40) 1.60 (0.36) 0.698
## KYN_TRP_ratio_log -3.36 (0.28) -3.33 (0.28) -3.31 (0.30) 0.869
## KYN_SER_ratio_log 2.03 (1.72) 2.11 (1.57) 1.94 (1.77) 0.939
## QUIN_PIC_ratio_log 2.03 (0.42) 2.10 (0.37) 2.05 (0.55) 0.867
## QUIN_KYNA_ratio_log 1.99 (0.36) 2.03 (0.33) 2.07 (0.43) 0.760
## threeHK_KYN_ratio_log -4.10 (0.26) -4.08 (0.24) -4.10 (0.23) 0.953
## threeHK_KYNA_ratio_log -0.14 (0.48) -0.19 (0.41) -0.14 (0.45) 0.890
## IL1B_pg_mL_log -1.51 (1.36) -1.69 (1.21) -1.69 (0.34) 0.957
## IL1B_pg_mL_LLOD_log -1.07 (0.31) -1.10 (0.14) -1.13 (0.01) 0.554
## IL2_pg_mL_log -0.63 (1.25) -0.80 (1.21) -0.73 (1.24) 0.917
## IL2_pg_mL_LLOD_log -1.27 (1.28) -1.22 (1.23) -1.22 (1.27) 0.985
## IL4_pg_mL_log -2.48 (0.39) -2.44 (0.34) -2.44 (0.29) 0.886
## IL4_pg_mL_LLOD_log -2.48 (0.39) -2.44 (0.34) -2.44 (0.29) 0.886
## IL6_pg_mL_log -0.08 (0.48) 0.09 (0.56) -0.05 (0.46) 0.439
## IL8_pg_mL_log 1.36 (0.51) 1.31 (0.42) 1.36 (0.31) 0.888
## IL10_pg_mL_log -1.04 (0.25) -0.94 (0.32) -0.95 (0.29) 0.389
## IL12p70_pg_mL_log -0.88 (0.39) -0.84 (0.40) -0.86 (0.37) 0.918
## IL12p70_pg_mL_LLOD_log -0.88 (0.39) -0.84 (0.40) -0.86 (0.37) 0.918
## IL13_pg_mL_log 1.25 (0.19) 1.28 (0.21) 1.26 (0.22) 0.924
## IL13_pg_mL_LLOD_log 1.25 (0.19) 1.28 (0.21) 1.26 (0.22) 0.924
## TNFa_pg_mL_log 0.31 (0.19) 0.27 (0.22) 0.29 (0.20) 0.755
## IFNy_pg_mL_log 1.77 (0.54) 1.68 (0.54) 1.76 (0.52) 0.808
## CRP_ng_mL_log 7.24 (1.80) 7.03 (1.85) 7.06 (1.73) 0.909
## NIC_nM_log -0.24 (1.90) -0.33 (1.90) -0.15 (1.83) 0.944
## NIC_nM_LLOD_log -0.24 (1.90) -0.33 (1.90) -0.15 (1.83) 0.944
## NTA_nM_log 5.62 (0.57) 5.48 (0.52) 5.56 (0.61) 0.668
## SAA_ng_mL_log 7.86 (0.76) 7.77 (0.81) 7.79 (0.77) 0.906
## VCAM_1_ng_mL_log 5.71 (0.22) 5.72 (0.24) 5.71 (0.22) 0.979
## ICAM_1_ng_mL_log 5.77 (0.26) 5.75 (0.27) 5.75 (0.24) 0.951
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Findings: MADRS was progressively lower across 3 timepoints. BSS downtrended at infusion 3 compared to baseline. Although TRP was significantly different across timepoints, TRP only downtrended with subsequent treatments compared to baseline.
10 Univariate screen (remitters) by infusion timepoint
compareGroups::descrTable(infusionno~.
, Vert,
include.miss=TRUE,
hide.no = '0',
ref="BL",
subset=Remission=="Remitter")##
## --------Summary descriptives table by 'infusionno'---------
##
## _______________________________________________________________________
## BL 1st 3rd p.overall
## N=46 N=46 N=46
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## sex: 0.970
## male 18 (39.1%) 19 (41.3%) 19 (41.3%)
## female 28 (60.9%) 27 (58.7%) 27 (58.7%)
## age 44.3 (12.6) 44.7 (12.4) 44.7 (12.4) 0.985
## BMI 28.8 (5.08) 28.7 (5.03) 28.7 (5.03) 0.997
## race: 1.000
## asian 1 (2.17%) 1 (2.17%) 1 (2.17%)
## black 1 (2.17%) 0 (0.00%) 0 (0.00%)
## white 44 (95.7%) 45 (97.8%) 45 (97.8%)
## patientno 1046 (1388) 1045 (1387) 1045 (1387) 1.000
## Remission: Remitter 46 (100%) 46 (100%) 46 (100%) .
## MADRS_Score 27.2 (5.68) 11.8 (6.01) 4.20 (2.70) <0.001
## BSS_Score 7.78 (7.85) 1.76 (4.02) 0.61 (2.18) <0.001
## TRP_nM_log 10.2 (0.24) 10.1 (0.26) 10.1 (0.22) 0.030
## five_HT_nM_log 4.36 (1.33) 4.21 (1.41) 3.97 (1.43) 0.390
## KYN_nM_log 6.84 (0.27) 6.75 (0.26) 6.76 (0.27) 0.208
## three_HK_nM_log 2.78 (0.37) 2.69 (0.34) 2.69 (0.33) 0.384
## KYNA_nM_log 2.96 (0.39) 2.91 (0.36) 2.91 (0.38) 0.754
## PIC_nM_log 2.92 (0.55) 2.77 (0.42) 2.81 (0.40) 0.291
## Quin_nM_log 4.97 (0.30) 4.94 (0.30) 4.98 (0.30) 0.754
## AA_nM_log 1.82 (0.47) 1.77 (0.41) 1.64 (0.43) 0.132
## KYN_TRP_ratio_log -3.34 (0.24) -3.32 (0.26) -3.30 (0.25) 0.761
## KYN_SER_ratio_log 2.48 (1.37) 2.54 (1.41) 2.79 (1.45) 0.531
## QUIN_PIC_ratio_log 2.06 (0.56) 2.17 (0.46) 2.17 (0.47) 0.470
## QUIN_KYNA_ratio_log 2.01 (0.36) 2.03 (0.33) 2.07 (0.35) 0.736
## threeHK_KYN_ratio_log -4.06 (0.29) -4.06 (0.27) -4.06 (0.32) 0.995
## threeHK_KYNA_ratio_log -0.18 (0.37) -0.22 (0.37) -0.22 (0.37) 0.859
## IL1B_pg_mL_log -2.39 (1.30) -1.85 (1.00) -1.65 (1.17) 0.329
## IL1B_pg_mL_LLOD_log -1.12 (0.04) -1.12 (0.08) -1.08 (0.32) 0.430
## IL2_pg_mL_log -0.95 (0.75) -1.15 (0.78) -1.04 (0.68) 0.579
## IL2_pg_mL_LLOD_log -1.44 (0.87) -1.41 (0.77) -1.27 (0.74) 0.555
## IL4_pg_mL_log -2.49 (0.31) -2.48 (0.33) -2.46 (0.28) 0.846
## IL4_pg_mL_LLOD_log -2.49 (0.31) -2.54 (0.55) -2.46 (0.28) 0.581
## IL6_pg_mL_log -0.08 (0.45) -0.08 (0.48) -0.17 (0.39) 0.527
## IL8_pg_mL_log 1.52 (0.33) 1.49 (0.32) 1.31 (0.36) 0.009
## IL10_pg_mL_log -0.95 (0.83) -0.90 (0.74) -0.94 (0.71) 0.951
## IL12p70_pg_mL_log -0.84 (1.32) -0.85 (1.26) -0.81 (1.29) 0.984
## IL12p70_pg_mL_LLOD_log -0.84 (1.32) -0.92 (1.33) -0.81 (1.29) 0.910
## IL13_pg_mL_log 1.38 (0.54) 1.38 (0.54) 1.37 (0.53) 0.999
## IL13_pg_mL_LLOD_log 1.32 (0.66) 1.26 (0.76) 1.37 (0.53) 0.710
## TNFa_pg_mL_log 0.36 (0.28) 0.33 (0.31) 0.34 (0.29) 0.851
## IFNy_pg_mL_log 1.80 (0.56) 1.77 (0.56) 1.81 (0.45) 0.946
## CRP_ng_mL_log 6.93 (1.35) 6.91 (1.29) 6.86 (1.30) 0.965
## NIC_nM_log . (.) -0.11 (1.31) -0.47 (1.11) .
## NIC_nM_LLOD_log -0.33 (1.33) -0.11 (1.31) -0.47 (1.11) 0.398
## NTA_nM_log 5.70 (0.56) 5.66 (0.48) 5.49 (0.54) 0.130
## SAA_ng_mL_log 7.61 (0.79) 7.63 (0.79) 7.63 (0.73) 0.988
## VCAM_1_ng_mL_log 5.73 (0.22) 5.71 (0.21) 5.69 (0.25) 0.672
## ICAM_1_ng_mL_log 5.69 (0.25) 5.68 (0.24) 5.64 (0.27) 0.641
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯Findings: both MADRS and BSS declined significantly with subsequent infusion timepoints. TRP downtrended from baseline to infusion 1, and was significantly lower at infusion 3 relative to baseline. IL8 at infusion 3 was lower compared to baseline and infusion 1.
11 Univariate screen (whole group) by continuous outcome (Spearman’s matrix)
Bx_spearmans<- Vert %>%
select("age", "BMI","MADRS_Score", "BSS_Score",
all_of(starts_with(vars_KP)),
all_of(starts_with(vars_inflam)),
all_of(starts_with(vars_vasc))) %>%
select(!starts_with("IL1"))%>%
select(!starts_with("IL2")) %>%
select(!starts_with("IL4")) %>%
select(!contains("LLOD"))
mydata.cor = cor(Bx_spearmans, method = c("spearman"), use="complete.obs")
matrix<-Hmisc::rcorr(as.matrix(mydata.cor))
corrplot::corrplot(mydata.cor,
addCoef.col = 'black',
p.mat=matrix$P,
insig="blank",
order="hclust",
method="color",
type="upper",
diag=FALSE,
number.cex=0.8,
na.label.col = "gray",
addgrid.col=TRUE,
tl.col = 'red',
tl.srt = 45,
title="Spearmans's matrix of age, BMI, and log transformed biomarkers (whole sample)")
MADRS correlations: TRP BSS correlations: TRP, MADRS BMI correlations: age, AA, KYN/TRP, QUIN/PIC, IL6, CRP, SAA, NTA Age correlations: BMI, AA, KYN/TRP, QUIN, KYN, TNFa
12 TRP_model_1: TRP by Remission*infusionno (mixed effects model)
model_TRP_vif<-lme4::lmer(TRP_nM~sex+age+BMI+race+Remission+infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::vif(model_TRP_vif)## GVIF Df GVIF^(1/(2*Df))
## sex 1.070739 1 1.034765
## age 1.117809 1 1.057265
## BMI 1.175612 1 1.084256
## race 1.157261 2 1.037189
## Remission 1.021103 1 1.010496
## infusionno 1.002135 2 1.000533model_TRP<-lme4::lmer(TRP_nM~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(model_TRP))
## 218 42
## 215 41pairwise_remission<-emmeans(model_TRP, pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="Tryptophan (emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_TRP, pairwise~infusionno|Remission)
plot(pairwise_infusionno, xlab="Tryptophan (emmeans)", ylab="Ketamine infusion timepoint", comparisons = TRUE)
PAIRWISE COMPARISON OF ESTIMATES: 1. Between-groups comparison: TRP did not distinguish prospective remission status at all 3 timepoints 2. Within-groups comparison: TRP declined significantly between baseline and infusion 3, for both remitter/non-remitter groups
13 TRP_model_2: MADRS by TRP*infusionno (mixed effects model)
lmer_TRP<-lme4::lmer(MADRS_Score~age+sex+BMI+TRP_nM*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(lmer_TRP))
## 148 143
## 147 143# summary(lmer_TRP)
# car::Anova(lmer_TRP)
sjPlot::tab_model(lmer_TRP, show.std = TRUE, show.est = FALSE, title="Mixed model: MADRS according to TRP-by-infusion interaction")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.04 | 0.70 – 1.38 | <0.001 | <0.001 |
| age | 0.04 | -0.08 – 0.15 | 0.503 | 0.503 |
| sex [female] | 0.18 | -0.06 – 0.42 | 0.137 | 0.137 |
| BMI | 0.04 | -0.07 – 0.15 | 0.488 | 0.488 |
| TRP nM | -0.00 | -0.14 – 0.14 | 0.987 | 0.987 |
| infusionno [1st] | -1.28 | -1.45 – -1.10 | <0.001 | <0.001 |
| infusionno [3rd] | -1.77 | -1.95 – -1.60 | <0.001 | <0.001 |
| TRP nM * infusionno [1st] | 0.09 | -0.09 – 0.26 | 0.339 | 0.339 |
| TRP nM * infusionno [3rd] | 0.14 | -0.04 – 0.32 | 0.134 | 0.134 |
| Random Effects | ||||
| σ2 | 28.34 | |||
| τ00 patientno | 16.04 | |||
| τ00 Site_Location | 6.52 | |||
| ICC | 0.44 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.567 / 0.759 | |||
FINDINGS: MADRS was not associated with TRP, adjusting for demo and interaction with infusion timepoint
14 TRP_model_3: post-treatment MADRS by pre-treatment TRP (linear model)
# MADRS by baseline TRP, adjusted by baseline MADRS, remission interaction, and demo
Biok_wide_new$TRP_nM_BL<-exp(Biok_wide_new$TRP_nM_log_BL)
lm_TRP_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+TRP_nM_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_TRP_BL))
## 4 25
## 2 23sjPlot::tab_model(lm_TRP_BL, show.std = TRUE, show.est = FALSE, title="Linear model: post-treatment MADRS according to pre-treatment TRP, adjusting for demo and pre-treatment MADRS")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.27 | -0.67 – 0.12 | 0.090 |
| age | 0.08 | -0.17 – 0.32 | 0.536 |
| sex [female] | 0.43 | -0.08 – 0.93 | 0.095 |
| BMI | 0.04 | -0.20 – 0.28 | 0.727 |
| MADRS Score BL | 0.23 | -0.00 – 0.47 | 0.052 |
| TRP nM BL | 0.27 | 0.03 – 0.51 | 0.030 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.152 / 0.086 | ||
FINDINGS: MADRS (post-treatment) was predicted by baseline TRP adjusting for MADRS (baseline) + demo
15 5HT_model_1: 5HT (outcome) by Remission*infusionno (mixed model)
model_SER<-lme4::lmer(five_HT_nM_log~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location)+(1|Batch_Number), data=Biok_vert_df)
car::qqPlot(residuals(model_SER))
## 204 131
## 201 129pairwise_remission<-emmeans(model_SER,pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="Serotonin (emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_SER,pairwise~infusionno|Remission)
plot(pairwise_infusionno, comparisons=TRUE, xlab="Serotonin (emmeans)", ylab="Ketamine infusion timepoint")
FINDINGS: 1. At baseline and timepoint 3, 5HT was significantly lower in remitters compared to non-remitters 2. In remitters, 5HT declined significantly between BL and infusion 3
16 5HT_model_2: MADRS by 5HT*infusionno (mixed model)
lmer_5HT<-lme4::lmer(MADRS_Score~age+sex+BMI+five_HT_nM_log*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(lmer_5HT))
## 147 104
## 146 104# summary(lmer_5HT)
sjPlot::tab_model(lmer_5HT, show.std =TRUE, show.est=FALSE, title="Mixed model of MADRS according to 5HT-by-infusion and covariates")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.11 | 0.76 – 1.46 | <0.001 | <0.001 |
| age | 0.03 | -0.08 – 0.14 | 0.579 | 0.579 |
| sex [female] | 0.12 | -0.10 – 0.35 | 0.283 | 0.283 |
| BMI | 0.04 | -0.07 – 0.15 | 0.470 | 0.470 |
| five HT nM log | -0.06 | -0.20 – 0.09 | 0.438 | 0.438 |
| infusionno [1st] | -1.30 | -1.46 – -1.13 | <0.001 | <0.001 |
| infusionno [3rd] | -1.79 | -1.96 – -1.63 | <0.001 | <0.001 |
|
five HT nM log * infusionno [1st] |
0.11 | -0.06 – 0.28 | 0.220 | 0.220 |
|
five HT nM log * infusionno [3rd] |
0.23 | 0.06 – 0.39 | 0.007 | 0.007 |
| Random Effects | ||||
| σ2 | 27.78 | |||
| τ00 patientno | 15.28 | |||
| τ00 Site_Location | 8.68 | |||
| ICC | 0.46 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.566 / 0.767 | |||
RESULTS: MADRS was significantly associated with interaction of 5HT and infusion 3
17 5HT_model_3: post-treatment MADRS by baseline 5HT (linear model)
lm_5HT_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+five_HT_nM_log_BL*Remission, data=Biok_wide_new)
car::qqPlot(residuals(lm_5HT_BL))
## 2 4
## 1 2sjPlot::tab_model(lm_5HT_BL, show.std =TRUE, show.est=FALSE, title="Linear model: post-treatment MADRS by baseline TRP, adjusting for demographics and baseline MADRS")| MADRS Score 3 rd | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 0.99 | 0.70 – 1.28 | 0.539 | <0.001 |
| age | 0.08 | -0.05 – 0.22 | 0.225 | 0.225 |
| sex [female] | 0.08 | -0.19 – 0.35 | 0.551 | 0.551 |
| BMI | 0.02 | -0.11 – 0.15 | 0.770 | 0.770 |
| MADRS Score BL | 0.15 | 0.01 – 0.28 | 0.030 | 0.030 |
| five HT nM log BL | 0.22 | 0.02 – 0.42 | 0.034 | 0.034 |
| Remission [Remitter] | -1.65 | -1.92 – -1.38 | 0.042 | <0.001 |
|
five HT nM log BL * Remission [Remitter] |
-0.23 | -0.50 – 0.04 | 0.094 | 0.094 |
| Observations | 70 | |||
| R2 / R2 adjusted | 0.755 / 0.727 | |||
RESULTS: post-treatment MADRS was associated with baseline 5HT, regardless of prospective remission status, adjusting for baseline MADRS and demographics
18 KYN_model_1: Kynurenine (KYN) analysis
model_KYN<-lme4::lmer(KYN_nM_log~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location)+(1|Batch_Number), data=Biok_vert_df)
car::qqPlot(residuals(model_KYN))
## 218 202
## 215 199pairwise_remission<-emmeans(model_KYN,pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="Kynurenine (emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_KYN,pairwise~infusionno|Remission)
plot(pairwise_infusionno, comparisons=TRUE, xlab="Kynurenine (emmeans)", ylab="Ketamine infusion timepoint")
RESULTS: 1. KYN did not distinguish remission at any timepoint 2. KYN decreased significantly at infusion 1 and 3 (relative to baseline), for BOTH remitters and non-remitters.
19 KYN_model_2: MADRS by KYN*infusionno (mixed model)
# Mixed effects model of MADRS by AA-infusionno
lmer_KYN<-lme4::lmer(MADRS_Score~sex+age+BMI+KYN_nM_log*infusionno+ (1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(lmer_KYN))
## 143 151
## 143 150sjPlot::tab_model(lmer_KYN, show.std=TRUE, show.est=FALSE, title="Mixed model: MADRS by KYN*infusionno, adjusted by demo")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.07 | 0.73 – 1.40 | 0.076 | <0.001 |
| sex [female] | 0.16 | -0.08 – 0.39 | 0.192 | 0.192 |
| age | 0.01 | -0.11 – 0.13 | 0.845 | 0.845 |
| BMI | 0.02 | -0.09 – 0.14 | 0.682 | 0.682 |
| KYN nM log | -0.03 | -0.18 – 0.11 | 0.657 | 0.657 |
| infusionno [1st] | -1.29 | -1.46 – -1.12 | 0.064 | <0.001 |
| infusionno [3rd] | -1.79 | -1.96 – -1.62 | 0.008 | <0.001 |
|
KYN nM log * infusionno [1st] |
0.11 | -0.06 – 0.27 | 0.205 | 0.205 |
|
KYN nM log * infusionno [3rd] |
0.17 | -0.01 – 0.34 | 0.060 | 0.060 |
| Random Effects | ||||
| σ2 | 28.10 | |||
| τ00 patientno | 16.37 | |||
| τ00 Site_Location | 6.77 | |||
| ICC | 0.45 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.566 / 0.762 | |||
RESULTS: -Mixed effects model: MADRS trended with KYN at infusion 3, adjusting for demographics
20 KYN/5HT_model_1: Kynurenine/5HT by remission*infusionno (mixed model)
model_KYNSER<-lme4::lmer(KYN_SER_ratio_log~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(model_KYNSER))
## 204 131
## 201 129pairwise_remission<-emmeans(model_KYNSER,pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="KYN:5HT ratio (emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_KYNSER,pairwise~infusionno|Remission)
plot(pairwise_infusionno, comparisons=TRUE, xlab="KYN:5HT rato (emmeans)", ylab="Ketamine infusion timepoint")
RESULTS 1. QQ plot still abnormal - may need robust model 2. Significantly higher KYN:5HT ratio (infusion 3) in remitters compared to non-remitters 3. Amongst remitters there’s an uptrend in KYN:5HT ratio at infusion 3 compared to baseline
21 KYN/5HT_model_2: MADRS by KYN/5HT*infusionno interaction (mixed model)
# Mixed effects model of MADRS by AA-infusionno
lmer_KYNSER<-lme4::lmer(MADRS_Score~sex+age+BMI+KYN_SER_ratio_log*infusionno+ (1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(lmer_KYNSER))
## 147 104
## 146 104# summary(lmer_KYNSER)
# car::Anova(lmer_KYNSER)
sjPlot::tab_model(lmer_KYNSER, show.std=TRUE, show.est=FALSE, title="Mixed model: MADRS by KYN/5HT*infusionno interaction, adjusted by demo")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.11 | 0.75 – 1.46 | <0.001 | <0.001 |
| sex [female] | 0.12 | -0.11 – 0.35 | 0.302 | 0.302 |
| age | 0.03 | -0.08 – 0.15 | 0.561 | 0.561 |
| BMI | 0.04 | -0.07 – 0.15 | 0.464 | 0.464 |
| KYN SER ratio log | 0.05 | -0.10 – 0.19 | 0.533 | 0.533 |
| infusionno [1st] | -1.30 | -1.46 – -1.13 | <0.001 | <0.001 |
| infusionno [3rd] | -1.79 | -1.96 – -1.63 | <0.001 | <0.001 |
|
KYN SER ratio log * infusionno [1st] |
-0.08 | -0.26 – 0.09 | 0.336 | 0.336 |
|
KYN SER ratio log * infusionno [3rd] |
-0.20 | -0.36 – -0.03 | 0.020 | 0.020 |
| Random Effects | ||||
| σ2 | 28.09 | |||
| τ00 patientno | 15.47 | |||
| τ00 Site_Location | 8.56 | |||
| ICC | 0.46 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.563 / 0.764 | |||
RESULTS: 1. MADRS (post-treatment) inversely correlated with KYN:SER at infusion #3, adjusting for demo Note: need help with robust mixed model if appropriate (https://cran.r-project.org/web/packages/sjPlot/vignettes/tab_model_robust.html)
22 KYN/5HT_model_3: post-tx MADRS by baseline KYN/5HT and covariates (linear model)
# Baseline elevated KYN:5HT is associated with outcome, adjusted by baseline severity and demo
lm_KYNSER_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+KYN_SER_ratio_log_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_KYNSER_BL))
## 2 4
## 1 2# summary(lm_KYNSER_BL)
sjPlot::tab_model(lm_KYNSER_BL, show.std=TRUE, show.est=FALSE, title="Linear model: Post-tx MADRS by baseline KYN/5HT adjusted by baseline MADRS and demo")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.17 | -0.56 – 0.22 | 0.589 |
| age | 0.08 | -0.17 – 0.33 | 0.504 |
| sex [female] | 0.26 | -0.23 – 0.75 | 0.290 |
| BMI | 0.04 | -0.21 – 0.28 | 0.764 |
| MADRS Score BL | 0.29 | 0.05 – 0.53 | 0.019 |
| KYN SER ratio log BL | -0.22 | -0.46 – 0.01 | 0.066 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.134 / 0.066 | ||
RESULTS: MADRS (post-treatment) trended with baseline KYN:5HT (p=0.06), adjusting for MADRS (baseline) and demo
23 AA_model_1: Anthranilic acid by Remission*infusion interaction (mixed model)
model_AA<-lme4::lmer(AA_nM_log~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(model_AA))
## 50 205
## 49 202pairwise_remission<-emmeans(model_AA,pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="AA level(emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_AA,pairwise~infusionno|Remission)
plot(pairwise_infusionno, comparisons=TRUE, xlab="AA level (emmeans)", ylab="Ketamine infusion timepoint")
RESULTS 1. Baseline AA is significantly higher in remitters than non-remitters 2. In remitters, AA is significantly lower at infusion 3 compared to baseline
24 AA_model_2: MADRS by AA*infusionno and covariates
lmer_AA<-lme4::lmer(MADRS_Score~sex+age+BMI+AA_nM_log*infusionno+ (1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(lmer_AA))
## 148 143
## 147 143sjPlot::tab_model(lmer_AA, show.est=FALSE, show.std=TRUE, title="Mixed model: MADRS by AA*infusionno (interaction), adjusting for demographics")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.08 | 0.75 – 1.42 | <0.001 | <0.001 |
| sex [female] | 0.12 | -0.11 – 0.36 | 0.292 | 0.292 |
| age | 0.02 | -0.10 – 0.14 | 0.708 | 0.708 |
| BMI | 0.03 | -0.09 – 0.15 | 0.637 | 0.637 |
| AA nM log | -0.03 | -0.16 – 0.11 | 0.697 | 0.697 |
| infusionno [1st] | -1.30 | -1.47 – -1.14 | <0.001 | <0.001 |
| infusionno [3rd] | -1.80 | -1.96 – -1.63 | <0.001 | <0.001 |
|
AA nM log * infusionno [1st] |
0.12 | -0.05 – 0.29 | 0.174 | 0.174 |
|
AA nM log * infusionno [3rd] |
0.04 | -0.13 – 0.22 | 0.644 | 0.644 |
| Random Effects | ||||
| σ2 | 28.55 | |||
| τ00 patientno | 16.34 | |||
| τ00 Site_Location | 7.23 | |||
| ICC | 0.45 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.560 / 0.759 | |||
RESULT: MADRS is NOT associated with AA at any timepoint, adjusting for demo and random effects (patient, site)
25 AA_model_3: post-tx MADRS by pre-tx AA and covariates
lm_AA_BL<-lm(MADRS_Score_3rd~age+sex+BMI+MADRS_Score_BL+AA_nM_log_BL, data=Biok_wide_new)
car::qqPlot(residuals(lm_AA_BL))
## 2 25
## 1 23sjPlot::tab_model(lm_AA_BL, show.est=FALSE, show.std=TRUE, title="Linear model: post-Tx MADRS by pre-tx AA, adjusting for pre-tx MADRS and covariates")| MADRS Score 3 rd | |||
|---|---|---|---|
| Predictors | std. Beta | standardized CI | p |
| (Intercept) | -0.19 | -0.58 – 0.19 | 0.803 |
| age | 0.11 | -0.14 – 0.36 | 0.381 |
| sex [female] | 0.30 | -0.18 – 0.78 | 0.221 |
| BMI | 0.09 | -0.16 – 0.33 | 0.485 |
| MADRS Score BL | 0.22 | -0.02 – 0.46 | 0.067 |
| AA nM log BL | -0.28 | -0.53 – -0.03 | 0.030 |
| Observations | 70 | ||
| R2 / R2 adjusted | 0.152 / 0.086 | ||
RESULTS (CONTINUOUS OUTCOME) 1. Post-treatment MADRS is negatively correlated with baseline AA, adjusting for baseline MADRS and demographics
26 IL8_mod_1: IL8 by remission*infusion (mixed model)
model_IL8<-lme4::lmer(IL8_pg_mL_log~sex+age+BMI+race+Remission*infusionno+(1|patientno)+(1|Site_Location), data=Biok_vert_df)
car::qqPlot(residuals(model_IL8), xlab="Intereukin 8 level (IL8_pg_mL_log)")
## 45 22
## 44 21pairwise_remission<-emmeans(model_IL8,pairwise~Remission|infusionno)
plot(pairwise_remission, comparisons=TRUE, xlab="IL8 level(emmeans)", ylab="Prospective remission status")
pairwise_infusionno<-emmeans(model_IL8,pairwise~infusionno|Remission)
plot(pairwise_infusionno, comparisons=TRUE, xlab="IL8 level (emmeans)", ylab="Ketamine infusion timepoint")
RESULTS 1. At any infusion timepoint, there are NO significant differences in IL8 by remission status 2. Amongst remitters, IL8 is significantly lower at infusion 3 compared to BL and infusion 1 levels
27 IL8_model_2: MADRS by IL8*infusionno (mixed effects)
# Mixed effects model of MADRS by IL8-infusionno
lmer_IL8<-lme4::lmer(MADRS_Score~sex+age+BMI+IL8_pg_mL_log*infusionno+ (1|patientno)+(1|Site_Location), data=Biok_vert_df)
sjPlot::tab_model(lmer_IL8, show.est = FALSE, show.std=TRUE, title="Mixed model: MADRS by IL8*infusionno, adjusting for demographics")| MADRS Score | ||||
|---|---|---|---|---|
| Predictors | std. Beta | standardized CI | p | std. p |
| (Intercept) | 1.09 | 0.75 – 1.43 | <0.001 | <0.001 |
| sex [female] | 0.13 | -0.11 – 0.36 | 0.290 | 0.290 |
| age | 0.02 | -0.10 – 0.14 | 0.716 | 0.716 |
| BMI | 0.04 | -0.08 – 0.16 | 0.506 | 0.506 |
| IL8 pg mL log | -0.07 | -0.20 – 0.06 | 0.297 | 0.297 |
| infusionno [1st] | -1.31 | -1.48 – -1.15 | <0.001 | <0.001 |
| infusionno [3rd] | -1.80 | -1.97 – -1.63 | <0.001 | <0.001 |
|
IL8 pg mL log * infusionno [1st] |
0.20 | 0.04 – 0.37 | 0.017 | 0.017 |
|
IL8 pg mL log * infusionno [3rd] |
0.12 | -0.05 – 0.30 | 0.167 | 0.167 |
| Random Effects | ||||
| σ2 | 27.40 | |||
| τ00 patientno | 17.42 | |||
| τ00 Site_Location | 7.28 | |||
| ICC | 0.47 | |||
| N patientno | 75 | |||
| N Site_Location | 4 | |||
| Observations | 217 | |||
| Marginal R2 / Conditional R2 | 0.563 / 0.770 | |||
RESULTS (CONTINUOUS OUTCOME) -Mixed effects: MADRS is associated with IL8 at infusion timepoint 1, adjusting for baseline MADRS and demo