2024-06-21

084_ Revealing multi-omics biomarker signatures for treatment resistance

Research Question and Aims

Despite substantial research on the biological foundations of psychiatric disorders, there is still a demand for practical clinical tools, like fluid biomarkers, that can classify and predict treatment resistance ahead of the clinical course, enabling targeted treatment strategies for those patients. One of the key reasons for poor clinical outcomes is the resistance to standard treatments (1), which affects about one-third of all patients with schizophrenia (SCZ) and results in treatment-resistant schizophrenia (TRS; 2). Similarly, for major depressive disorder (MDD), treatment-resistant depression (TRD) impacts around 30-50% of all patients with depression (3). When considering bipolar disorder (BD), the definition of treatment resistance is less consistent, although it is well described that 1/3 of lithium maintenance-treated patients do not respond at all (4). Moreover, there are efforts to specifically classify the depressive component of BD in a more definitive manner as treatment-resistant (5). Although numerous studies have identified biomarkers associated with treatment resistance (6), there is currently no accessible blood-based biomarker or combination thereof to reliably identify individuals with treatment-resistant psychiatric disorders and to predict who do not respond to standard pharmacotherapy and may benefit from more intensive or second-line treatment options.
Several factors contribute to this challenge, including the limited availability of comprehensive clinical study data, especially longitudinal studies that assemble multiple omics modalities. Furthermore, the complex polygenic and heterogenous nature of psychiatric disorders adds another level of difficulty. The discussed etiology/ies of treatment resistance and its underlying mechanisms are highly heterogenous and involve a range of diverse biological pathways including neurodevelopmental, neuroinflammatory, neurosignalling and neuroplasticiy, neurodegenerative and homeostatic alterations that could be dysregulated in respective patient subgroups (6).
For this reason, it seems more likely that a biomarker-profile based on multi-omics analyses, as opposed to a single biomarker or a single omics modality (7), will achieve better prediction of treatment resistance.
To address the described multidimensionality of treatment resistance in SCZ, BPD, MDD, we propose a multi-omics analysis to integrate data from metabolomics, lipidomics, proteomics, transcriptomics, and genomics to detect multimodal, multivariate biomarker signatures to identify and predict treatment resistance. This project aims to identify translational multi-omics signatures based on available GWAS, RNAseq, Proteomics and Lipidomics data to predict treatment resistance of SCZ, BD and MDD patients. Moreover, this project aims to validate if multi-omics outperforms single modality-omics-approaches to predict treatment resistance. Considering that SCZ, BD an MDD are spectrum disorders with genetic overlaps, we aim finally to investigate whether the biomarker-signatures to identify treatment resistance for each diagnosis do partly overlap or represent overlapping biological pathways to investigate a hypothesized transdiagnostic aspect of treatment resistance.
The biomarker signatures identified in this project will form the foundation for a future endeavor, which will focus on developing a simplified liquid biopsy profiler designed for broader clinical use (FOOTPRINT initiative, led by Peter Falkai).

Analytic Plan

1. Defining treatment-resistant patients in the PsyCourse Study
We will categorize PsyCourse patients based on the category “treatment resistance” for every disease entity separately, using the available longitudinal clinical characterization data, specifically data on medication at visit 1-4, psychiatric history at visit 1, assessment of psychiatric problems between study visits V2-4, duration of illness at visit 1 and the assessment of disease course through the OPCRIT Item 90 assessed on visit 4 as well as the assessment of additional psychiatric hospitalization as in- or daypatient during visits. For parts of the treatment resistance definition, we have to take into account clinical data related to the participants' clinical course prior to their inclusion in the PsyCourse study. We will handle this data with caution due to the retrospective nature.
We will use the following treatment resistance definitions due to the fact that a strict application of the in literature discussed standard definitions in retrospective analyses results in low case numbers. By defining different treatment resistance categories, we can achieve a higher case number. Ultimately, the robustness of the different definitions (homogeneous enough and biologically meaningful or too heterogeneous) must be demonstrated through correlation with biological data. If the patient fits into one of the shells to one timepoint during the study, we will classify the individuum as treatment-resistant.
To include patients who are severely affected but for whom a decision for intensified medication therapy has not been made due to unknown reasons, we introduce another clinically defined category, named Shell X with specified criteria mentioned below for each disease entity.

For treatment-resistant schizophrenia
Shell 1: Clozapine currently or previously documented
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 2: Intake of at least 2 antipsychotics in adequate doses (literature-based definition) plus non-remission (assessed through the Andreassen remission criteria: PANSS total score <60 and score ≤3 on items P1, P2, P3, G5, G9, N1, N4, N6)
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 3: Intake of at least 2 antipsychotics in adequate doses (literature-based definition) plus number of hospitalizations >3
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 4: Intake of at least 3 antipsychotics (in adequate doses)
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 5: Intake of at least 3 antipsychotics (in adequate doses) plus number of hospitalizations >3
a) at the time of inclusion in the study (visit 1), b) at visit 2-4 Shell 6: OPCRIT Item 90 at visit 4: “ongoing chronic disease” = 4 or “ongoing chronic disease with deterioration” = 5
Shell X: GAF <35, PANSS total score >60, hospitalizations > 3, at least 2 domains of the WHO QOL BREF < 50
a) at the time of inclusion in the study (visit 1), b) at visit 2-4

For treatment-resistant depression:
In the literature, treatment-resistant depression is defined as an inadequate response to treatment with antidepressant (AD) therapy, Bauer et al. 2013 distinguished two types of TRD (11):
Stage I TRD: failure of ≥1 adequate trial using one major class of AD.
Stage II TRD: failure of two distinct classes of AD
For this reason, we define our categories as follows:
Shell 1: Intake of at least two antidepressants plus depressive symptoms (IDS-C30 >25)
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 2: Intake of one antidepressant plus depressive symptoms at study inclusion (IDS-C30 >25)
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell X: GAF <35, IDSC30 total score >25, hospitalizations > 3, at least 2 domains of the WHO QOL BREF < 50
a) at the time of inclusion in the study (visit 1), b) at visit 2-4

For treatment resistance in bipolar disease:
We define our categories as follows:
Shell 1: combination of >2 drugs for bipolar disease plus IDS-C30 >25
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 2: combination of >2 drugs for bipolar disease plus YMRS >19
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 3: combination of >2 drugs for bipolar disease plus number of hospitalizations >3
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
Shell 0: GAF <35, IDSC30 total score >25 or YMRS > 19, hospitalizations > 3, at least 2 domains of the WHO QOL BREF < 50
a) at the time of inclusion in the study (visit 1), b) at visit 2-4
To further characterize the different groups, defined partially by the ratings from PANSS, IDSC, and YMRS, we will also include data from the BDI-II (Beck Depression Inventory), Altman Self-Rating Mania Scale (ASRM), and Mania Self-Rating Scale (MSS) for clinical characterization.
2. Association analyses & statistical approach with multi-omics data
Only the biological data from visit 1 will be used.
After separating the PsyCourse patients into treatment-resistant and non- treatment -resistant groups for SCZ, BP, and MDD, each omics modality will be assessed independently to determine its ability to distinguish between treatment -resistant and non- treatment -resistant patients for each condition. Once these analyses are completed and significant features are identified, a further analysis will examine if the combination of top features from each omics analysis can improve the sensitivity and specificity of a treatment resistance profiler for each condition.
Since not all individuals have been examined for all different omics modalities, it needs to be determined whether differences between treatment-resistant and non-resistant patients in terms of their epigenetics, mRNA transcriptome, proteome, or lipidome contribute added value to the development of a profiler for each entity separately.
Using the aforementioned categorization, we will investigate:

2a. Differences in the genetic background between treatment-resistant and non-treatment-resistant patients
Genetic analysis of 1,764 case samples from the PsyCourse study will be used. Polygenic risk scores (PRS) for major psychiatric disorders and traits related with treatment resistance (e.g. PMID 26806518 (for BP), PMID 35712048 (for MDD), PMID 30883267 (for SCZ)) will be constructed based on the summary statistics of the latest genome-wide association studies (GWAS) for these phenotypes. PRS-CS tool will be used to infer posterior SNP effect sizes under continuous shrinkage priors and estimate the global shrinkage parameter (φ) using a fully Bayesian approach. PRS will be then calculated in PLINK 1.9 using dosage data. The association of polygenic burden (PRScs) for several traits with treatment resistance will be tested using linear/logistic models and adequate covariates. Moreover, we aim to perform gene expression imputation analysis, based on available TWAS studies, to predict the gene expression within a certain tissue and to explore whether clustering based on gene expression imputation would outperform a PRS-based stratification (9,10).

2b. Association of differences in miRNA sequencing data, epigenetic data, mRNA transcriptome data, proteome profiles and plasma lipid profiles with the categorization of treatment-resistant and non-treatment-resistant patients
Downstream analysis of miRNA, mRNA, proteomics and metabolomics data commonly involves several key steps to interpret the biological significance of the data. These steps include: 1) Raw data is firstly processed to assess the quality and remove low-quality reads across samples with specific software (fastp for RNAseq data, PTXQC for proteomics data, XCMS for metabolomics). 2) Next we map all reads to the reference and get the quantification matrix at different levels (STAR for RNAseq data, MaxQuant for protein data, MAVEN for metabolomics), after normalization and imputation, differential expressed genes and proteins could then be identified in treatment resistance group (Deseq2, edgeR). 3) Functional annotation and pathway analysis will be performed afterwards to link differentially expressed molecules to biological functions and pathways using databases like KEGG, GO, and Reactome to understand the biological context with treatment resistance traits. Ratio-based quantitative profiling approach that scales the absolute feature values of samples will be used to produce reproducible and comparable data suitable for integration across batches, labs, platforms and omics types (Quartet).
For smallRNAome sequencing 1,169 clinical samples collected during the first visit will be used. Epigenetic data from individuals with bipolar disorder in the PsyCourse study will be incorporated (Illumina EPIC array). The epigenetic data of updated Illumina EPIC array version 2 (192 PsyCourse individuals collected at the first visit) will be used for schizophrenia and bipolar patients. mRNA transcriptome data from 538 patients with schizophrenia-spectrum disorders at the first visit will be included.
Proteomics of 74 patients at visit 1 and protein profiling analysis of 214 from visit 1 will be incorporated. The plasma lipid profiles from 410 cases will be used. All analyses will be adjusted for age, sex and other relevant covariates in the context of repeated measures / linear mixed models.

3. Identification of a multi-omics signature associated with treatment resistance
We will apply machine learning tools, such as Support Vector Machine (SVM), least absolute shrinkage and selection operator (LASSO) and sparse partial least squares (SPLS) algorithm to identify features which can explain differences in the two cohorts of treatment-resistant patients and non-treatment-resistant patients, thus generating a treatment resistance -multi-omics signature for every disease separately. We will test if a multi-omics signature in comparison to the single-omic signature performs better to classify the patients in regard to sensitivity and specificity of the signatures. Wherever deemed necessary, we will use available data on sex, age, time of day of sampling, BMI, etc. to correct the biological measurements for potential confounding effects.

Resources needed

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v2_evnt_prcp_it_30
v2_evnt_prcp_b4_30
v2_evnt_prcp_f_30
v2_evnt_prcp_it_31
v2_evnt_prcp_b4_31
v2_evnt_prcp_f_31
v2_Antidepressants
v2_Antipsychotics
v2_Mood_stabilizers
v2_Tranquilizers
v2_Other_psychiatric
v2_adv
v2_medchange
v2_lith
v2_lith_prd
v2_smk_strt_stp
v2_no_cig
v2_alc_pst6_mths
v2_alc_5orm
v2_pst6_ill_drg
v2_panss_p1
v2_panss_p2
v2_panss_p3
v2_panss_p4
v2_panss_p5
v2_panss_p6
v2_panss_p7
v2_panss_n1
v2_panss_n2
v2_panss_n3
v2_panss_n4
v2_panss_n5
v2_panss_n6
v2_panss_n7
v2_panss_g1
v2_panss_g2
v2_panss_g3
v2_panss_g4
v2_panss_g5
v2_panss_g6
v2_panss_g7
v2_panss_g8
v2_panss_g9
v2_panss_g10
v2_panss_g11
v2_panss_g12
v2_panss_g13
v2_panss_g14
v2_panss_g15
v2_panss_g16
v2_panss_sum_pos
v2_panss_sum_neg
v2_panss_sum_gen
v2_panss_sum_tot
v2_idsc_itm1
v2_idsc_itm2
v2_idsc_itm3
v2_idsc_itm4
v2_idsc_itm5
v2_idsc_itm6
v2_idsc_itm7
v2_idsc_itm8
v2_idsc_itm9
v2_idsc_itm9a
v2_idsc_itm9b
v2_idsc_itm10
v2_idsc_itm11
v2_idsc_itm12
v2_idsc_itm13
v2_idsc_itm14
v2_idsc_itm15
v2_idsc_itm16
v2_idsc_itm17
v2_idsc_itm18
v2_idsc_itm19
v2_idsc_itm20
v2_idsc_itm21
v2_idsc_itm22
v2_idsc_itm23
v2_idsc_itm24
v2_idsc_itm25
v2_idsc_itm26
v2_idsc_itm27
v2_idsc_itm28
v2_idsc_itm29
v2_idsc_itm30
v2_idsc_sum
v2_ymrs_itm1
v2_ymrs_itm2
v2_ymrs_itm3
v2_ymrs_itm4
v2_ymrs_itm5
v2_ymrs_itm6
v2_ymrs_itm7
v2_ymrs_itm8
v2_ymrs_itm9
v2_ymrs_itm10
v2_ymrs_itm11
v2_ymrs_sum
v2_cgi_s
v2_cgi_c
v2_gaf
v2_med_pst_wk
v2_med_pst_sx_mths
v2_bdi2_itm1
v2_bdi2_itm2
v2_bdi2_itm3
v2_bdi2_itm4
v2_bdi2_itm5
v2_bdi2_itm6
v2_bdi2_itm7
v2_bdi2_itm8
v2_bdi2_itm9
v2_bdi2_itm10
v2_bdi2_itm11
v2_bdi2_itm12
v2_bdi2_itm13
v2_bdi2_itm14
v2_bdi2_itm15
v2_bdi2_itm16
v2_bdi2_itm17
v2_bdi2_itm18
v2_bdi2_itm19
v2_bdi2_itm20
v2_bdi2_itm21
v2_bdi2_sum
v2_asrm_itm1
v2_asrm_itm2
v2_asrm_itm3
v2_asrm_itm4
v2_asrm_itm5
v2_asrm_sum
v2_mss_itm1
v2_mss_itm2
v2_mss_itm3
v2_mss_itm4
v2_mss_itm5
v2_mss_itm6
v2_mss_itm7
v2_mss_itm8
v2_mss_itm9
v2_mss_itm10
v2_mss_itm11
v2_mss_itm12
v2_mss_itm13
v2_mss_itm14
v2_mss_itm15
v2_mss_itm16
v2_mss_itm17
v2_mss_itm18
v2_mss_itm19
v2_mss_itm20
v2_mss_itm21
v2_mss_itm22
v2_mss_itm23
v2_mss_itm24
v2_mss_itm25
v2_mss_itm26
v2_mss_itm27
v2_mss_itm28
v2_mss_itm29
v2_mss_itm30
v2_mss_itm31
v2_mss_itm32
v2_mss_itm33
v2_mss_itm34
v2_mss_itm35
v2_mss_itm36
v2_mss_itm37
v2_mss_itm38
v2_mss_itm39
v2_mss_itm40
v2_mss_itm41
v2_mss_itm42
v2_mss_itm43
v2_mss_itm44
v2_mss_itm45
v2_mss_itm46
v2_mss_itm47
v2_mss_itm48
v2_mss_sum
v2_whoqol_itm1
v2_whoqol_itm2
v2_whoqol_itm3
v2_whoqol_itm4
v2_whoqol_itm5
v2_whoqol_itm6
v2_whoqol_itm7
v2_whoqol_itm8
v2_whoqol_itm9
v2_whoqol_itm10
v2_whoqol_itm11
v2_whoqol_itm12
v2_whoqol_itm13
v2_whoqol_itm14
v2_whoqol_itm15
v2_whoqol_itm16
v2_whoqol_itm17
v2_whoqol_itm18
v2_whoqol_itm19
v2_whoqol_itm20
v2_whoqol_itm21
v2_whoqol_itm22
v2_whoqol_itm23
v2_whoqol_itm24
v2_whoqol_itm25
v2_whoqol_itm26
v2_whoqol_dom_glob
v2_whoqol_dom_phys
v2_whoqol_dom_psy
v2_whoqol_dom_soc
v2_whoqol_dom_env
v3_age
v3_interv_date
v3_clin_ill_ep_snc_lst
v3_clin_no_ep
v3_clin_fst_ill_ep_man
v3_clin_fst_ill_ep_dep
v3_clin_fst_ill_ep_mx
v3_clin_fst_ill_ep_psy
v3_clin_fst_ill_ep_dur
v3_clin_fst_ill_ep_hsp
v3_clin_fst_ill_ep_hsp_dur
v3_clin_fst_ill_ep_symp_wrs
v3_clin_fst_ill_ep_slf_end
v3_clin_fst_ill_ep_suic
v3_clin_fst_ill_ep_oth_end
v3_clin_fst_ill_ep_med_chg
v3_clin_fst_ill_ep_othr
v3_clin_sec_ill_ep_man
v3_clin_sec_ill_ep_dep
v3_clin_sec_ill_ep_mx
v3_clin_sec_ill_ep_psy
v3_clin_sec_ill_ep_dur
v3_clin_sec_ill_ep_hsp
v3_clin_sec_ill_ep_hsp_dur
v3_clin_sec_ill_ep_symp_wrs
v3_clin_sec_ill_ep_slf_end
v3_clin_sec_ill_ep_suic
v3_clin_sec_ill_ep_oth_end
v3_clin_sec_ill_ep_med_chg
v3_clin_sec_ill_ep_othr
v3_clin_add_oth_hsp
v3_clin_oth_hsp_nmb
v3_clin_oth_hsp_dur
v3_clin_othr_psy_med
v3_cur_psy_trm
v3_con_problems_snc_lst
v3_con_psy_snc_lst
v3_con_no_psy_hosp
v3_con_psy_hosp_dur
v3_con_psy_hosp_why_medchg
v3_con_psy_hosp_why_othr
v3_con_psy_hosp_why_othr_txt
v3_chg_empl_stat
v3_curr_paid_empl
v3_disabl_pens
v3_spec_emp
v3_wrk_abs_pst_6_mths
v3_cur_work_restr
v3_weight
v3_bmi
v3_waist
v3_evnt_prcp_it_1
v3_evnt_prcp_b4_1
v3_evnt_prcp_f_1
v3_evnt_prcp_it_2
v3_evnt_prcp_b4_2
v3_evnt_prcp_f_2
v3_evnt_prcp_it_3
v3_evnt_prcp_b4_3
v3_evnt_prcp_f_3
v3_evnt_prcp_it_4
v3_evnt_prcp_b4_4
v3_evnt_prcp_f_4
v3_evnt_prcp_it_5
v3_evnt_prcp_b4_5
v3_evnt_prcp_f_5
v3_evnt_prcp_it_6
v3_evnt_prcp_b4_6
v3_evnt_prcp_f_6
v3_evnt_prcp_it_7
v3_evnt_prcp_b4_7
v3_evnt_prcp_f_7
v3_evnt_prcp_it_8
v3_evnt_prcp_b4_8
v3_evnt_prcp_f_8
v3_evnt_prcp_it_9
v3_evnt_prcp_b4_9
v3_evnt_prcp_f_9
v3_evnt_prcp_it_10
v3_evnt_prcp_b4_10
v3_evnt_prcp_f_10
v3_evnt_prcp_it_11
v3_evnt_prcp_b4_11
v3_evnt_prcp_f_11
v3_evnt_prcp_it_12
v3_evnt_prcp_b4_12
v3_evnt_prcp_f_12
v3_evnt_prcp_it_13
v3_evnt_prcp_b4_13
v3_evnt_prcp_f_13
v3_evnt_prcp_it_14
v3_evnt_prcp_b4_14
v3_evnt_prcp_f_14
v3_evnt_prcp_it_15
v3_evnt_prcp_b4_15
v3_evnt_prcp_f_15
v3_evnt_prcp_it_16
v3_evnt_prcp_b4_16
v3_evnt_prcp_f_16
v3_evnt_prcp_it_17
v3_evnt_prcp_b4_17
v3_evnt_prcp_f_17
v3_evnt_prcp_it_18
v3_evnt_prcp_b4_18
v3_evnt_prcp_f_18
v3_evnt_prcp_it_19
v3_evnt_prcp_b4_19
v3_evnt_prcp_f_19
v3_evnt_prcp_it_20
v3_evnt_prcp_b4_20
v3_evnt_prcp_f_20
v3_evnt_prcp_it_21
v3_evnt_prcp_b4_21
v3_evnt_prcp_f_21
v3_evnt_prcp_it_22
v3_evnt_prcp_b4_22
v3_evnt_prcp_f_22
v3_evnt_prcp_it_23
v3_evnt_prcp_b4_23
v3_evnt_prcp_f_23
v3_evnt_prcp_it_24
v3_evnt_prcp_b4_24
v3_evnt_prcp_f_24
v3_evnt_prcp_it_25 v3_evnt_prcp_b4_25
v3_evnt_prcp_f_25
v3_evnt_prcp_it_26
v3_evnt_prcp_b4_26
v3_evnt_prcp_f_26
v3_evnt_prcp_it_27
v3_evnt_prcp_b4_27
v3_evnt_prcp_f_27
v3_evnt_prcp_it_28
v3_evnt_prcp_b4_28
v3_evnt_prcp_f_28
v3_evnt_prcp_it_29
v3_evnt_prcp_b4_29
v3_evnt_prcp_f_29
v3_evnt_prcp_it_30
v3_evnt_prcp_b4_30
v3_evnt_prcp_f_30
v3_evnt_prcp_it_31
v3_evnt_prcp_b4_31
v3_evnt_prcp_f_31
v3_suic_ide_snc_lst_vst
v3_scid_suic_ide
v3_scid_suic_thght_mth
v3_scid_suic_note_thgts
v3_suic_attmpt_snc_lst_vst
v3_no_suic_attmpt
v3_prep_suic_attp_ord
v3_suic_note_attmpt
v3_Antidepressants
v3_Antipsychotics
v3_Mood_stabilizers
v3_Tranquilizers
v3_Other_psychiatric
v3_adv
v3_medchange
v3_lith
v3_lith_prd
v3_smk_strt_stp
v3_no_cig
v3_alc_pst6_mths
v3_alc_5orm
v3_pst6_ill_drg
v3_panss_p1
v3_panss_p2
v3_panss_p3
v3_panss_p4
v3_panss_p5
v3_panss_p6
v3_panss_p7
v3_panss_n1
v3_panss_n2
v3_panss_n3
v3_panss_n4
v3_panss_n5
v3_panss_n6
v3_panss_n7
v3_panss_g1
v3_panss_g2
v3_panss_g3
v3_panss_g4
v3_panss_g5
v3_panss_g6
v3_panss_g7
v3_panss_g8
v3_panss_g9
v3_panss_g10
v3_panss_g11
v3_panss_g12
v3_panss_g13
v3_panss_g14
v3_panss_g15
v3_panss_g16
v3_panss_sum_pos
v3_panss_sum_neg
v3_panss_sum_gen
v3_panss_sum_tot
v3_idsc_itm1
v3_idsc_itm2
v3_idsc_itm3
v3_idsc_itm4
v3_idsc_itm5
v3_idsc_itm6
v3_idsc_itm7
v3_idsc_itm8
v3_idsc_itm9
v3_idsc_itm9a
v3_idsc_itm9b
v3_idsc_itm10
v3_idsc_itm11
v3_idsc_itm12
v3_idsc_itm13
v3_idsc_itm14
v3_idsc_itm15
v3_idsc_itm16
v3_idsc_itm17
v3_idsc_itm18
v3_idsc_itm19
v3_idsc_itm20
v3_idsc_itm21
v3_idsc_itm22
v3_idsc_itm23
v3_idsc_itm24
v3_idsc_itm25
v3_idsc_itm26
v3_idsc_itm27
v3_idsc_itm28
v3_idsc_itm29
v3_idsc_itm30
v3_idsc_sum
v3_ymrs_itm1
v3_ymrs_itm2
v3_ymrs_itm3
v3_ymrs_itm4
v3_ymrs_itm5
v3_ymrs_itm6
v3_ymrs_itm7
v3_ymrs_itm8
v3_ymrs_itm9
v3_ymrs_itm10
v3_ymrs_itm11
v3_ymrs_sum
v3_cgi_s
v3_cgi_c
v3_gaf
v3_med_pst_wk
v3_med_pst_sx_mths
v3_bdi2_itm1
v3_bdi2_itm2
v3_bdi2_itm3
v3_bdi2_itm4
v3_bdi2_itm5
v3_bdi2_itm6
v3_bdi2_itm7
v3_bdi2_itm8
v3_bdi2_itm9
v3_bdi2_itm10
v3_bdi2_itm11
v3_bdi2_itm12
v3_bdi2_itm13
v3_bdi2_itm14
v3_bdi2_itm15
v3_bdi2_itm16
v3_bdi2_itm17
v3_bdi2_itm18
v3_bdi2_itm19
v3_bdi2_itm20
v3_bdi2_itm21
v3_bdi2_sum
v3_asrm_itm1
v3_asrm_itm2
v3_asrm_itm3
v3_asrm_itm4
v3_asrm_itm5
v3_asrm_sum
v3_mss_itm1
v3_mss_itm2
v3_mss_itm3
v3_mss_itm4
v3_mss_itm5
v3_mss_itm6
v3_mss_itm7
v3_mss_itm8
v3_mss_itm9
v3_mss_itm10
v3_mss_itm11
v3_mss_itm12
v3_mss_itm13
v3_mss_itm14
v3_mss_itm15
v3_mss_itm16
v3_mss_itm17
v3_mss_itm18
v3_mss_itm19
v3_mss_itm20
v3_mss_itm21
v3_mss_itm22
v3_mss_itm23
v3_mss_itm24
v3_mss_itm25
v3_mss_itm26
v3_mss_itm27
v3_mss_itm28
v3_mss_itm29
v3_mss_itm30
v3_mss_itm31
v3_mss_itm32
v3_mss_itm33
v3_mss_itm34
v3_mss_itm35
v3_mss_itm36
v3_mss_itm37
v3_mss_itm38
v3_mss_itm39
v3_mss_itm40
v3_mss_itm41
v3_mss_itm42
v3_mss_itm43
v3_mss_itm44
v3_mss_itm45
v3_mss_itm46
v3_mss_itm47
v3_mss_itm48
v3_mss_sum
v3_whoqol_itm1
v3_whoqol_itm2
v3_whoqol_itm3
v3_whoqol_itm4
v3_whoqol_itm5
v3_whoqol_itm6
v3_whoqol_itm7
v3_whoqol_itm8
v3_whoqol_itm9
v3_whoqol_itm10
v3_whoqol_itm11
v3_whoqol_itm12
v3_whoqol_itm13
v3_whoqol_itm14
v3_whoqol_itm15
v3_whoqol_itm16
v3_whoqol_itm17
v3_whoqol_itm18
v3_whoqol_itm19
v3_whoqol_itm20
v3_whoqol_itm21
v3_whoqol_itm22
v3_whoqol_itm23
v3_whoqol_itm24
v3_whoqol_itm25
v3_whoqol_itm26
v3_whoqol_dom_glob
v3_whoqol_dom_phys
v3_whoqol_dom_psy
v3_whoqol_dom_soc
v3_whoqol_dom_env
v4_age
v4_interv_date
v4_clin_ill_ep_snc_lst
v4_clin_no_ep
v4_clin_fst_ill_ep_man
v4_clin_fst_ill_ep_dep
v4_clin_fst_ill_ep_mx
v4_clin_fst_ill_ep_psy
v4_clin_fst_ill_ep_dur
v4_clin_fst_ill_ep_hsp
v4_clin_fst_ill_ep_hsp_dur
v4_clin_fst_ill_ep_symp_wrs
v4_clin_fst_ill_ep_slf_end
v4_clin_fst_ill_ep_suic
v4_clin_fst_ill_ep_oth_end
v4_clin_fst_ill_ep_med_chg
v4_clin_fst_ill_ep_othr
v4_clin_sec_ill_ep_man
v4_clin_sec_ill_ep_dep
v4_clin_sec_ill_ep_mx
v4_clin_sec_ill_ep_psy
v4_clin_sec_ill_ep_dur
v4_clin_sec_ill_ep_hsp
v4_clin_sec_ill_ep_hsp_dur
v4_clin_sec_ill_ep_symp_wrs
v4_clin_sec_ill_ep_slf_end
v4_clin_sec_ill_ep_suic
v4_clin_sec_ill_ep_oth_end
v4_clin_sec_ill_ep_med_chg
v4_clin_sec_ill_ep_othr
v4_clin_add_oth_hsp
v4_clin_oth_hsp_nmb
v4_clin_oth_hsp_dur
v4_clin_othr_psy_med
v4_cur_psy_trm
v4_con_problems_snc_lst
v4_con_psy_snc_lst
v4_con_no_psy_hosp
v4_con_psy_hosp_dur
v4_con_psy_hosp_why_medchg
v4_con_psy_hosp_why_othr
v4_con_psy_hosp_why_othr_txt
v4_chg_empl_stat
v4_curr_paid_empl
v4_disabl_pens
v4_spec_emp
v4_wrk_abs_pst_6_mths
v4_cur_work_restr
v4_weight
v4_bmi
v4_waist
v4_opcrit
v4_evnt_prcp_it_1
v4_evnt_prcp_b4_1
v4_evnt_prcp_f_1
v4_evnt_prcp_it_2
v4_evnt_prcp_b4_2
v4_evnt_prcp_f_2
v4_evnt_prcp_it_3
v4_evnt_prcp_b4_3
v4_evnt_prcp_f_3
v4_evnt_prcp_it_4
v4_evnt_prcp_b4_4
v4_evnt_prcp_f_4
v4_evnt_prcp_it_5
v4_evnt_prcp_b4_5
v4_evnt_prcp_f_5
v4_evnt_prcp_it_6
v4_evnt_prcp_b4_6
v4_evnt_prcp_f_6
v4_evnt_prcp_it_7
v4_evnt_prcp_b4_7
v4_evnt_prcp_f_7
v4_evnt_prcp_it_8
v4_evnt_prcp_b4_8
v4_evnt_prcp_f_8
v4_evnt_prcp_it_9
v4_evnt_prcp_b4_9
v4_evnt_prcp_f_9
v4_evnt_prcp_it_10
v4_evnt_prcp_b4_10
v4_evnt_prcp_f_10
v4_evnt_prcp_it_11
v4_evnt_prcp_b4_11
v4_evnt_prcp_f_11
v4_evnt_prcp_it_12
v4_evnt_prcp_b4_12
v4_evnt_prcp_f_12
v4_evnt_prcp_it_13
v4_evnt_prcp_b4_13
v4_evnt_prcp_f_13
v4_evnt_prcp_it_14
v4_evnt_prcp_b4_14
v4_evnt_prcp_f_14
v4_evnt_prcp_it_15
v4_evnt_prcp_b4_15
v4_evnt_prcp_f_15
v4_evnt_prcp_it_16
v4_evnt_prcp_b4_16
v4_evnt_prcp_f_16
v4_evnt_prcp_it_17
v4_evnt_prcp_b4_17
v4_evnt_prcp_f_17
v4_evnt_prcp_it_18
v4_evnt_prcp_b4_18
v4_evnt_prcp_f_18
v4_evnt_prcp_it_19
v4_evnt_prcp_b4_19
v4_evnt_prcp_f_19
v4_evnt_prcp_it_20
v4_evnt_prcp_b4_20
v4_evnt_prcp_f_20
v4_evnt_prcp_it_21
v4_evnt_prcp_b4_21
v4_evnt_prcp_f_21
v4_evnt_prcp_it_22
v4_evnt_prcp_b4_22
v4_evnt_prcp_f_22
v4_evnt_prcp_it_23
v4_evnt_prcp_b4_23
v4_evnt_prcp_f_23
v4_evnt_prcp_it_24
v4_evnt_prcp_b4_24
v4_evnt_prcp_f_24
v4_evnt_prcp_it_25
v4_evnt_prcp_b4_25
v4_evnt_prcp_f_25
v4_evnt_prcp_it_26
v4_evnt_prcp_b4_26
v4_evnt_prcp_f_26
v4_evnt_prcp_it_27
v4_evnt_prcp_b4_27
v4_evnt_prcp_f_27
v4_evnt_prcp_it_28
v4_evnt_prcp_b4_28
v4_evnt_prcp_f_28
v4_evnt_prcp_it_29
v4_evnt_prcp_b4_29
v4_evnt_prcp_f_29
v4_evnt_prcp_it_30
v4_evnt_prcp_b4_30
v4_evnt_prcp_f_30
v4_evnt_prcp_it_31
v4_evnt_prcp_b4_31
v4_evnt_prcp_f_31
v4_suic_ide_snc_lst_vst
v4_scid_suic_ide
v4_scid_suic_thght_mth
v4_scid_suic_note_thgts
v4_suic_attmpt_snc_lst_vst
v4_no_suic_attmpt
v4_prep_suic_attp_ord
v4_suic_note_attmpt
v4_Antidepressants
v4_Antipsychotics
v4_Mood_stabilizers
v4_Tranquilizers
v4_Other_psychiatric
v4_adv
v4_medchange
v4_lith
v4_lith_prd
v4_alda_A
v4_alda_B1
v4_alda_B2
v4_alda_B3
v4_alda_B4
v4_alda_B5
v4_smk_strt_stp
v4_no_cig
v4_alc_pst6_mths
v4_alc_5orm
v4_pst6_ill_drg
v4_panss_p1
v4_panss_p2
v4_panss_p3
v4_panss_p4
v4_panss_p5
v4_panss_p6
v4_panss_p7
v4_panss_n1
v4_panss_n2
v4_panss_n3
v4_panss_n4
v4_panss_n5
v4_panss_n6
v4_panss_n7
v4_panss_g1
v4_panss_g2
v4_panss_g3
v4_panss_g4
v4_panss_g5
v4_panss_g6
v4_panss_g7
v4_panss_g8
v4_panss_g9
v4_panss_g10
v4_panss_g11
v4_panss_g12
v4_panss_g13
v4_panss_g14
v4_panss_g15
v4_panss_g16
v4_panss_sum_pos
v4_panss_sum_neg
v4_panss_sum_gen
v4_panss_sum_tot
v4_idsc_itm1
v4_idsc_itm2
v4_idsc_itm3
v4_idsc_itm4
v4_idsc_itm5
v4_idsc_itm6
v4_idsc_itm7
v4_idsc_itm8
v4_idsc_itm9
v4_idsc_itm9a
v4_idsc_itm9b
v4_idsc_itm10
v4_idsc_itm11
v4_idsc_itm12
v4_idsc_itm13
v4_idsc_itm14
v4_idsc_itm15
v4_idsc_itm16
v4_idsc_itm17
v4_idsc_itm18
v4_idsc_itm19
v4_idsc_itm20
v4_idsc_itm21
v4_idsc_itm22
v4_idsc_itm23
v4_idsc_itm24
v4_idsc_itm25
v4_idsc_itm26
v4_idsc_itm27
v4_idsc_itm28
v4_idsc_itm29
v4_idsc_itm30
v4_idsc_sum
v4_ymrs_itm1
v4_ymrs_itm2
v4_ymrs_itm3
v4_ymrs_itm4
v4_ymrs_itm5
v4_ymrs_itm6
v4_ymrs_itm7
v4_ymrs_itm8
v4_ymrs_itm9
v4_ymrs_itm10
v4_ymrs_itm11
v4_ymrs_sum
v4_cgi_s
v4_cgi_c
v4_gaf
v4_med_pst_wk
v4_med_pst_sx_mths
v4_bdi2_itm1
v4_bdi2_itm2
v4_bdi2_itm3
v4_bdi2_itm4
v4_bdi2_itm5
v4_bdi2_itm6
v4_bdi2_itm7
v4_bdi2_itm8
v4_bdi2_itm9
v4_bdi2_itm10
v4_bdi2_itm11
v4_bdi2_itm12
v4_bdi2_itm13
v4_bdi2_itm14
v4_bdi2_itm15
v4_bdi2_itm16
v4_bdi2_itm17
v4_bdi2_itm18
v4_bdi2_itm19
v4_bdi2_itm20
v4_bdi2_itm21
v4_bdi2_sum
v4_asrm_itm1
v4_asrm_itm2
v4_asrm_itm3
v4_asrm_itm4
v4_asrm_itm5
v4_asrm_sum
v4_mss_itm1
v4_mss_itm2
v4_mss_itm3
v4_mss_itm4
v4_mss_itm5
v4_mss_itm6
v4_mss_itm7
v4_mss_itm8
v4_mss_itm9
v4_mss_itm10
v4_mss_itm11
v4_mss_itm12
v4_mss_itm13
v4_mss_itm14
v4_mss_itm15
v4_mss_itm16
v4_mss_itm17
v4_mss_itm18
v4_mss_itm19
v4_mss_itm20
v4_mss_itm21
v4_mss_itm22
v4_mss_itm23
v4_mss_itm24
v4_mss_itm25
v4_mss_itm26
v4_mss_itm27
v4_mss_itm28
v4_mss_itm29
v4_mss_itm30
v4_mss_itm31
v4_mss_itm32
v4_mss_itm33
v4_mss_itm34
v4_mss_itm35
v4_mss_itm36
v4_mss_itm37
v4_mss_itm38
v4_mss_itm39
v4_mss_itm40
v4_mss_itm41
v4_mss_itm42
v4_mss_itm43
v4_mss_itm44
v4_mss_itm45
v4_mss_itm46
v4_mss_itm47
v4_mss_itm48
v4_mss_sum
v4_whoqol_itm1
v4_whoqol_itm2
v4_whoqol_itm3
v4_whoqol_itm4
v4_whoqol_itm5
v4_whoqol_itm6
v4_whoqol_itm7
v4_whoqol_itm8
v4_whoqol_itm9
v4_whoqol_itm10
v4_whoqol_itm11
v4_whoqol_itm12
v4_whoqol_itm13
v4_whoqol_itm14
v4_whoqol_itm15
v4_whoqol_itm16
v4_whoqol_itm17
v4_whoqol_itm18
v4_whoqol_itm19
v4_whoqol_itm20
v4_whoqol_itm21
v4_whoqol_itm22
v4_whoqol_itm23
v4_whoqol_itm24
v4_whoqol_itm25
v4_whoqol_itm26
v4_whoqol_dom_glob
v4_whoqol_dom_phys
v4_whoqol_dom_psy
v4_whoqol_dom_soc
v4_whoqol_dom_env
gsa_id
v1_smRNAome_id
v1_epic_id
v1_epic2_id
v1_lexo_id
v1_prot_id
v1_ab_prof_id
v1_lip_id