2023-06-15
073_ Differences in the centrality of cognitive domains and polygenic scores in affective and psychotic disorders: a network analysis
Research Question and Aims
Cognitive deficits are a core feature of both schizophrenia (SCZ) and bipolar disorder (BD)[1]. These deficits impact attention, memory, processing speed, and executive functioning. The severity of cognitive deficits is associated with the severity of other symptoms, such as depressive and psychotic symptoms, in both disorders. Additionally, cognitive deficits can worsen during psychotic episodes, further impairing the individual's ability to function. The interplay between cognitive deficits and other symptoms is complex and bidirectional[2]. Therefore, understanding the relationship between cognitive deficits and other symptoms in SCZ and BD is essential for developing effective treatments that target both domains. The network approach, in this perspective, may be particularly useful for analyzing and visualizing complex relationships among psychopathology symptoms in specific populations[3]. In network analysis, nodes reflect symptoms, and edges between nodes reflect relationships between symptoms. Central symptoms reveal how different symptoms are interconnected within a disorder or symptom network, while bridge symptoms are particularly relevant in explaining comorbidity. Although risk factors and genetic liability are also expected to influence symptom interactions, only a few studies have integrated them into network models thus far. This study aims to identify the relationship between cognitive impairment, affective symptoms, psychotic symptoms, and functioning in a large sample of patients with affective or psychotic disorders using a network approach.
Additionally, the study aims to evaluate the influence of polygenic and environmental risk factors, such as trauma, on these symptoms.
References
1. PMID: 32663999
2. PMID: 23450289
3. PMID: 23537483
Analytic Plan
General Hypotheses:
The network structure and central and bridge symptoms are different among people with affective disorders compared with people with psychosis spectrum disorders. Patients with BD have a network model with higher centrality for BD-PRS, MDD-PRS and affective symptoms compared with SCZ or HC. Patients with SCZ have a network model with higher centrality for cognitive deficits, psychotic symptoms and SCZ-PRS compared with BD or HC.
Participants:
For this purpose, we would use cross-sectional data derived from two different cohorts:
1. The Bipogent Cohort: composed of outpatients of the Bipolar and Depressive Disorders Unit of the Hospital Clinic of Barcelona, diagnosed with BDI or BDII, recruited from February 2017 to July 2019. In the cohort, we have a total of 531 individuals phenotyped and genotyped (251 patients and 280 matched healthy controls).
2. The PsyCourse Cohort: 1200 participants of the PsyCourse study with bipolar-spectrum diagnoses (baseline evaluations)
Phenotype definition:
Baseline evaluations from the Bipogent and PsyCouse cohorts will be considered:
- Socio-demographics
- Psychiatric and medical history
- Medication data and treatment response
- Family history of psychiatric disease
- Clinical evaluations: mood assessment (HDRS, IDS, YMRS), functioning (FAST, GAF), early life stress (CTQ, CTS), illness severity (CGI), suicide (attempt, behavior)
- Neurocognitive assessment: Processing Speed (TMT-A total time); attention and working memory (WAIS-III digit span); executive functions (TMT-B); verbal memory (CVLT/VLMT), IQ (WAIS-III, MWT-B).
Polygenic Risk Scores:
Polygenic risk scores (PRS) for major psychiatric disorders (BD, schizophrenia, major depressive disorder) will be constructed based on the summary statistics of the latest genome-wide association studies (GWAS) for these disorders. 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 independently in the two cohorts, and data will be meta-analyzed.
Statistical Analyses:
Network analysis will be performed to explore the relationships between psychiatric symptoms (i.e., depressive, manic, positive, and negative symptoms), cognitive outcomes, functioning, trauma, clinical variables (substance use, suicide, predominant polarity, seasonality, duration of illness), BD-PRS, MD-PRS, SCZ-PRS, in individuals diagnosed BD, SCZ and HC. In addition, network results for different populations will be compared.
Network structures were estimated using Gaussian Markov random field (Costantini et al., 2015; Lauritzen, 1996) with the EBICglasso model. A nonparanormal transformation of the data will be applied before the network estimation as data did not follow a normal distribution. To control for spurious connections in the network estimation, an optimal regularization parameter will be selected by using graphical LASSO and extended Bayesian information criterion (EBIC). A threshold will be used to remove edges not surviving p-value <0.05.
In the network, nodes represent the studied variables and edges the bidirectional and undirected correlation between each pair of nodes. Thicker and more saturated edges represented stronger correlations; blue and red edges indicated positive and negative partial correlations, respectively.
Network centrality measures of expected influence, betweenness, and closeness of different nodes will be explored. The accuracy of edge weights will be measured by the 95% confidence intervals (CIs) computed through bootstrapping. The centrality indices' stability will be quantified using a case-dropping bootstrap procedure, and the correlation stability coefficient (CS-coefficient) between centrality indices for the full sample was calculated.
To examine whether network structure changes among patients with different diagnosis, we separately assessed differences in network structure, global strength, and significant edges in the three groups. Statistical significance was evaluated by a p-value <0.05. Network estimation and accuracy will be conducted by the “bootnet” R package and “qgraph” R package. Network comparison will be conducted by “Network Comparison Test” R package.
Resources needed
Socio-Demographics:
v1_sex
v1_age
v1_marital_stat
v1_partner
v1_liv_aln
v1_school
v1_prof_dgr
v1_ed_status
v1_curr_paid_empl
v1_disabl_pens
v1_spec_emp
v1_wrk_abs_pst_5_yrs
v1_cur_work_restr
Ethnicity:
v1_cntr_brth
Psychiatric Treatment:
v1_cur_psy_trm
v1_outpat_psy_trm
v1_daypat_inpat_trm
v1_age_1st_inpat_trm
v1_dur_illness
v1_age_1st_out_trm
v1_Antidepressants
v1_Antipsychotics
v1_Mood_stabilizers
v1_Tranquilizers
v1_Other_psychiatric
v1_lith
v1_lith_prd
Diagnosis (personal and familiar):
v1_fam_hist
v1_scid_dsm_dx_cat
v1_stat
v1_scid_dsm_dx
v1_scid_age_MDE
v1_scid_no_MDE
v1_scid_age_mania
v1_scid_no_mania
v1_scid_age_hypomania
v1_scid_ever_psyc
Psychiatric symptoms:
v1_ymrs_itm1
v1_ymrs_itm2
v1_ymrs_itm3
v1_ymrs_itm4
v1_ymrs_itm5
v1_ymrs_itm6
v1_ymrs_itm7
v1_ymrs_itm8
v1_ymrs_itm9
v1_ymrs_itm10
v1_ymrs_itm11
v1_ymrs_sum
v1_idsc_itm1
v1_idsc_itm2
v1_idsc_itm3
v1_idsc_itm4
v1_idsc_itm5
v1_idsc_itm6
v1_idsc_itm7
v1_idsc_itm8
v1_idsc_itm9
v1_idsc_itm9a
v1_idsc_itm9b
v1_idsc_itm10
v1_idsc_itm11
v1_idsc_itm12
v1_idsc_itm13
v1_idsc_itm14
v1_idsc_itm15
v1_idsc_itm16
v1_idsc_itm17
v1_idsc_itm18
v1_idsc_itm19
v1_idsc_itm20
v1_idsc_itm21
v1_idsc_itm22
v1_idsc_itm23
v1_idsc_itm24
v1_idsc_itm25
v1_idsc_itm26
v1_idsc_itm27
v1_idsc_itm28
v1_idsc_itm29
v1_idsc_itm30
v1_idsc_sum
v1_panss_p1
v1_panss_p2
v1_panss_p3
v1_panss_p4
v1_panss_p5
v1_panss_p6
v1_panss_p7
v1_panss_sum_pos
v1_panss_n1
v1_panss_n2
v1_panss_n3
v1_panss_n4
v1_panss_n5
v1_panss_n6
v1_panss_n7
v1_panss_sum_neg
v1_panss_g1
v1_panss_g2
v1_panss_g3
v1_panss_g4
v1_panss_g5
v1_panss_g6
v1_panss_g7
v1_panss_g8
v1_panss_g9
v1_panss_g10
v1_panss_g11
v1_panss_g12
v1_panss_g13
v1_panss_g14
v1_panss_g15
v1_panss_g16
v1_panss_sum_gen
v1_panss_sum_tot
v1_bdi2_itm1
v1_bdi2_itm2
v1_bdi2_itm3
v1_bdi2_itm4
v1_bdi2_itm5
v1_bdi2_itm6
v1_bdi2_itm7
v1_bdi2_itm8
v1_bdi2_itm9
v1_bdi2_itm10
v1_bdi2_itm11
v1_bdi2_itm12
v1_bdi2_itm13
v1_bdi2_itm14
v1_bdi2_itm15
v1_bdi2_itm16
v1_bdi2_itm17
v1_bdi2_itm18
v1_bdi2_itm19
v1_bdi2_itm20
v1_bdi2_itm21
v1_bdi2_sum
v1_mss_itm1
v1_mss_itm2
v1_mss_itm3
v1_mss_itm4
v1_mss_itm5
v1_mss_itm6
v1_mss_itm7
v1_mss_itm8
v1_mss_itm9
v1_mss_itm10
v1_mss_itm11
v1_mss_itm12
v1_mss_itm13
v1_mss_itm14
v1_mss_itm15
v1_mss_itm16
v1_mss_itm17
v1_mss_itm18
v1_mss_itm19
v1_mss_itm20
v1_mss_itm21
v1_mss_itm22
v1_mss_itm23
v1_mss_itm24
v1_mss_itm25
v1_mss_itm26
v1_mss_itm27
v1_mss_itm28
v1_mss_itm29
v1_mss_itm30
v1_mss_itm31
v1_mss_itm32
v1_mss_itm33
v1_mss_itm34
v1_mss_itm35
v1_mss_itm36
v1_mss_itm37
v1_mss_itm38
v1_mss_itm39
v1_mss_itm40
v1_mss_itm41
v1_mss_itm42
v1_mss_itm43
v1_mss_itm44
v1_mss_itm45
v1_mss_itm46
v1_mss_itm47
v1_mss_itm48
v1_mss_sum
Trauma:
v3_cts_els_dic
v3_cts_1_dic
v3_cts_2_dic
v3_cts_3_dic
v3_cts_4_dic
v3_cts_5_dic
Severity:
v1_cgi_s
v1_gaf
Neuropsychological Evaluation:
v1_nrpsy_com
v1_nrpsy_lng
v1_nrpsy_mtv
v1_nrpsy_tmt_A_rt
v1_nrpsy_tmt_A_err
v1_nrpsy_tmt_B_rt
v1_nrpsy_tmt_B_err
v1_nrpsy_dgt_sp_frw
v1_nrpsy_dgt_sp_bck
v1_nrpsy_dg_sym
v1_nrpsy_mwtb
Suicide-related variables:
v1_scid_evr_suic_ide
v1_scid_suic_ide
v1_scid_suic_thght_mth
v1_scid_suic_note_thgts
v1_suic_attmpt
v1_scid_no_suic_attmpt
v1_prep_suic_attp_ord
v1_suic_note_attmpt
v1_age_fst_suic_att
v1_age_sec_suic_att
v1_age_thr_suic_att
Genetic data (imputed).