2022-07-28

062_ Explore sex differences regarding clinical, neuropsychological and psychosocial variables in the Psycourse sample in patients with bipolar disorder and schizophrenia.

Research Question and Aims

Bipolar disorder (BD) and schizophrenia (SZ) are associated with neurocognitive impairments, even during periods of remission. Beyond clinical variables, other factors, such as sex, may contribute to neurocognitive performance in these disorders. Sex differences exist in terms of epidemiology, clinical phenomenology, course of illness, and other BD clinical characteristics as well as in patients with SZ and related psychoses. Interest in the study of sex differences in SZ and BD has increased considerably in recent decades, however evidence addressing sex differences in neurocognition in BD and SZ remains unclear given the limited number of studies published reporting conflicting results. Whereas some studies reported significant sex effects on neuropsychological performance independent of group (i.e. healthy controls -HC- vs. patients) others found significant diagnosis group by sex interaction indicating different neurocognitive patterns in BD and HC. The heterogeneity found across studies means that the results on the contribution of sex on neurocognitive differences should be treated with caution. The aim of the study will be to examine sex differences in neurocognition, psychosocial functioning and genetic risk in a large sample of patients with BD, SZ and healthy controls. Genetic variants are involved in the susceptibility to SCZ and BD. The genetic risk for BD or SCZ rarely depends on a single genetic alteration. Generally, many genetic variants, in the form of Single Nucleotide Polymorphisms (SNPs), contribute to the development of BD or SCZ. These genetic variants can be combined into a single polygenic risk score (PRS), that gives an idea of the cumulative genetic risk for BD or SCZ in a specific patient. A recent article has shown an association between the PRS and worse cognitive performance in mid- and old age, as well as worse school performance was seen in males only with no trend effects in females, indicating sex-specific effects of schizophrenia genetics on cognitive ability in healthy individuals across the entire lifespan.

Analytic Plan

Analysis will include the PsyCourse samples (patients with bipolar disorder and schizophrenia and healthy controls). Using these data we will investigate the association of sex with clinical and cognitive in order to define different profiles among bipolar and schizophrenic patients. Three groups (SZ, BD and controls) will be compared on clinical, neuropsychological and sociodemographic variables using linear and logistic models as appropriate and controlling for the presence of comorbidities/somatic disorder. MANOVA analysis will be performed to show overall differences in neuropsychological tests between groups. These analyses will be adjusted for relevant covariates. The following polygenic risk scores (PRS) will be calculated and included in the models: schizophrenia, bipolar disorder, major depressive disorder, educational attainment, cognitive performance. PRS-cs will be used for the weighting of the effect sizes in each respective discovery GWAS. PLINK 1.9 will be used for scoring and analyses will be run in R.
These analyses will be coordinated with PsyCourse proposal 009 in order to avoid/minimize overlapping approaches.

Resources needed

Neuropsychological variables
v1_nrpsy_tmt_A_rt
v1_nrpsy_tmt_B_rt
v1_nrpsy_dgt_sp_frw
v1_nrpsy_dgt_sp_bck
v1_nrpsy_dg_sym
v1_nrpsy_mwtb
v2_nrpsy_vlmt_corr
v2_nrpsy_vlmt_lss_d
v2_nrpsy_vlmt_lss_t
v2_nrpsy_vlmt_rec

Demographic
v1_sex
v1_age
v1_marital_stat
v1_partner
v1_no_bio_chld
v1_stp_chld
v1_liv_aln
v1_school
v1_prof_dgr
v1_ed_status
v1_curr_paid_empl
v1_disabl_pens

Clinical
v1_cur_psy_trm
v1_age_1st_out_trm
v1_daypat_inpat_trm
v1_age_1st_inpat_trm
v1_dur_illness
v1_1st_ep

Medication
v1_Antidepressants
v1_Antipsychotics
v1_Mood_stabilizers
v1_Tranquilizers
v1_Other_psychiatric
v1_lith
v1_lith_prd

Family
v1_fam_hist

Substance use
v1_ever_smkd
v1_no_cig
v1_lftm_alc_dep
v1_evr_ill_drg
v1_can

Somatic disease
v1_chol_trig
v1_hyperten
v1_ang_pec
v1_heart_att
v1_stroke
v1_diabetes
v1_hyperthy
v1_hypothy
v1_osteopor
v1_asthma
v1_autoimm
v1_cancer
v1_stom_ulc
v1_kid_fail
v1_epilepsy
v1_migraine
v1_parkinson
v1_tbi
v1_liv_cir_inf

Suicide
v1_scid_evr_suic_ide
v1_suic_attmpt
v1_scid_no_suic_attmpt

Diagnostic
v1_scid_dsm_dx_cat
v1_scid_age_MDE
v1_scid_no_MDE
v1_scid_age_mania
v1_scid_no_mania
v1_scid_age_hypomania
v1_scid_no_hypomania
v1_scid_ever_delus
v1_scid_ever_halls
v1_scid_ever_psyc
v1_scid_age_fst_psyc

Scales:
v1_idsc_sum
v1_ymrs_sum
v1_gaf
v1_bdi2_sum
v1_whoqol_itm14
v1_whoqol_dom_glob v1_whoqol_dom_phys
v1_whoqol_dom_psy
v1_whoqol_dom_soc
v1_whoqol_dom_env
v1_cape_itm37A
v1_cgi_s
v1_panss_sum_pos
v1_panss_sum_neg
v1_panss_sum_gen v1_panss_sum_tot
v1_med_pst_wk
v1_med_pst_sx_mths

Genomics data: Raw genotypes (GSA chip) and imputed data from PsyCourse patients and healthy controls.