2023-05-08

072_ The influence of genetic liability for circadian rhythms alterations on bipolar disorder outcomes

Research Question and Aims

Patients with bipolar disorder (BD) display persistent disturbances of circadian rhythms (CR)[1]. Studies suggested that changes in sleep duration in euthymic BD were associated with relapse of mood episodes, increased risk of suicidal behavior, poor functioning, and worse cognitive outcomes[2,3,4]. Among biological markers of CR, modern GWAS have been used to derive PRSs for several sleep-related traits such as chronotype (eveningness–morningness)5, sleep duration6, insomnia7, or relative amplitude8 - a measure of CR disruptions, but their role in predicting BD outcomes has been overlooked.
We aim to: Examine the association between PRSs for circadian disruptions and cognitive, functioning, and clinical outcomes (including sleep patterns) in a sample of individuals with BD or Schizophrenia and healthy controls. Furthermore, we aim to integrate PRSs for these traits, and clinical variables into a machine-learning algorithm able to predict BD phenotypes (BD with poor cognitive and global functioning and more severe illness vs BD with good cognitive and global functioning and less severe illness) or BD diagnosis.

References
1. PMID: 34850507
2. PMID: 29776774
3. PMID: 30290235
4. PMID: 29286594
5. PMID: 30696823
6. PMID: 30531941
7. PMID: 30804565
8. PMID: 30120083

Analytic Plan

General Hypotheses:
Individual genetic liability for specific circadian rhythms traits is different in BD compared with HC or other psychiatric diagnoses. The integration between genetic and clinical factors would be able to discriminate different phenotypes of BD.

Concrete Hypotheses:
1. Subjects with bipolar disorder (BD) will have higher polygenic risk scores (PRS) for circadian rhythms alterations compared to psychoses spectrum disorders and to HC
2. Subjects with bipolar disorder (BD) with higher PRS for circadian rhythms alterations will have worse cognitive outcomes, functioning, and clinical outcomes (i.e. suicide, more illness severity) compared with patients with lower PRSs for these traits.
3. Integrating genetic (PRS) with socio-demographical and clinical factors will significantly increase the accuracy of prediction of BD (in the total population) and BD outcomes (among individuals with BD).

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 schizophrenia- or bipolar-spectrum diagnoses (we will use only data of genotyped patients or healthy controls)

Phenotype definition:
Baseline evaluations from the Bipogent and PsyCouse cohorts will be considered:
- Socio-demographics
- Psychiatric and medical history
- Medication data
- Family history of psychiatric disease
- Clinical evaluations: mood assessment, functioning, illness severity, suicide (attempt, behavior)
- Neurocognitive assessment: Processing Speed (TMT-A total time, key number of WAIS-III and DST); attention (digit forward); attention and working memory (digit backward of 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 for sleep duration, Morningness-Eveningness chronotypes, and relative sleep amplitude will be constructed by using the results of meta-analysis of genome-wide association studies (GWAS) on the topic. 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:
Partial correlation analyses adjusted by age and gender will be performed to assess the correlation between PRSs and cognitive outcomes, functioning, or other clinical outcomes, including lifetime number of suicide attempts, rapid cycling, predominant polarity, illness severity, substance use, seasonality, and sleep patterns (derived from the sub-items of the HDRS and IDS). Multiple linear regressions will be performed to study the association between the measured PRS and the described variables (cognitive and clinical). Separate multiple regression analyses were performed for each PRS, considered the main independent variable while controlling for sex, age, ancestry, and diagnostic category. For the development of machine learning algorithms, super vector machines will be used to predict diagnosis and BD outcomes. In order to compare the accuracy of linear and nonlinear modeling with the objective of identifying the presence of SNP x SNP interactions, the full datasets will be used to build the SVM models. The data will be randomly split into train/test subsets using 75%/25% proportions, then the SVM models will be built and tested on a large number (100 times) of such splits for each of the different kernels to obtain distributions of accuracy scores for each model. The metric used for all of the performance assessments was the Area Under the receiver operating characteristic (ROC) curve (AUC) metric, also known (ROC) score. Analyses will be conducted with Python using two main packages (scikit-learn and pandas) and R (for univariate or bivariate analyses).

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_ne
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

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).