Make lithium great again – Precisely! - Université Paris Cité Accéder directement au contenu
Article Dans Une Revue Bipolar Disorders Année : 2021

Make lithium great again – Precisely!

Résumé

Abstract Background Despite its pivotal role in prophylaxis for bipolar-I-disorders (BD-I), variability in lithium (Li) response is poorly understood and only a third of patients show a good outcome. Converging research strands indicate that rest–activity rhythms can help characterize BD-I and might differentiate good responders (GR) and non-responders (NR). Methods Seventy outpatients with BD-I receiving Li prophylaxis were categorized as GR or NR according to the ratings on the retrospective assessment of response to lithium scale (Alda scale). Participants undertook 21 consecutive days of actigraphy monitoring of sleep quantity (SQ), sleep variability (SV) and circadian rhythmicity (CR). Results Twenty-five individuals were categorized as GR (36%). After correcting statistical analysis to minimize false discoveries, four variables (intra-daily variability; median activity level; amplitude; and relative amplitude of activity) significantly differentiated GR from NR. The odds of being classified as a GR case were greatest for individuals showing more regular/stable CR (1.41; 95% confidence interval (CI) 1.08, 2.05; p < 0.04). Also, there was a trend for lower SV to be associated with GR (odds ratio: 0.56; 95% CI 0.31, 1.01; p < 0.06). Conclusions To our knowledge, this is the largest actigraphy study of rest–activity rhythms and Li response. Circadian markers associated with fragmentation, variability, amount and/or amplitude of day and night-time activity best-identified GR. However, associations were modest and future research must determine whether these objectively measured parameters, singly or together, represent robust treatment response biomarkers. Actigraphy may offer an adjunct to multi-platform approaches aimed at developing personalized treatments or stratification of individuals with BD-I into treatment-relevant subgroups.
Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Mots clés

Domains Evidence map Longitudinal Modifiers Phenotype Misperception of sleep Sleep duration Sleep efficiency Sleep latency major depression patient satisfaction patient-reported experience measures clinical cohort DSM-5 ICD-11 Validity of diagnosis Diagnostic delay Delayed early intervention Mood stabilisers bipolar affective disorders depressive disorders genetics outcome studies Alda scale DNA methylation MS-HRM biomarkers bipolar disorder lithium response transferability validation Bipolar disorders Children Circadian rhythms First episode High risk Meta-regression Sleep quality Youth affective symptoms depression pain Bipolar disorder Major depression Personality Polygenic score Schizophrenia Suicidal behavior Epigenetics Genomics Major depressive disorder Mood disorders Multi-omics Transcriptomics chronic kidney disease magnetic resonance imaging radiomics childhood maltreatment childhood trauma mood recurrence physical abuse CKD-chronic kidney disease kidney microcysts nephrotoxicity Circadian Energy Lifestyle Practice guidelines Quality Sleep comorbidities prevalence suicide GWAS age at onset polarity at onset polygenic score antidepressants clinical severity expert centres side effects treatment-resistant depression COVID-19 burnout health care workers mediation outbreak post-traumatic sanitary crisis Genetic correlation Genome-wide association study Pleiotropy Polygenicity Suicide Suicide attempt actigraphy animal models biomarker chronobiology circadian clock gene levels of analysis light sleep circadian genes machine learning phenotype antecedents illness trajectories PSQI dimensions variability Infectious diseases Systematic review Vaccination Cognitive function Cohort Memory Obesity Cocaine addiction impulsiveness recurrent suicide attempt resilience serious suicide attempt substance use disorder Antecedents Bipolar I disorder Cohorts Comorbidities Family history Trajectories activity rhythms biological rhythms bipolar disorders chronotype circadian rhythms eveningness meta-analysis morningness rest systematic review Psychiatry Seasonal variation Solar insolation Sunlight Cognition Handedness Language disorders Laterality Neurodevelopment Lithium Methylation Response variability non-coding RNA CRP Childhood maltreatment Childhood trauma Metabolic abnormalities Metabolic syndrome Genotype-by-sex interaction Sex differences Clusters Data set Hierarchical agglomerative clustering Machine learning Alcohol use disorder Clinical trajectory Comorbidity Dual diagnosis Sequence of onset circadian gene early life stress gene expression Functioning Maintenance treatment Mood stabilizers Unsupervised machine learning Patient-reported experience measures health services research major depressive disorder patient experience psychiatry qualitative research quality of care schizophrenia severe mental illness Bipolar Disorders Depression Follow-up studies Medication adherence lithium response Adolescence Mania Meta-analysis Psychosis Blood-brain barrier Neurokinetics Pharmacodynamics Plexus choroid Transporters antipsychotics metabolic syndrome Catatonia Confusion Delirium Longitudinal study Prediction Recurrence Subtype Clinical markers Predictors Response Treatment Actigraphy Bipolar Course

Dates et versions

hal-03786909 , version 1 (23-09-2022)

Identifiants

Citer

Frank Bellivier, Allan Young, Jan Scott, Bruno Étain, David Cousins. Make lithium great again – Precisely!. Bipolar Disorders, 2021, 23 (2), pp.209-210. ⟨10.1111/bdi.13023⟩. ⟨hal-03786909⟩

Collections

INSERM UP-SANTE
29 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More