1. Make lithium great again – Precisely!
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Frank Bellivier, Bruno Etain, Allan H. Young, Jan Scott, David A. Cousins, Hôpitaux Universitaire Saint-Louis, Lariboisière, Fernand-Widal, Optimisation thérapeutique en Neuropsychopharmacologie (OPTeN (UMR_S_1144 / U1144)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), King‘s College London, Newcastle University [Newcastle], Newcastle Upon Tyne Hospitals NHS Foundation Trust, and Etain, Bruno
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Sleep latency ,recurrent suicide attempt ,childhood maltreatment ,non-coding RNA ,Meta-regression ,Comorbidity ,Clinical markers ,GWAS ,genetics ,Dual diagnosis ,validation ,Multi-omics ,response ,Energy ,MESH: Risk ,burnout ,substance use disorder ,Vaccination ,Laterality ,Catatonia ,Genomics ,Quality ,Diagnostic delay ,machine learning ,Mood stabilisers ,Suicidal behavior ,depression ,treatment-resistant depression ,Epigenetics ,levels of analysis ,Cognitive function ,addiction ,dimensions ,Longitudinal study ,age at onset ,post-traumatic ,MESH: Sex Characteristics ,medicine.medical_specialty ,Data set ,Methylation ,MESH: Sulfurtransferases ,Clusters ,03 medical and health sciences ,PSQI ,Bipolar I disorder ,LITHIUM USE ,Functioning ,Misperception of sleep ,Transcriptomics ,Genotype-by-sex interaction ,MESH: Humans ,variability ,patient experience ,Modifiers ,transferability ,biomarkers ,MESH: Adult ,MESH: Retrospective Studies ,Precision medicine ,Psychosis ,Pharmacodynamics ,antecedents ,major depression ,MESH: Female ,Cohorts ,Youth ,patient satisfaction ,Seasonal variation ,Response variability ,Practice guidelines ,Trajectories ,activity rhythms ,Argument ,quality of care ,Suicide attempt ,pain ,Sequence of onset ,bipolar disorder ,Psychiatry ,Maintenance treatment ,MESH: Suicidal Ideation ,outcome studies ,MESH: Depression, Postpartum ,Alda scale ,Clinical Practice ,Phenotype ,biological rhythms ,Tolerability ,radiomics ,biomarker ,affective symptoms ,circadian genes ,medicine.drug ,mood recurrence ,Validity of diagnosis ,DSM-5 ,health care workers ,severe mental illness ,medicine ,Bipolar disorders ,morningness ,Unsupervised machine learning ,Neurokinetics ,Sleep duration ,business.industry ,illness trajectories ,COVID-19 ,Delirium ,clinical cohort ,meta-analysis ,Treatment ,chronotype ,Bipolar ,Systematic review ,eveningness ,Delayed early intervention ,Sleep ,chronobiology ,Prediction ,chronic kidney disease ,qualitative research ,actigraphy ,Lithium (medication) ,MS-HRM ,CKD-chronic kidney disease ,0302 clinical medicine ,Cognition ,circadian gene ,MESH: Bipolar Disorder ,Confusion ,Children ,DNA methylation ,MESH: Middle Aged ,High risk ,Metabolic syndrome ,health services research ,3. Good health ,Adolescence ,Psychiatry and Mental health ,Transporters ,Sunlight ,Infectious diseases ,light ,CRP ,Personality ,MESH: Cross-Sectional Studies ,Polygenicity ,Memory ,MESH: Sleep Initiation and Maintenance Disorders ,Polygenic score ,Sex differences ,Circadian rhythms ,mediation ,Intensive care medicine ,resilience ,Medication adherence ,Biological Psychiatry ,Pleiotropy ,Lifestyle ,Metabolic abnormalities ,030227 psychiatry ,clinical severity ,030217 neurology & neurosurgery ,Sleep efficiency ,clock gene ,Neurodevelopment ,[SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,impulsiveness ,Subtype ,Alcohol use disorder ,Cocaine ,Recurrence ,MESH: Risk Factors ,Mood stabilizers ,magnetic resonance imaging ,rest ,Handedness ,Blood-brain barrier ,sanitary crisis ,nephrotoxicity ,Circadian ,Cohort ,Sleep quality ,animal models ,side effects ,serious suicide attempt ,Mania ,lithium ,antidepressants ,Solar insolation ,Domains ,Mood disorders ,Language disorders ,MESH: Suicide, Attempted ,patient-reported experience measures ,lithium response ,Genetic correlation ,Genome-wide association study ,prevalence ,Family history ,early life stress ,Major depressive disorder ,Follow-up studies ,comorbidities ,ICD-11 ,kidney microcysts ,Obesity ,MESH: Patient Acuity ,First episode ,Clinical trajectory ,suicide ,Plexus choroid ,childhood trauma ,outbreak ,Predictors ,Hierarchical agglomerative clustering ,MESH: Quantitative Trait Loci ,Evidence map ,MESH: France ,antipsychotics ,physical abuse ,[SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,Longitudinal ,Schizophrenia ,gene expression ,expert centres ,polarity at onset ,Course ,business ,depressive disorders ,bipolar affective disorders - 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.
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