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Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
Residual symptoms and cognitive impairment are among important sources of disability in patients with bipolar disorder. Methylene blue could improve such symptoms because of its potential neuroprotective effects.
We conducted a double-blind crossover study of a low dose (15 mg, ‘placebo’) and an active dose (195 mg) of methylene blue in patients with bipolar disorder treated with lamotrigine.
Thirty-seven participants were enrolled in a 6-month trial (trial registration: NCT00214877). The outcome measures included severity of depression, mania and anxiety, and cognitive functioning.
The active dose of methylene blue significantly improved symptoms of depression both on the Montgomery–Åsberg Depression Rating Scale and Hamilton Rating Scale for Depression (P = 0.02 and 0.05 in last-observation-carried-forward analysis). It also reduced the symptoms of anxiety measured by the Hamilton Rating Scale for Anxiety (P = 0.02). The symptoms of mania remained low and stable throughout the study. The effects of methylene blue on cognitive symptoms were not significant. The medication was well tolerated with transient and mild side-effects.
Methylene blue used as an adjunctive medication improved residual symptoms of depression and anxiety in patients with bipolar disorder.
Little is known about the impact of insulin resistance on bipolar
To examine the relationships between insulin resistance, type 2 diabetes
and clinical course and treatment outcomes in bipolar disorder.
We measured fasting glucose and insulin in 121 adults with bipolar
disorder. We diagnosed type 2 diabetes and determined insulin resistance.
The National Institute of Mental Health Life Chart was used to record the
course of bipolar disorder and the Alda scale to establish response to
prophylactic lithium treatment.
Patients with bipolar disorder and type 2 diabetes or insulin resistance
had three times higher odds of a chronic course of bipolar disorder
compared with euglycaemic patients (50% and 48.7% respectively
v. 27.3%, odds ratio (OR) = 3.07, P
= 0.007), three times higher odds of rapid cycling (38.5% and 39.5%
respectively v. 18.2%, OR = 3.13, P =
0.012) and were more likely to be refractory to lithium treatment (36.8%
and 36.7% respectively v. 3.2%, OR = 8.40,
P<0.0001). All associations remained significant
after controlling for antipsychotic exposure and body mass index in
Comorbid insulin resistance may be an important factor in resistance to
treatment in bipolar disorder.
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