ORCID Profile
0000-0001-6219-4945
Current Organisation
University of California, San Diego
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Publisher: Oxford University Press (OUP)
Date: 21-06-2016
DOI: 10.1093/HMG/DDW181
Publisher: Elsevier BV
Date: 03-2016
Publisher: Springer Science and Business Media LLC
Date: 05-05-2016
Publisher: Springer Science and Business Media LLC
Date: 16-03-2200
DOI: 10.1038/S41380-020-0689-5
Abstract: Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi
Publisher: Royal College of Psychiatrists
Date: 28-02-2022
DOI: 10.1192/BJP.2022.28
Abstract: 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.
Publisher: Frontiers Media SA
Date: 31-05-2018
Publisher: American Medical Association (AMA)
Date: 09-11-2017
Publisher: Wiley
Date: 06-12-2021
DOI: 10.1111/BDI.13162
Abstract: Bipolar disorder (BD) is characterized by episodes of depression and mania and disrupted circadian rhythms. Lithium is an effective therapy for BD, but only 30%–40% of patients are fully responsive. Preclinical models show that lithium alters circadian rhythms. However, it is unknown if the circadian rhythm effects of lithium are essential to its therapeutic properties. In secondary analyses of a multi‐center, prospective, trial of lithium for BD, we examined the relationship between circadian rhythms and therapeutic response to lithium. Using standardized instruments, we measured morningness, diurnal changes in mood, sleep, and energy (circadian rhythm disturbances) in a cross‐sectional study of 386 BD subjects with varying lithium exposure histories. Next, we tracked symptoms of depression and mania prospectively over 12 weeks in a subset of 88 BD patients initiating treatment with lithium. Total, circadian, and affective mood symptoms were scored separately and analyzed. Subjects with no prior lithium exposure had the most circadian disruption, while patients stable on lithium monotherapy had the least. Patients who were stable on lithium with another drug or unstable on lithium showed intermediate levels of disruption. Treatment with lithium for 12 weeks yielded significant reductions in total and affective depression symptoms. Lithium responders (Li‐Rs) showed improvement in circadian symptoms of depression, but non‐responders did not. There was no difference between Li‐Rs and nonresponders in affective, circadian, or total symptoms of mania. Exposure to lithium is associated with reduced circadian disruption. Lithium response at 12 weeks was selectively associated with the reduction of circadian depressive symptoms. We conclude that stabilization of circadian rhythms may be an important feature of lithium's therapeutic effects. Clinical Trials Registry: NCT0127253.
Location: United States of America
No related grants have been discovered for Michael McCarthy.