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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.
Since the NAD+-dependent histone deacetylases sirtuin-1 (SIRT1) and sirtuin-2 (SIRT2) are critically involved in epigenetics, endocrinology and immunology and affect the longevity in model organisms, we investigated their expression in brains of 3-month-old and 14–15 months old rat model of depression Flinders Sensitive Line (FSL) and control Flinders Resistant Line (FRL) rats. In view of the dysregulated NPY system in depression, we also studied NPY in young and old FSL to explore the temporal trajectory of depressive-like–ageing interaction. Sirt1, Sirt2 and Npy mRNA were determined using qRT-PCR in prefrontal cortex (PFC) from young and old FSL and FRL, and in hippocampi from young FSL and FRL. PFC: Sirt1 expression was decreased in FSL (p = 0.001). An interaction between age and genotype was found (p = 0.032); young FSL had lower Sirt1 with respect to both age (p = 0.026) and genotype (p = 0.001). Sirt2 was lower in FSL (p = 0.003). Npy mRNA was downregulated in FSL (p = 0.001) but did not differ between the young and old rat groups. Hippocampus: Sirt1 was reduced in young FSL compared to young FRL (p = 0.005). There was no difference in Sirt2 between FSL and FRL. Npy levels were decreased in hippocampus of young FSL compared to young FRL (p = 0.003). Effects of ageing could not be investigated due to loss of samples. To conclude, i this is the first demonstration that SIRT1 and SIRT2 are changed in brain of FSL, a rat model of depression; ii the changes are age-dependent; iii sirtuins are potential targets for treatment of age-related neurodegenerative diseases.
The aim of the current study was to investigate the importance of genetic and environmental effects in the variation of body mass index, and to investigate linkage for obesity to previously reported candidate regions on chromosome 2 and 10. A sample of 1422 twin pairs from the population based Swedish Twin Registry was used in order to estimate the genetic and environmental effects in the variation of body mass index by means of structural equation modeling. A selection of those, 51 concordant and 155 discordant for obesity, was used for the linkage analysis by implementing the “combined” Haseman-Elston approach. Heritability of body mass index ranged from 59–70%, implying that genetic effects were of importance for the variation of obesity, and there were significant sex and age differences. Linkage could not be verified in candidate regions of chromosomes 2 and 10, indicating that these genetic variants have a significant effect in extreme obese populations rather than in moderately obese Caucasians. However, the results were sensitive to issues related to power, minor effects of the genes, ethnic differences and the complex mechanism underlying obesity.
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