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.

双相情感障碍患者对锂的反应与临床和跨诊断遗传因素有关。这些变量的预测组合可能有助于临床医生更好地预测哪些患者会对锂治疗产生反应。

结合跨诊断遗传和临床因素来预测双相情感障碍患者对锂的反应。

这项研究利用了作为国际锂遗传学联合会 (ConLi + Gen) 项目的一部分收集的遗传和临床数据 ( n = 1034)。计算了精神分裂症和重度抑郁症的多基因风险评分 (PRS)，然后使用交叉验证的机器学习回归方法与临床变量相结合。使用 leave-site-out 交叉验证对来自 10 个研究地点的 692 名双相情感障碍患者使用岭回归、弹性网和随机森林回归对单峰、多峰和遗传分层模型进行了训练和验证。然后，所有模型都在 342 名患者的独立测试集上进行了测试。然后在分类框架中测试表现最好的模型。

表现最好的线性模型解释了 5.1% ( P = 0.0001) 的锂反应方差，由临床变量、PRS 变量和它们之间的相互作用项组成。表现最好的非线性模型仅使用临床变量，并解释了 8.1% ( P = 0.0001) 的锂反应方差。先验基因组分层将非线性模型性能提高到 13.7% ( P = 0.0001)，并改进了锂反应的二元分类。该模型根据重度抑郁症和精神分裂症的元多基因负荷对患者进行分层，然后使用临床数据进行训练。

使用 PRS 首先对患者进行基因分层，然后使用临床预测因子训练机器学习模型，从而大大改善了锂反应预测。当将来与其他 PRS 和生物标志物一起使用时，这种方法可能有助于告知哪些患者最有可能对锂治疗做出反应。