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利用群体药代动力学和药物基因组学改善锂剂量预测:瑞典的一项队列全基因组关联研究。

Improving lithium dose prediction using population pharmacokinetics and pharmacogenomics: a cohort genome-wide association study in Sweden.

机构信息

Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Centre for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.

Department of Psychiatry, Columbia University, NY, USA; Department of Biostatistics, Columbia University Mailman School of Public Health, NY, USA; Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Healthcare Services, Stockholm, Sweden.

出版信息

Lancet Psychiatry. 2022 Jun;9(6):447-457. doi: 10.1016/S2215-0366(22)00100-6.

Abstract

BACKGROUND

Lithium is the most effective treatment for bipolar disorder, resulting in strong suicide prevention effects. The therapeutic range of lithium, however, is narrow and treatment initiation requires individual titration to address inter-individual variability. We aimed to improve lithium dose prediction using clinical and genomic data.

METHODS

We performed a population pharmacokinetic study followed by a genome-wide association study (GWAS), including two clinical Swedish cohorts. Participants in cohort 1 were from specialised outpatient clinics at Huddinge Hospital, in Stockholm, Sweden, and participants in cohort 2 were identified using the Swedish National Quality Registry for Bipolar disorder (BipoläR). Patients who received a lithium dose corresponding to at least one tablet of lithium sulphate (6 mmol) per day and had clinically relevant plasma concentrations of lithium were included in the study. Data on age, sex, bodyweight, height, creatinine concentration, estimated glomerular filtration rate (eGFR), lithium preparation, number of tablets of lithium per day, serum lithium concentration, and medications affecting kidney function (C09 antihypertensives, C03 [except C03D] sodium-retaining diuretics, and non-steroidal anti-inflammatory drugs) were obtained retrospectively for several timepoints when possible from electronic health records, BipoläR, and the Swedish prescription registry. The median time between timepoints was 1·07 years for cohort 1 and 1·09 years for cohort 2. The primary outcome of interest was the natural logarithm of total body clearance for lithium (CL) associated with the clinical variables. The residual effects after accounting for age and sex, representing the individual-level effects (CL), were used as the dependent variable in a GWAS.

FINDINGS

2357 patients who were administered lithium (1423 women [60·4%] and 934 men [39·6%]; mean age 53·6 years [range 17-89], mainly of European descent) were included and 5627 data points were obtained. Age (variance explained [R]: R=0·41 and R=0·31; both p<0·0001), sex (R=0·0063 [p=0·045] and R=0·026 [p<0·0001]), eGFR (R=0·38 and R=0·20; both p<0·0001), comedication with diuretics (R=0·0058 [p=0·014] and R=0·0026 [p<0·0001]), and agents acting on the renin-aldosterone-angiotensin system (R=0·028 and R=0·015; both p<0·0001) were clinical predictors of CL. Notably, an association between CL and serum lithium was observed, with a lower CL being associated with higher serum lithium (R=0·13 and R=0·15; both p<0·0001). In a GWAS of CL, one locus was associated with a change in CL (rs583503; β=-0·053 [95% CI -0·071 to -0·034]; p<0·00000005). We also found enrichment of the associations with genes expressed in the medulla (p=0·0014, corrected FDR=0·04) and cortex of the kidney (p=0·0015, corrected FDR=0·04), as well as associations with polygenic risk scores for eGFR (p value threshold: 0·05, p=0·01), body-mass index (p value threshold: 0·05, p=0·00025), and blood urea nitrogen (p value threshold: 0·001, p=0·00043). The model based on six clinical predictors explained 61·4% of the variance in CL in cohort 1 and 49·8% in cohort 2. Adding genetic markers did not lead to major improvement of the models: within the subsample of genotyped individuals, the variance explained only increased from 59·32% to 59·36% in cohort 1 and from 49·21% to 50·03% in cohort 2 when including rs583503 and the four first principal components.

INTERPRETATION

Our model predictors could be used clinically to better guide lithium dosage, shortening the time to reach therapeutic concentrations, thus improving care. Identification of the first genomic locus and PRS to be associated with CL introduces the opportunity of individualised medicine in lithium treatment.

FUNDING

Stanley Medical Research Institute, Swedish Research Council, Swedish Foundation for Strategic Research, Swedish Brain Foundation, Swedish Research Council, Söderström-Königska Foundation, Bror Gadelius Minnesfond, Swedish Mental Health Fund, Karolinska Institutet and Hospital.

摘要

背景

锂是治疗双相情感障碍最有效的药物,具有很强的预防自杀作用。然而,锂的治疗范围很窄,治疗开始时需要个体化滴定以解决个体间的差异。我们旨在使用临床和基因组数据来改善锂剂量预测。

方法

我们进行了一项群体药代动力学研究,随后进行了全基因组关联研究(GWAS),包括两个瑞典临床队列。队列 1 的参与者来自斯德哥尔摩 Huddinge 医院的专门门诊诊所,队列 2 的参与者是通过瑞典双相情感障碍国家质量登记处(BipoläR)确定的。纳入研究的患者每天接受至少一片硫酸锂(6 mmol)的锂剂量,并且具有临床相关的锂血浆浓度。尽可能从电子健康记录、BipoläR 和瑞典处方登记处获取年龄、性别、体重、身高、肌酐浓度、估算肾小球滤过率(eGFR)、锂制剂、每日锂片数、血清锂浓度和影响肾功能的药物(C09 抗高血压药、C03[除 C03D 外]保钾利尿剂和非甾体抗炎药)的数据。队列 1 的中位数时间间隔为 1.07 年,队列 2 为 1.09 年。感兴趣的主要结局是与临床变量相关的锂总清除率的自然对数(CL)。在考虑年龄和性别后,代表个体水平影响(CL)的剩余效应被用作 GWAS 的因变量。

结果

共纳入 2357 名接受锂治疗的患者(1423 名女性[60.4%]和 934 名男性[39.6%];平均年龄 53.6 岁[范围 17-89],主要为欧洲血统),获得了 5627 个数据点。年龄(解释方差[R]:R=0.41 和 R=0.31;均<0.0001)、性别(R=0.0063[p=0.045]和 R=0.026[p<0.0001])、eGFR(R=0.38 和 R=0.20;均<0.0001)、利尿剂联合用药(R=0.0058[p=0.014]和 R=0.0026[p<0.0001])和肾素-血管紧张素-醛固酮系统作用药物(R=0.028 和 R=0.015;均<0.0001)是 CL 的临床预测因子。值得注意的是,观察到 CL 与血清锂之间存在关联,较低的 CL 与较高的血清锂相关(R=0.13 和 R=0.15;均<0.0001)。在 CL 的 GWAS 中,一个位点与 CL 的变化相关(rs583503;β=-0.053[95%CI-0.071 至-0.034];p<0.00000005)。我们还发现与肾脏髓质(p=0.0014,校正 FDR=0.04)和皮质(p=0.0015,校正 FDR=0.04)中表达的基因的关联存在富集,以及与 eGFR(p 值阈值:0.05,p=0.01)、体重指数(p 值阈值:0.05,p=0.00025)和血尿素氮(p 值阈值:0.001,p=0.00043)的多基因风险评分的关联。基于六个临床预测因子的模型解释了队列 1 中 CL 变异的 61.4%和队列 2 中 49.8%的变异。添加遗传标记并没有导致模型的重大改进:在基因分型个体的亚样本中,当包括 rs583503 和前四个主要成分时,队列 1 中解释的方差仅从 59.32%增加到 59.36%,队列 2 中从 49.21%增加到 50.03%。

结论

我们的模型预测因子可以在临床上用于更好地指导锂剂量,缩短达到治疗浓度的时间,从而改善护理。确定与 CL 相关的第一个基因组座和 PRS 为锂治疗中的个体化医学开辟了机会。

资助

斯坦利医学研究所、瑞典研究理事会、瑞典战略研究基金会、瑞典大脑基金会、瑞典研究理事会、Söderström-Königska 基金会、Bror Gadelius 纪念基金会、瑞典心理健康基金会、卡罗林斯卡研究所和医院。

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