Summary: Researchers have identified six predictors that may help determine the correct amount of lithium to treat a patient with bipolar disorder.
Source: Karolinska Institute
Six predictors could help determine the amount of lithium needed to treat patients with bipolar disorder, according to a large study led by researchers at Karolinska Institutet in Sweden.
The study, published in the journal The Lancet Psychiatry, also pinpoints genetic markers that seem to influence how quickly the body eliminates lithium from its system.
“Our model could already now be used to predict how much lithium a patient with bipolar disorder will need. This could cut valuable time spent on finding the right dose for each patient, potentially with life-saving impact,” says Martin Schalling, professor at the Department of Molecular Medicine and Surgery, Karolinska Institutet and the study’s senior author.
Lithium is one of the most important treatments for patients with bipolar disorder, a condition that has been linked to an increased risk of suicide.
The chemical substance works as a mood stabilizer and reduces episodes of depression and mania. How much is needed vary greatly between individuals and finding the right dose for each patient is key as too much can be toxic while too little is ineffective.
To minimize the risk of side-effects, clinicians tend to initiate treatment at low doses that increase over time, meaning it could take months before the treatment has an effect.
To overcome this, researchers have long sought to find a model that could predict dose response in individual patients.
Previous studies have identified markers such as age, sex and kidney function as possible predictors of how quickly the body eliminates lithium from its system (lithium clearance), which can be used to determine the amount needed on a daily basis. However, most studies have been limited by small sample sizes.
In the current study, the researchers examined electronic health records and registry data from a total of 2,357 patients with bipolar disorder, which may represent the largest sample size for this kind of study to date. Both men and women in ages ranging from 17 to 89 were included, mainly of European ancestry.
The study found associations between the speed of lithium clearance and age, sex, kidney function (measured as eGFR), serum lithium concentrations and medication with diuretics and substances targeting the renin-angiotensin-aldosterone system (RAAS), which could be used to treat hypertension and other conditions.
“Our findings suggest that older patients, women, patients with reduced kidney function, and those taking certain medications require lower doses of lithium. Interestingly, we also discovered that the amount of lithium taken and lithium concentrations in the blood do not seem to be completely proportional, which goes somewhat against current thinking.
“Our model based on these predictors explained around 50–60% of the variance in lithium clearance, which is better than previous models and could be used to inform treatment decision,” says first author Vincent Millischer, a postdoctoral researcher at the Department of Molecular Medicine and Surgery, Karolinska Institutet, and resident in psychiatry at the Medical University of Vienna.
The study also found associations between a lower lithium clearance and one genetic locus on chromosome 11 and could also show that genetic variants affecting BMI and kidney function were associated with lithium clearance.
Even though adding the genetic markers only marginally improved the model’s predictive capability, the researchers say it opens the opportunity of personalized medicine in lithium treatment in the future.
“Next, we will test our model in a clinical trial to see if it can reduce the time it takes to find the right amount of lithium for each patient,” Martin Schalling says.
“If the outcome is positive, we will develop a digital app that could be used by psychiatrists in the future to help assess lithium dosage for patients with bipolar disorder.”
Improving lithium dose prediction using population pharmacokinetics and pharmacogenomics: a cohort genome-wide association study in Sweden
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.
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 (CLLi) associated with the clinical variables.
The residual effects after accounting for age and sex, representing the individual-level effects (CLLi,age/sex), were used as the dependent variable in a GWAS.
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 [R2]: R2cohort1=0·41 and R2cohort2=0·31; both p<0·0001), sex (R2cohort1=0·0063 [p=0·045] and R2cohort2=0·026 [p<0·0001]), eGFR (R2cohort1=0·38 and R2cohort2=0·20; both p<0·0001), comedication with diuretics (R2cohort1=0·0058 [p=0·014] and R2cohort2=0·0026 [p<0·0001]), and agents acting on the renin–aldosterone–angiotensin system (R2cohort1=0·028 and R2cohort2=0·015; both p<0·0001) were clinical predictors of CLLi.
Notably, an association between CLLi and serum lithium was observed, with a lower CLLi being associated with higher serum lithium (R2cohort1=0·13 and R2cohort2=0·15; both p<0·0001). In a GWAS of CLLi,age/sex, one locus was associated with a change in CLLi (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 CLLi 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.
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 CLLi introduces the opportunity of individualised medicine in lithium treatment.
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.