Abstract
Lithium is a drug primarily used to treat psychiatric disorders and has shown significant efficacy in treating bipolar disorder (BD) and major depressive disorder (MDD). Although lithium can stabilize emotional fluctuations and prevent the onset of depressive and manic episodes, case reports of patients treated with lithium developing kidney dysfunction after being infected with SARS-CoV-2 emerged during the COVID-19 pandemic. We used the National COVID Cohort Collaborative (N3C) dataset to investigate the relationship between patients with lithium exposure and with SAR-Co-V-2 infection, and kidney dysfunction. Using data from March 1, 2021, to September 30, 2022, patients were classified as whether they were infected with SARS-CoV-2. We used the estimated glomerular filtration rate (eGFR) to observe whether there are significant differences between the two groups. Our findings suggest that the impact of lithium treatment and COVID-19 on kidney function may not be significant, consistent with most other studies’ findings.
Introduction
Lithium is a well-established first line treatment of bipolar disorder, in particular for long-term preventative maintenance therapy [1,2,3]. There are, however, well recognized side-effects that require ongoing monitoring and include measures of kidney and thyroid functioning. Lithium is excreted from the body through the kidney and any intervention or condition that specifically has the potential to affect kidney functioning with a direct effect on lithium excretion. Diminished kidney functioning is likely to result in decreased lithium excretion and thereby elevated blood levels, which is of major concern due to the narrow therapeutic window of lithium [4,5,6].
The COVID-19 outbreak in 2020 raised concerns for patients with chronic conditions in general and also for patients receiving lithium therapy. Although COVID-19 is primarily a respiratory disease, the kidneys may be one of the target organs of infection by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) due to the binding to the ACE2 receptors in the kidney[7]. Studies have shown that acute kidney injury (AKI) is a relatively common complication of COVID-19 and a marker of disease severity [8,9,10,11,12], raising questions of whether patients exposed to lithium and COVID/SARS are at higher risk. Despite reports that lithium may have a protective effect in SARS-CoV-2 infection[13][14], many people taking lithium still become infected[12].
Reports of patients with COVID-19 infection and with lithium levels exceeding therapeutic thresholds experiencing AKI and subsequent lithium toxicity [15]. Additionally, there have been case reports of patients with controlled lithium use without toxicity becoming infected and suddenly experiencing lithium toxicity. For instance, a 68-year-old male with a 20-year history of BD who had been on long-term lithium therapy developed altered mental status and symptoms of lithium toxicity after becoming ill with COVID-19. Laboratory tests revealed kidney dysfunction, such as elevated creatinine levels and abnormal blood sodium and potassium levels [16]. A similar case was reported in a 42-year-old female with a 10-year history of BD. However, due to the experience gained from the previous case, this patient had her lithium dosage proactively reduced early in the course of the illness, ultimately avoiding lithium-induced injury or toxicity [16]. To date, case reports of COVID-19-associated lithium toxicity that emphasize the temporal relationship between COVID-19 illness and lithium toxicity, suggest an association between lithium toxicity and AKI [14]. This association highlights the potential increase in lithium toxicity cases among patients undergoing long-term lithium therapy who are concurrently infected with SARS-CoV-2 during the ongoing COVID-19 pandemic [17]. It underscores the necessity for close monitoring of lithium levels in patients with bipolar disorder who are battling COVID-19 [18]. To validate the identified association, it is important to further explore this relationship in a bigger data set.
We utilized the National COVID Cohort Collaborative (N3C), to explore the relationship between lithium, COVID exposure and AKI. The N3C was established in 2020 and includes data on patients with and without COVID infection from over institutions [19]. Using this large N3C resource, we conducted a retrospective cohort study of kidney function in patients who had been on long-term lithium therapy and exposed to COVID.
Methods
Dataset
As of March 1, 2025, the N3C dataset contains 22.8 million patient data from 98 locations across the United States, covering 8,914,402 COVID-19 cases, 33.9B data records, and 3.3B clinical observations. These data are contributed to and maintained by 410 research institutions, 603 research projects, and 33 professional field teams. In general, N3C coordinates data from different sources and common data models (such as ACT, PCORnet, TriNetX, etc.) into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). OMOP CDM provides N3C with a standardized framework that enables clinical data from different sources to be integrated, analyzed, and shared to support COVID-19 research and discovery. N3C’s analytical environment supports complex data analysis using OMOP CDM structured data, including statistical analysis, machine learning, and data visualization.
Overview/approach
Our primary outcome of interest was elevated eGFR. We compared patients who were on lithium therapy and infected with COVID-19 during the study period with those who were on long-term lithium therapy but were not infected with COVID-19 during the study period. Our aim was to test the hypothesis that patients on lithium therapy who were infected with COVID-19 during the study period would have a significantly higher eGFR compared to those on lithium therapy who were not infected with COVID-19 during the study period. Additionally, we focused on analyzing the changes in eGFR before and after infection among the patients in the infection group. All concept_ids required for identifying relevant conditions, drug exposures, and measurements in the N3C database were obtained from the Athena browser provided by OHDSI [20].
Cohort design, inclusion and exclusion criteria
In our cohort selection, we first screened patients who had taken or had a record of lithium treatment at any time in N3C. However, our inclusion criteria are very strict, because our study is about whether COVID-19 infection will cause additional renal burden for patients taking lithium, so we believe that in our study, we must strictly control that patients must have been taking lithium during our selected study period. Because in N3C, different patients have different medication start times and medication end times, we screened these patients by their medication start date (drug_exposure_start_date) and medication end date (drug_exposure_end_date). We selected the time between March 1, 2021 and September 30, 2022, because this is the time period with the highest infection rate of the epidemic. We have four exclusion criteria: 1) No record of COVID-19 infection between March 1, 2021 and September 30, 2022; 2) Patients under 18 years old and over 80 years old; 3) Patients with a record of AKI within 21 days before COVID-19 infection; 4) Patients with end-stage renal disease (ESKD). The infection group was defined based on either a diagnosis of COVID-19 recorded in the condition_occurrence table or a laboratory-confirmed positive test for SARS-CoV-2 recorded in the measurement table. The control group included individuals who had no documented diagnosis of COVID-19 and no recorded positive test results throughout the study period.
Statistical analysis
We used Linear Mixed Effects Modeling (LMM) to analyze the differences in estimated glomerular filtration rate (eGFR) in the control group and the infection group. LMM allows us to consider intra-individual correlation, which is necessary because each patient has multiple measurements. In addition, we also analyzed the effects of age, gender, and comorbidities such as Coronary Heart Disease (CHF), Hypertension (HBP), diabetes, liver disease, kidney disease, and blood pressure disease on eGFR. Second, to gain a deeper understanding of the impact of COVID-19 infection on patients’ renal function, we conducted a detailed time series analysis of patients in the infection group, we used the locally weighted regression (LOESS) method to generate trend lines to reveal the changing pattern of eGFR before and after infection.
All analyses were performed on the N3C Enclave platform using R, Python, and SQL. In the Python analysis, we used the statsmodels package to build the LMM model, and in the R language analysis, we used packages such as ggplot 2 and dplyr to visualize the data.
Results
As of March 1, 2025, 111,371 patients in the N3C database had at least 1 recorded exposure to lithium, and 1,525 patients had documented continued records of taking lithium between March 1, 2021 and September 30, 2022 (Figure 1). During the process, we excluded 1,099 patients who had no COVID-19 illness records during the study period, 117 patients who were younger than 18 years old or older than 80 years old, and 7 patients with confirmed end-stage renal disease (ESKD). 166 patients who continued to take lithium during the study period but were not infected with COVID-19 (Control Group), and 136 patients who continued to take lithium during the study period and were diagnosed with COVID-19 (Infection Group).
Figure 1.
Flowchart of Patient Selection for the Control and Infection Groups in N3C
Table 1 shows the demographic and clinical characteristics of the sample. The mean age of the two groups was similar (47.31 years in the control group vs. 44.58 years in the infection group)., The majority of patients were non-Hispanic. Smaller proportions of patients in both groups had congestive heart failure and high blood pressure, but there were slightly more patients with diabetes and liver disease in both groups, with 28.68% of patients with diabetes in the control group and 25.3% of patients with diabetes in the infection group. 21.32% of patients in the control group had liver disease, while 16.27% of patients in the infection group had liver disease. In addition, in terms of mental illness, patients with BD accounted for a higher proportion in both groups, accounting for 77.94% in the control group and 80.12% in the infection group, which is consistent with the characteristics of the study population. In terms of vaccination status, 42.45% of patients in the infection group received the new crown vaccine, while the proportion in the control group was slightly higher, at 48.53%.
Table 1.
Demographic and clinical variables of patients exposed to Lithium, with and without SARS/COVID exposure
| Overall(N = 302) | ||
|---|---|---|
| with COVID Infection | without COVID infection | |
| Total number of people | 136 | 166 |
| Age | ||
| Mean(SD) | 44.58(16.33) | 47.31(16.92) |
| Median[Min,Max] | 45[18,79] | 47[18,79] |
| Sex | ||
| Male | 60(44.12%) | 62(37.35%) |
| Female | 60(44.12%) | 84(50.60%) |
| No record | 16(11.76%) | 20(12.05%) |
| Ethnicity | ||
| Hispanic or Latino | 10(7.35%) | 12(7.23%) |
| Not Hispanic or Latino | 105(77.21%) | 127(76.51%) |
| No record | 21(15.44%) | 27(16.27%) |
| Race | ||
| White | 104(76.47%) | 122(73.49%) |
| Non-White | 32(23.53%) | 44(26.51%) |
| Congestive heart failure | ||
| No | 131(96.32%) | 161(96.99%) |
| Yes | 5(3.68%) | 5(3.01%) |
| High blood pressure | ||
| No | 134(98.53%) | 165(99.4%) |
| Yes | 2(1.47%) | 1(0.6%) |
| Diabetes | ||
| No | 97(71.32%) | 124(74.7%) |
| Yes | 39(28.68%) | 42(25.3%) |
| Liver disease | ||
| No | 107(78.68%) | 139(83.73%) |
| Yes | 29(21.32%) | 27(16.27%) |
| Renal disease | ||
| No | 133(97.79%) | 159(95.78%) |
| Yes | 3(2.21%) | 7(4.22%) |
| Bipolar disorder | ||
| No | 30(22.06%) | 33(19.88%) |
| Yes | 106(77.94%) | 133(80.12%) |
| COVID vaccination | ||
| No | 78(57.55%) | 85(51.47%) |
| Yes | 58(42.45%) | 81(48.53%) |
Table 2 shows the results of the linear mixed effects regression analysis, showing the effects of COVID-19 infection, age, and gender on eGFR. Specifically, COVID-19 infection did not significantly affect eGFR (p=0.610), but gender had a significant effect on eGFR (p=0.037), with males having an average eGFR 7.739 units higher than females. In addition, age was divided into four groups, namely 18-25, 25-40, 40-60, and 60-80. Among them, the two age groups of 18-25 and 25-40 had an effect on eGFR, with their P values of 0.008 and 0.025, respectively, indicating that the younger the age, the greater the effect on eGFR. Table 3 analyzes the effects of COVID-19 infection and specific medical comorbidities on eGFR. From the results, the effects of congestive heart failure, hypertension, diabetes, liver disease, kidney disease, and bipolar disorder on eGFR were not statistically significant. This suggests that in our study sample, the effects of these medical comorbidities and COVID-19 infection on eGFR may be small or not significantly associated.
Table 2.
Linear mixed effect regression of the effect of COVID-19 illness, age , gender on eGFR
| Variable | Coefficient | Std. Error | p-Value | 95% CI (Lower) | 95% CI (Upper) |
|---|---|---|---|---|---|
| Infection with Covid-19 | 0.887 | 1.740 | 0.610 | -2.523 | 4.298 |
| Gender (Male) | 7.739 | 3.708 | 0.037 | 0.470 | 15.007 |
| Age 18-25 | 53.462 | 20.081 | 0.008 | 14.103 | 92.820 |
| Age 25-40 | 43.773 | 19.464 | 0.025 | 5.624 | 81.923 |
| Age 40-60 | 28.962 | 19.143 | 0.130 | -8.558 | 66.482 |
| Age 60-80 | 20.272 | 19.186 | 0.291 | -17.331 | 57.875 |
Table 3.
Linear mixed effect regression of COVID-19 infection and specific medical comorbidities on eGFR
| Variable | Coefficien t | Std. Error | p-valu e | 95% CI (Lower) | 95% CI (Upper) |
|---|---|---|---|---|---|
| Infection with Covid-19 | 1.102 | 1.789 | 0.538 | -2.404 | 4.607 |
| Congestive heart failure | -10.461 | 12.848 | 0.416 | -35.643 | 14.72 |
| High blood pressure | -6.168 | 24.265 | 0.799 | -53.727 | 41.391 |
| Diabetes | -6.538 | 4.752 | 0.169 | -15.851 | 2.776 |
| Liver Disease | 6.662 | 5.231 | 0.203 | -3.591 | 16.915 |
| Renal Disease | -1.145 | 13.132 | 0.931 | -26.883 | 24.593 |
| Bipolar disease | -0.542 | 5.215 | 0.917 | -10.762 | 9.678 |
Figure 2 shows the trend of eGFR changes in patients from the infection group before and after COVID-19 infection. In order to more intuitively show the trend of eGFR changes, we used a scatter plot combined with a smooth trend line. Among them, each point in the scatter plot represents the eGFR measurement value of an individual at a specific time point, and the color distinguishes the two periods before infection (red) and after infection (blue). At the same time, we used the locally weighted regression (LOESS) method to generate trend lines to reveal the changing pattern of eGFR before and after infection. In addition, the figure also includes a reference line (red dotted line) of the overall eGFR average level to facilitate the observation of the degree of deviation of individual eGFR values from the average level. It can be observed from the figure that there is a certain fluctuation in the eGFR values of individuals before and after COVID-19 infection. Before infection, eGFR values are mostly concentrated at a higher level, and the differences between individuals are small. After infection, although the eGFR values of most individuals remain at a high level, there are more fluctuations, and the eGFR values of some individuals have dropped significantly. The LOESS trend line shows that the average level of eGFR values after infection decreased slightly, but this decrease was not significant. This may indicate that there are individual differences in the effect of COVID-19 infection on eGFR, and not all individuals will experience a significant decrease in eGFR.
Figure 2.
eGFR time series analysis of Infection group
Discussion and Conclusions
This study aims to evaluate the difference in eGFR between COVID-19-infected patients and non-infected patients who were taking lithium salts for a long time in N3C patients between March 1, 2021, and September 30, 2022. The results showed that there was no significant difference in eGFR between the two groups of patients. In the infected group, there was also no statistically significant difference in eGFR before and after infection. This means that COVID-19 infection does not seem to impose an additional burden on the kidney function of patients taking lithium salts for a long time. Our study also noted that men infected with SARS-CoV-2 had a higher eGFR than women. In the comparison of different age groups, the 18-25- and 25-40-years old groups seemed to be more affected by SARS-CoV-2 compared with the 40-60- and 60-80-years old groups. In addition, medical comorbidities had no additional effect on this result.
However, our study also raises quality considerations in the drug_exposure table in N3C. As Figure 1 showed, after selecting a specific observation window, the cohort number decreased dramatically. We then further investigated this phenomenon. We identified a total of 1,958,027 lithium exposure records for BD patients. Every record contains a drug_exposure_start_date, with no missing values in that column. However, 831,974 records (42.5%) have a “NULL” value in drug_exposure_end_date, and 756,574 records (38.6%) have the same drug_exposure_start_date and drug_exposure_end_date. Which means among the 1,126,153 records that do include an end date, 756,574 records (67.2%) have the same date for both drug_exposure_start_date and drug_exposure_end_date. However, lithium is a long-term drug for BD patients, this raises questions about the accuracy of the end date field, as single-day or one-time use is clinically unlikely.
After discussing with N3C’s data experts and literature reviews, we learned that their explanation for “null” values is that they do not know the specific information, which is usually expressed as unknown [21][22]. This ambiguous interpretation complicates our investigation of patients taking lithium continuously. Because the potential explanations of “null” are also complex and diverse, (e.g. it may be that the doctor or patient did not report it in the EHR in a timely manner, it may be an error in the standardization process, or even a problem in data transmission). Therefore, a “null” value cannot assume that it represents the same clinical situation. N3C has a pragmatic approach to data harmonization and centralized data management and the resulting repository provides a tremendous resource for observational research [22] . However, there may be opportunities to build upon and leverage the centralized data management approach to improve data quality.
Despite this, the N3C dataset remains a valuable resource for answering COVID-19 related questions. For example, a large-scale cohort study used N3C data to show that COVID-19 vaccination can significantly reduce the risk of acute kidney injury compared with COVID-19 infection [23]. In addition, N3C data can also be used to generate valuable insights in specific research areas, including dexamethasone and risk factor of in-hospital mortality in COVID-19 patients [24]; risk of severe COVID-19 in different ethnicity living with HIV [25]; risk factors associated with acute sequelae of SARS-CoV-2 [26].
Our results are consistent with other literature, indicating that patients taking lithium salts for a long time may not suffer additional damage to their kidney function when infected with SARS-CoV-2 [27]. In addition, some articles have studied the relationship between lithium and COVID-19 and found that therapeutic lithium levels are associated with a lower risk of COVID-19 [28]. These findings further demonstrate the possible antiviral effect of lithium. It is worth noting that some studies have even suggested that lithium can be used as a drug to treat COVID-19 [29]. In future studies, this research could be improved with a larger sample size and more complete data, for example by incorporating additional large-scale databases such as All of Us [30] or OHDSI. We plan to conduct a sensitivity analysis comparing COVID-19 patients with those having other respiratory diseases to comprehensively assess COVID-19’s impact on kidney function and the potential role of lithium in treatment.
Acknowledgements and Ethics approval
The N3C data is managed by the NIH. Data is transferred from N3C to the National Center for Advancing Translational Sciences (NCATS) under the Johns Hopkins University Trust Agreement (IRB00249128). The use of data in this study was approved by the N3C (DUR-22361BD) and reviewed by the University of Michigan Medical School Institutional Review Board (IRB HUM00192962).
Figures & Tables
References
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