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Lithium for Bipolar Disorder and Risk of Thyroid Dysfunction and Chronic Kidney Disease

2025· article· en· W4407369449 on OpenAlexaff
Joe Kwun Nam Chan, Marco Solmi, Christoph U. Correll, Corine Sau Man Wong, Heidi Ka Ying Lo, Francisco Tsz Tsun Lai, Wing Chung Chang

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineKidney diseaseBipolar disorderQuetiapineLithium (medication)Internal medicineHazard ratioPopulationPsychiatrySchizophrenia (object-oriented programming)Confidence interval

Abstract

fetched live from OpenAlex

Importance: Literature indicates adverse effects of lithium on thyroid and kidney function. However, existing data are heterogeneous, with limitations in quality and lack evaluation of adverse effects of lithium vs other mood stabilizers, especially commonly prescribed second-generation antipsychotics. Lithium serum level thresholds associated with thyroid and kidney abnormalities remain unknown. Objective: To examine risk of thyroid and kidney dysfunction in patients with incident bipolar disorder (BD) treated with lithium and other mood stabilizers and antipsychotics in an Asian population and to determine lithium serum level cutoffs associated with these physical complications. Design, Setting, and Participants: This population-based retrospective cohort study identified patients aged 15 years or older with first-diagnosed BD in Hong Kong from 2002 to 2018, utilizing a medical record database of public health care services. Data analysis was performed from February to May 2024. Exposures: Lithium vs nonlithium treatment. The nonlithium group was further stratified into valproate, olanzapine, quetiapine, and risperidone groups. Main Outcomes and Measures: Main outcomes were hypothyroidism, hyperthyroidism, and chronic kidney disease stage 3 or higher (CKD3+), with additional investigation on CKD stage 4 or higher (CKD4+) and end-stage kidney disease (ESKD). Outcomes were ascertained using laboratory test results. Cox proportional hazards regression analyses were performed for risk estimation with adjusted hazard ratios (aHRs) and 95% CIs. Receiver operating characteristic analyses with the Youden index were employed to determine lithium serum level cutoffs associated with thyroid and kidney dysfunction. Results: There were 4752 individuals with analyzable data for hypothyroidism (mean [SD] age, 39.5 [15.6] years; mean [SD] follow-up, 8.4 [4.8] years; 2889 female [60.8%]), 4500 with data for hyperthyroidism (mean [SD] age, 39.7 [15.6] years; mean [SD] follow-up, 8.7 [4.7] years; 2716 female [60.4%]), and 7029 with data for CKD (mean [SD] age, 37.9 [14.8] years; mean [SD] follow-up, 8.3 [4.8] years; 4251 female [60.5%]). Lithium was associated with increased risk of hypothyroidism (aHR, 2.00; 95% CI, 1.72-2.33) and CKD3+ (aHR, 1.35; 95% CI, 1.15-1.60), but not CKD4+ or ESKD, compared with nonlithium treatments. Higher lithium serum levels were associated with elevated rates of hypothyroidism (aHR, 2.08; 95% CI, 1.67-2.59), hyperthyroidism (aHR, 1.81; 95% CI, 1.31-2.50), and CKD3+ (aHR, 2.11; 95% CI, 1.57-2.85). Greater number of lithium toxicity episodes was associated with increased CKD3+ risk. Valproate, olanzapine, quetiapine, and risperidone generally exhibited reduced likelihood of thyroid dysfunction and CKD3+ compared with lithium, without any difference in advanced CKD. Mean lithium serum levels greater than 0.5028 mEq/L, greater than 0.5034 mEq/L, and greater than 0.5865 mEq/L represented thresholds associated with hypothyroidism, hyperthyroidism, and CKD3+, respectively. Conclusions and Relevance: In this cohort study of patients with incident BD, lithium was associated with a mildly increased risk of thyroid dysfunction and CKD in a predominantly Chinese population. The identified lithium level thresholds associated with risks of physical complications may facilitate the development of evidence-based guidelines recommending lithium treatment, particularly in Asian populations, and the promotion of personalized care and risk-benefit balancing in the treatment for BD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.262
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2025
Admission routes1
Has abstractyes

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