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Record W4223927794 · doi:10.1016/j.jsxm.2022.03.611

Testosterone Therapy is Associated with Depression, Suicidality, and Intentional Self-Harm: Analysis of a National Federated Database

2022· article· en· W4223927794 on OpenAlexaff
Sirpi Nackeeran, Mehul S. Patel, Devi T. Nallakumar, Jesse Ory, Taylor P. Kohn, Christopher M. Deibert, Chase Carto, Ranjith Ramasamy

Bibliographic record

VenueThe Journal of Sexual Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTestosterone (patch)MedicineMajor depressive disorderPopulationPsychiatryDepression (economics)Suicide attemptPoison controlMoodSuicide preventionInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Long-term use of testosterone can be associated with mood destabilizing effects. Most studies investigating psychiatric complications of anabolic steroids have used small samples, but a comprehensive assessment of the risk of developing mental health disorders after testosterone use has not been performed at the population level. AIM: To determine whether testosterone therapy is associated with major depressive disorder or suicide attempts in men. METHODS: We conducted a retrospective cohort study of 70.3 million electronic health records collected from 46 healthcare organizations encompassing flagship hospitals, satellite hospitals, and outpatient clinics since 2008 to determine whether testosterone use is associated with major depressive disorder and suicide attempts in a large population. We included men 18 or older who either used testosterone or did not, defined by reported use, insurance claim, or prescription use of testosterone documented in the electronic health record. We propensity-score matched by age, race, ethnicity, obesity, and alcohol-related disorder. Additionally, a sub-group analysis was performed in testosterone deficient (<300 ng/dL) men comparing those with TD on testosterone therapy to a control group of men with TD who are not using testosterone. OUTCOMES: We determined measures of association with a new diagnosis of major depressive disorder and suicide attempt or intentional self-harm following testosterone use within 5 years. RESULTS: A total of 263,579 men who used testosterone and 17,838,316 men who did not were included in the analysis. Testosterone use was independently associated with both Major Depressive Disorder (OR 1.99, 95% CI 1.94-2.04, P < .0001) and Suicide Attempt/Intentional Self-Harm (OR 1.52, 95% CI 1.40-1.65, P < .0001). Results remained significant in testosterone deficient sub-group analysis. CLINICAL IMPLICATIONS: Men who use testosterone should be screened for and counseled about risks of depression and suicidality. STRENGTHS AND LIMITATIONS: Strengths of this study include a large sample size, the ability to account for chronology of diagnoses, the use of propensity score matching to control for potentially confounding variables, and the consistency of results with sub-group analyses. Limitations include the potential for incorrect coding within the electronic health record, a lack of granular information regarding testosterone therapy adherence, the possibility that unrecorded testosterone or anabolic steroid use were prevalent but not captured within the control group, and a lack of data regarding testosterone withdrawal. CONCLUSION: Testosterone use is independently associated with new-onset mental health disorders. Future studies are necessary to elucidate the role that androgen withdrawal plays and whether a causal relationship exists. Nackeeran S, Patel MS, Nallakumar DT, et al. Testosterone Therapy is Associated With Depression, Suicidality, and Intentional Self-Harm: Analysis of a National Federated Database. J Sex Med 2022;19:933-939.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.331
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations18
Published2022
Admission routes1
Has abstractyes

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