Does hair cortisol mediate the effects of sexual and gender minority stress on depression and anxiety? A structural equation modelling approach
Bibliographic record
Abstract
BACKGROUND: Sexually and gender-diverse people are at an increased risk of experiencing depression and anxiety, which has been attributed to minority stress. The aim of this study was to investigate, for the first time, whether sexually and gender-diverse people exhibit altered hair cortisol concentrations, and whether this mediates the effects of minority stress on depression and anxiety. METHODS: N = 328 sexually and gender-diverse people and n = 286 age- and BMI-matched cis-gender heterosexuals from the general Swiss population completed an online survey and collected a hair sample. Depression and anxiety were measured via the Patient Health Questionnaire (PHQ). Minority stress was assessed with a questionnaire covering discrimination, expected rejection, concealment, and internalised stigma. One centimetre of hair was analysed to determine past-month cortisol, using the gold standard liquid chromatography with tandem mass spectrometry. RESULTS: Sexually and gender-diverse people had significantly higher levels of depression than cis-gender heterosexual people. Moreover, gender-diverse people had higher levels of anxiety and lower hair cortisol in comparison to sexually diverse and cis-heterosexual people. Among sexually and gender-diverse people, minority stress was positively associated with depression and anxiety. Moreover, internalised stigma was positively associated with hair cortisol. LIMITATIONS: Depression and anxiety were measured via a self-reported instrument (PHQ). CONCLUSIONS: This study is the first to demonstrate that gender-diverse people experience increased levels of depression and anxiety while also exhibiting chronically lowered levels of cortisol, a profile seen in trauma-, fatigue, and pain-related conditions. These findings elucidate a mechanism potentially underlying some of the health disparities in this marginalised group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".