Hyperthyroidism and clinical depression: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Hyperthyroidism and clinical depression are common, and there is preliminary evidence of substantial comorbidity. The extent of the association in the general population, however, has not yet been estimated meta-analytically. Therefore we conducted this systematic review and meta-analysis (registered in PROSPERO: CRD42020164791). Until May 2020, Medline (via PubMed), PsycINFO, and Embase databases were systematically searched for studies on the association of hyperthyroidism and clinical depression, without language or date restrictions. Two reviewers independently selected epidemiological studies providing laboratory or ICD-based diagnoses of hyperthyroidism and diagnoses of depression according to operationalized criteria (e.g. DSM) or to cut-offs in established rating scales. All data, including study quality based on the Newcastle-Ottawa Scale, were independently extracted by two authors. Odds ratios for the association of clinical depression and hyperthyroidism were calculated in a DerSimonian-Laird random-effects meta-analysis. Out of 3372 papers screened we selected 15 studies on 239 608 subjects, with 61% women and a mean age of 50. Relative to euthyroid individuals, patients with hyperthyroidism had a higher chance of being diagnosed with clinical depression: OR 1.67 ([95% CI: 1.49; 1.87], I 2 : 6%; prediction interval: 1.40 to 1.99), a result supported in a number of sensitivity and subgroup analyses. The OR was slightly less pronounced for subclinical as opposed to overt hyperthyroidism (1.36 [1.06; 1.74] vs. 1.70 [1.49; 1.93]). This comorbidity calls for clinical awareness and its reasons need investigation and may include neurobiological mechanisms, common genetic vulnerability and a generally heightened risk for clinical depression in patients with chronic somatic disorders.
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 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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".