Bipolar Spectrum Disorder May Be Associated With Family History of Diseases
Bibliographic record
Abstract
BACKGROUND: This study aims at investigating into the presence of family history of diabetes, ischemic heart disease, thyroid disease, cancer, cerebrovascular disease, and epilepsy in bipolar patients. METHODS: Totally 1,148 patients admitted to our outpatient unit between January 2018 and January 2020, who were diagnosed with bipolar disorder according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), from whom informed consent was obtained, were cross-sectionally and consecutively evaluated. Each patient was questioned regarding a family history of diabetes, ischemic heart disease, thyroid disease, cancer (gastrointestinal, breast and prostate cancer, leukemia, and lymphoma), cerebrovascular disease and epilepsy in first- and second-degree relatives. RESULTS: Diabetes, ischemic heart disease, cancer, cerebrovascular disease and epilepsy were more common in the family histories than in bipolar patients. A strong correlation was found between family history positive for epilepsy and bipolar disorder with psychotic symptoms. Also, a correlation was found between family history for diabetes and seasonal course and family history positive for thyroid disease and comorbid anxiety disorder. CONCLUSIONS: This study is the first to investigate into the frequency of physical diseases in the family histories of bipolar patients. Current therapies target the association between common leading pathways and symptoms whereas it is the association between stress and neural circuits that underlie the pathophysiology that should be targeted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".