Adults with Mood Disorders Have an Increased Risk Profile for Cardiovascular Disease within the First 2 Years of Treatment
Bibliographic record
Abstract
OBJECTIVES: People with bipolar disorder (BD) and major depressive disorder (MDD) are at risk for premature death from various physical illnesses. A large component of this risk may be accounted for by an elevated risk of metabolic syndrome (MeS) and coronary heart disease (CHD). The objective of our study was to examine patients' physical health prior to first treatment and over 2 years of follow-up. METHODS: Ten-year risk for CHD and incidence of MeS were calculated for newly diagnosed patients with MDD (n = 30) and BD (n = 24) at baseline and over a 2-year follow-up. Age and sex-matched control subjects were obtained from the National Health and Nutrition Examination Survey III dataset. RESULTS: At baseline, 11.2% of patients met diagnostic criteria for MeS and this increased to 16.8% at follow-up. Women had higher rates of MeS but rates were similar across diagnosis. There was a significant increase within all MeS criteria. The 10-year CHD risk was low for patients at baseline and follow-up but increased across the follow-up period. Changes in CHD and MeS risk were not associated with a specific type of pharmacotherapy, as all medication classes appeared to increase risk. CONCLUSION: Prior to treatment, MeS and CHD risk rates for patients were similar to the general population, but their risk of CHD increased appreciably.
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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 | 0.000 | 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) | 0.003 | 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".