Tricyclic and <scp>SSRI</scp> usage influences the association between <scp>BMI</scp> and health risk factors
Bibliographic record
Abstract
To determine if selective-serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs) influence the association between obesity and cardiovascular disease risk, participants from the Third National Health and Nutrition Examination Survey (NHANES III; 1988-1992) and continuous NHANES (1999-2009, n = 18 274) were used. For a given body mass index (BMI), individuals taking SSRIs (n = 219) tended to have significantly better health risk profiles with lower systolic blood pressure (P = 0.002) and higher high-density lipoprotein (P = 0.003) compared with non-users. Conversely, those who used TCAs (n = 116) had significantly worse health risk profiles with higher diastolic blood pressure (P ≤ 0.0001) and triglycerides (P = 0.023) as compared with non-users for a given BMI. Insulin resistance (HOMA-IR) was higher in TCA users and those with larger BMIs, whereby the differences in insulin resistance between TCA users and non-users was greater with higher BMIs (interaction effect: P = 0.013). Furthermore, individuals taking SSRIs were less likely to have cardiovascular disease than non-users (odds ratio, 95% confidence interval = 0.50, 0.33-0.75) for a given BMI, with no differences by TCA use (odds ratio = 0.74, 0.44-1.24). SSRI and TCA use may alter how body weight relates with cardiovascular risk. When prescribing antidepressant medications, it may be necessary to monitor and consider body weight and cardiovascular risk profile of individual patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".