Anti-Depression Medication Taking and Risk of Metabolic Syndrome among US Citizens Aged 60+ years: an Across-sectional Analysis of the NHANES 2007-2008
Bibliographic record
Abstract
Abstract Objective: To examine whether having metabolic syndrome (MS) among seniors is associated with using anti-depression medication. Methods: A total of 1366 (617 men and 749 women) individuals aged 60+ years from the NHANES 2007/08 survey who had no reported heart disease and/or cancers but had information on prescribed medications in previous month were included in this analysis. All subjects were categorized into three prescribed drug use status, ie, none (group 1); no anti-depressants (group 2); and with anti-depressants (group 3). MS was defined with the criteria of the ATP III. Results: Over 80% of individuals reported taking prescribed medications with 6% of men and 16% of women respectively having used anti-depressants. About 36% of men and 40% of women respectively were considered to have MS. Results from multiple logistic regression analyses indicated that in comparing to group 1, the odds ratios (95% CI) of MS was 2.73 (1.96, 3.82) for group2 and 2.25 (1.07, 4.69) for group 3, respectively. Both group 2 and 3 had a similar metabolic risk profile, in comparing to group 1, they had higher odds of having diabetes and high level of blood pressures. Conclusion: Seniors with medications are more likely to be with MS, diabetes, and high level blood pressures. However, the observed the cardio-metabolic risk association seems similar between seniors using anti-depressant drugs and using other prescribed medications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".