Erectile Dysfunction and Undiagnosed Diabetes, Hypertension, and Hypercholesterolemia
Bibliographic record
Abstract
PURPOSE: We investigated whether erectile dysfunction, a marker for future cardiovascular disease, is associated with undiagnosed cardiometabolic risk factors among US men. Identifying the presence of these risk factors could lead to earlier initiation of treatment for primary prevention of cardiovascular disease. METHODS: We analyzed cross-sectional data from men aged 20 years and older who participated in the National Health and Nutrition Examination Survey during 2001-2004. Erectile dysfunction was determined by a single, validated survey question. We used logistic regression analyses to investigate the relationship between erectile dysfunction and undiagnosed hypertension, hypercholesterolemia, and diabetes. RESULTS: After multivariate adjustment, men with erectile dysfunction had more than double the odds of having undiagnosed diabetes (odds ratio = 2.20; 95% CI, 1.10-4.37), whereas no association was seen for undiagnosed hypertension or undiagnosed hypercholesterolemia. For the average man aged 40 to 59 years, the predicted probability of having undiagnosed diabetes increased from 1 in 50 in the absence of erectile dysfunction to 1 in 10 in the presence of erectile dysfunction. CONCLUSIONS: Our results underscore the importance of erectile dysfunction as a marker of undiagnosed diabetes. Erectile dysfunction should be a trigger to initiate diabetes screening, particularly among middle-aged men.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".