Bibliographic record
Abstract
Introduction: Lower urinary tract symptoms (LUTS), erectile dysfunction (ED), and hypogonadism are frequently encountered in aging males.Our objective was to determine if the presence of metabolic risk factors can be associated with LUTS or total testosterone (TT) levels.Methods: A total of 1087 adult males (≥45 years) who participated in an annual public event (2007-2018) were recruited.Participants completed the IPSS, IIEF-5, ADAM, and OAB questionnaires.Systolic/diastolic blood pressure (sBP/dBP), neck/ waist circumference, hip length, weight, and height were taken.Total cholesterol, triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), PSA, and TT were collected.Participants were categorized as per TT (low <230 ng/dL, intermediate 230-346 ng/dL, and normal ≥346 ng/dL) and IPSS (mild [0-7], moderate [8-19], and severe [20-35]).A metabolic risk score was created: 1 point for each criterion (BMI ≥30, TG >1.7 mmol/L, sBP ≥130, dBP ≥85, HDL <1.03 mmol/L).ANOVA regressions were performed for the metabolic score assessment (Prism 9).Results: Median age was 59 years old.TT was ≥346 ng/dL in 36%, 39%, and 39% in patients with mild, moderate, and severe LUTS, respectively.A total of 31% of participants had BMI ≥30, 42% had high TG, 23% had low HDL levels, 76% had high sBP, and 45% had elevated dBP.There was no association between metabolic risk score and IPSS (p=0.3995) or IIEF-5 scores (p=0.7367).TT category and metabolic score were associated (ANOVA, p=0.0001).A metabolic score of ≥2/5 was associated with TT <230 ng/dL.A score of ≥3/5 was associated with PSA of ≥2 ng/mL, while a score of ≤2/5 was associated with PSA <1 ng/mL.Conclusions: This cross-sectional analysis indicated an association of various metabolic derangements (BMI ≥30, TG >1.7 mmol/L, sBP ≥130, dBP ≥85, HDL <1.03 mmol/L) with serum TT and PSA levels, but not with LUTS.Further investigations are required to evaluate the potential health risks of hypogonadism in these individuals and if early diagnosis and interventions on the hypogonadal state can benefit them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".