Association between polychlorinated biphenyl 153 exposure and serum testosterone levels: analysis of the National Health and Nutrition Examination Survey
Bibliographic record
Abstract
BACKGROUND: To examine the effects of environmental polychlorinated biphenyls (PCBs) 153 exposure, an industrial plasticizer, on serum testosterone levels. METHODS: Using data collected from the 1999-2000 and 2001-2002 National Health and Nutrition Examination Survey (NHANES), we analyzed serum total testosterone and PCB153 levels, demographic data and comorbidities for men aged 18 years and older. Univariate and multivariate linear regression analysis was used to evaluate the association between total testosterone and serum PCB153. RESULTS: Five hundred and fifty-seven men met inclusion criteria. Median age was 45.7 [33.4-60.4] years old while median serum total testosterone and PCB153 levels were 479 [352.5-607] ng/dL and 0.20 [0.11-0.39] ng/g, respectively. Increasing age, higher body mass index (BMI), higher levels of serum PCB153, lower levels of activity and a history of diabetes mellitus and coronary heart disease were associated with decreasing serum testosterone levels on univariate linear regression. On multivariate linear regression, increasing age (estimate -6.29 ng/dL per year of life, P<0.001) and BMI (estimate -7.08 ng/dL per unit BMI, P<0.001) were associated with declining serum testosterone levels. While serum PCB153 levels were found to be negatively correlated with serum testosterone levels on univariate analysis (estimate -179.67, P<0.001), this association was not significant on the multivariable model (estimate -12.83, P=0.673). CONCLUSIONS: In this population-based analysis, we report an association between a decrease in serum testosterone with increasing serum levels of PCB153. Identifying environmental factors for etiology of low testosterone and mechanisms for causation will be important to aid in strategies to increase testosterone naturally in men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".