Bibliographic record
Abstract
The “Low T” marketing campaign has driven a dramatic increase in testosterone prescription in the last few years, particularly in North America. However, sales of testosterone are now falling again, as regulators, particularly the Food and Drug Administration in the US and Health Canada, have drawn attention to the potential cardiovascular risk of testosterone. Currently the role of androgens in cardiovascular disease (CVD) and other non-communicable chronic diseases is controversial and unclear. Observationally lower testosterone is associated with a wide range of conditions including CVD, cognitive problems and depression. However, observational studies are open to residual confounding and reverse causality. Moreover, different androgen biomarkers tend to have different relations with these outcomes. This thesis examined the association of androstanediol glucuronide (3α-diol-G), a correlate of the breakdown product of all androgens, with electrocardiogram (ECG) parameters in a population-representative sample of US men. To minimize reverse causality, this thesis also used a separate-sample instrumental variable (SSIV) estimator to examine the effect of testosterone on several outcomes, including well-established CVD risk factors, Framingham score, ECG parameters, inflammatory markers and cognitive function in Chinese men. The associations of 3α-diol-G and serum testosterone tertiles, with ECG parameters were examined in a nationally representative study of 773 US men aged 40+ years from National Health and Nutrition Examination Survey III phase 1 (1988-1991) using linear regression. For the SSIV analysis, a genetic rule predicting testosterone was developed in 289 young Chinese men from Hong Kong based on three selected testosterone-related single nuclear polymorphisms (rs10046, rs1008805 and rs1256031). Multivariable censored and linear regression models were used to examine the association of genetically predicted testosterone with the outcomesin4212 older Chinese men from the Guangzhou Biobank Cohort Study. 3α-diol-G and serum testosterone were poorly correlated (0.11) in US men. 3α-diol-G was unrelated to corrected QT interval (QTb)or heart rate but higher testosterone was associated with shorter QT interval and lower heart rate in US men. Genetically predicted testosterone was unrelated to most of the outcomes examined in Chinese men, including blood pressure, fasting glucose, Framingham score, QT interval, corrected QT interval using the Framingham formula (QTf), heart rate, white blood cell, granulocyte, lymphocyte, high sensitivity C-reactive protein, delayed 10-word recall score and Mini-Mental State Examination (MMSE)score, but was associated with higher LDL-cholesterol(0.02 mmol/L, 95% CI 0.01 to 0.04), lower HDL-cholesterol (-0.01 mmol/L, 95% CI -0.02 to -0.001) and longer corrected QT interval using the Bazett formula (QTb) interval(0.66 milliseconds, 95% CI 0.02 to 1.31). The null associations of 3α-diol-G with markers of cardiac function further suggest observations about endogenous serum testosterone may not indicate the cardiovascular effects of testosterone. Findings from SSIV do not corroborate observed protective associations of testosterone or correspondingly potentially protective effects of testosterone on a range of indicators of chronic diseases, but raise the possibility that higher testosterone might adversely affect lipid profile and cardiac function, with corresponding implications for cardiovascular events. Replication for cardiovascular events in a larger sample is required.
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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 | 0.003 |
| 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 | 0.001 | 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".