Androgen Therapy in Male Patients Suffering from Type 2 Diabetes: A Review of Benefits and Risks
Bibliographic record
Abstract
BACKGROUND: The current estimated numbers of patients with Type 2 Diabetes (T2D) is believed to be close to 10% of the whole populations of many geographical regions, causing serious concerns over the resulting elevated morbidity and mortality as well as the impact on health care systems around the world. In addition to negatively affecting the quality of life, diabetes is associated with cardiovascular and cerebrovascular complications, indicating that appropriate drug therapy should not only deal with metabolic dysfunction but also protect the vascular system, kidney function and skeletal muscle mass from the effects of the epigenetic changes induced by hyperglycaemia. OBJECTIVE: To provide an insight into the management of hypogonadism associated with T2D, this review focuses on clinical observations related to androgen therapy in qualified diabetic patients, and discusses the lines of evidence for its benefits and risks. The potential interactions of testosterone with medicines used by patients with T2D will also be discussed. CONCLUSION: From recent clinical findings, it became evident that a considerable percentage of patients suffering from T2D manifested low serum testosterone and experienced diminished sexual activity, as well as reduced skeletal muscle mass and lower bone density. Although there are some controversies, Testosterone Replacement Therapy (TRT) for this particular population of patients appears to be beneficial overall only if it is implemented carefully and monitored regularly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".