Characteristics of Secondary, Primary, and Compensated Hypogonadism in Aging Men: Evidence from the European Male Ageing Study
Bibliographic record
Abstract
CONTEXT: The diagnosis of late-onset hypogonadism (LOH) in older men with age-related declines in testosterone (T) is currently not well characterized. OBJECTIVE: Our objective was to investigate whether different forms of hypogonadism can be distinguished among aging men. DESIGN: The study was a cross-sectional survey on 3369 community-dwelling men aged 40-79 yr in eight European centers. METHODS: Four groups of subjects were defined: eugonadal (normal T and normal LH), secondary (low T and low/normal LH), primary (low T and elevated LH), and compensated (normal T and elevated LH) hypogonadism. Relationships between the defined gonadal status with potential risk factors and clinical symptoms were investigated by multilevel regression models. RESULTS: Among the men, 11.8, 2.0, and 9.5% were classified into the secondary, primary, and compensated hypogonadism categories, respectively. Older men were more likely to have primary [relative risk ratio (RRR) = 3.04; P < 0.001] and compensated (RRR = 2.41; P < 0.001) hypogonadism. Body mass index of 30 kg/m(2) or higher was associated with secondary hypogonadism (RRR = 8.74; P < 0.001). Comorbidity was associated with both secondary and primary hypogonadism. Sexual symptoms were more prevalent in secondary and primary hypogonadism, whereas physical symptoms were more likely in compensated hypogonadism. CONCLUSIONS: Symptomatic elderly men considered to have LOH can be differentiated on the basis of endocrine and clinical features and predisposing risk factors. Secondary hypogonadism is associated with obesity and primary hypogonadism predominately with age. Compensated hypogonadism can be considered a distinct clinical state associated with aging. Classification of LOH into different categories by combining LH with T may improve the diagnosis and management of LOH.
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 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".