Body Composition by DXA in Patients with Klinefelter and Kallmann Syndrome: The Kama Study
Bibliographic record
Abstract
CONTEXT: Klinefelter syndrome and Kallmann syndrome are 2 rare genetic disorders characterized by reduced testosterone (T) levels but differing in their gonadotrophin profiles. To date, no studies have directly compared body composition in these 2 syndromes. OBJECTIVE: This work aimed to assess the prevalence of altered body composition parameters in patients with Klinefelter and Kallmann syndromes and to compare body composition between the 2 groups. Secondary objectives included evaluating associations between body composition, bone mineral density (BMD), and serum follicle-stimulating hormone (FSH) levels. METHODS: This single-center, retrospective observational study included 50 patients, 29 with Klinefelter and 21 with Kallmann syndrome receiving T replacement therapy. Body composition was evaluated using whole-body dual-energy x-ray absorptiometry (DXA), which provided measurements of appendicular lean mass (ALM), total body fat (TBF), visceral adipose tissue (VAT), the ALM-to-height² ratio (appendicular lean mass index, ALMI), and the ALM-to-weight ratio. RESULTS: Radiologic sarcopenic obesity was identified in 7 patients (14%; 6/29 Klinefelter, 1/21 Kallmann), while osteosarcopenic obesity was found in 2 patients (4%), both with Klinefelter syndrome. Patients with Kallmann syndrome had significantly higher ALMI values than those with Klinefelter syndrome (8.37 ± 1.15 vs 7.28 ± 1.20 kg/m²; P < .001). Univariable analysis revealed an inverse association between FSH levels and ALMI (B = -0.026; P = .002), which remained statistically significant after adjustment for confounders (B = -0.030; P = .0022). CONCLUSION: This study demonstrated a significant difference in lean mass between Klinefelter and Kallmann syndromes, supporting a potential role for FSH in modulating muscle mass independently of T levels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".