Inadequacy of Body Weight-Based Recommendations for Individual Protein Intake—Lessons from Body Composition Analysis
Bibliographic record
Abstract
Current body weight-based protein recommendations are ignoring the large variability in body composition, particularly lean mass (LM), which drives protein requirements. We explored and highlighted the inter-individual variability of weight versus body composition-adjusted protein intakes by secondary analysis in three cohorts of (1) 574 healthy adults (mean ± SD age: 41.4 ± 15.2 years); (2) 403 cirrhotic patients (age: 44.7 ± 12.3 years) and (3) 547 patients with lung cancer (age: 61.3 ± 8.2 years). LM was assessed using different devices (magnetic resonance imaging, dual-energy X-ray absorptiometry, computer tomography, total body potassium and bioelectrical impedance), body weight-based protein intake, its ratio (per kg LM) and mean protein requirement were calculated. Variability in protein intake in all cohorts ranged from 0.83 to 1.77 g protein per kg LM per day using (theoretical protein intake of 60 g protein per day). Calculated mean protein requirement was 1.63 g protein per kg LM per day; consequently, 95.3% of healthy subjects, 100% of cirrhotic and 97.4% of cancer patients would present with a low protein intake per kg LM. Weight-adjusted recommendations are inadequate to address the LM specific differences in protein needs of healthy subjects or clinical populations. Absolute protein intake seems to be more relevant compared to the relative proportion of protein, which in turn changes with different energy needs.
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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 | 0.054 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".