Risk factors for low muscle mass, malnutrition, and (probable-) sarcopenia in adults with or without a history of cancer in the UK Biobank
Bibliographic record
Abstract
BACKGROUND AND AIMS: Early identification of people at risk of cancer-related malnutrition, low muscle mass (LMM) and sarcopenia is crucial to mitigate the impact of adverse outcomes. This study investigated risk factors associated with LMM, malnutrition and (probable-) sarcopenia and whether these varied in people with or without a history of cancer. METHODS: Participants in the UK Biobank, with or without a history of cancer, who completed the Oxford WebQ at the baseline assessment were included. LMM was estimated from fat-free mass derived from bioelectrical impedance analysis, and low muscle strength from handgrip strength, and used to identify probable or confirmed sarcopenia following the European Working Group on Sarcopenia in Older People 2 definition. The Global Leadership Initiative on Malnutrition criteria were applied to determine malnutrition. Generalised linear models were used to estimate prevalence ratios (PR) for associations between risk factors (clinical, functional, nutritional) and study outcomes. RESULTS: Overall, 50,592 adults with (n = 2,287, mean ± SD 59.7 ± 7.1 years) or without (n = 48,305, mean ± SD 55.8 ± 8.2 years) cancer were included. For all participants (PRs [cancer, without cancer]), slow walking pace (PR 1.85; 1.99), multimorbidity (PR 1.72; 1.51), inflammation (PR 2.91; 2.07), and low serum 25(OH)D (PR 1.85, 1.44) were associated with higher prevalence of LMM, while higher energy intake (PR 0.55; 0.49) was associated with lower prevalence. Slow walking pace (PR 1.54 [cancer], 1.51 [without cancer]) and higher protein intake (PR 0.18 [cancer]; 0.11 [without cancer]) were associated with increased or decreased prevalence of malnutrition, respectively regardless of cancer status. Multimorbidity was the only common factor associated with higher prevalence (PR 1.79 [cancer], 1.68 [without cancer]) of (probable-)sarcopenia in all participants. CONCLUSION: Risk factors for LMM and malnutrition were similar in adults with and without cancer, although these varied between LMM and malnutrition. These findings have implications for the future of risk stratification, screening and assessment for these conditions and the development or modification of existing screening tools.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".