Multimorbidity and the risk of malnutrition, frailty and sarcopenia in adults with cancer in the UK Biobank
Bibliographic record
Abstract
BACKGROUND: Malnutrition, sarcopenia and frailty are distinct, albeit interrelated, conditions associated with adverse outcomes in adults with cancer, but whether they relate to multimorbidity, which affects up to 90% of people with cancer, is unknown. This study investigated the relationship between multimorbidity with malnutrition, sarcopenia and frailty in adults with cancer from the UK Biobank. METHODS: This was a cross-sectional study including 4122 adults with cancer (mean [SD] age 59.8 [7.1] years, 50.7% female). Malnutrition was determined using the Global Leadership Initiative on Malnutrition criteria. Probable sarcopenia and sarcopenia were defined using the European Working Group on Sarcopenia in Older People 2 criteria. (Pre-)frailty was determined using the Fried frailty criteria. Multimorbidity was defined as ≥2 long-term conditions with and without the cancer diagnosis included. Logistic regression models were fitted to estimate the odds ratios (ORs) of malnutrition, sarcopenia and frailty according to the presence of multimorbidity. RESULTS: Genitourinary (28.9%) and breast (26.1%) cancers were the most common cancer diagnoses. The prevalence of malnutrition, (probable-)sarcopenia and (pre-)frailty was 11.1%, 6.9% and 51.2%, respectively. Of the 11.1% of participants with malnutrition, the majority (9%) also had (pre-)frailty, and 1.1% also had (probable-)sarcopenia. Of the 51.2% of participants with (pre-)frailty, 6.8% also had (probable-)sarcopenia. No participants had (probable-)sarcopenia alone, and 1.1% had malnutrition, (probable-)sarcopenia plus (pre-)frailty. In total, 33% and 65% of participants had multimorbidity, including and excluding the cancer diagnosis, respectively. The most common long-term conditions, excluding the cancer diagnosis, were hypertension (32.5%), painful conditions such as osteoarthritis or sciatica (17.6%) and asthma (10.4%). Overall, 80% of malnourished, 74% of (probable-)sarcopenia and 71.5% of (pre-)frail participants had multimorbidity. Participants with multimorbidity, including the cancer diagnosis, had higher odds of malnutrition (OR 1.72 [95% confidence interval, CI, 1.31-2.30; P < 0.0005]) and (pre-)frailty (OR 1.43 [95% CI 1.24-1.68; P < 0.0005]). The odds increased further in people with ≥2 long-term conditions in addition to their cancer diagnosis (malnutrition, OR 2.41 [95% CI 1.85-3.14; P < 0.0005]; (pre-)frailty, OR 2.03 [95% CI 1.73-2.38; P < 0.0005]). There was little evidence of an association of multimorbidity with sarcopenia. CONCLUSIONS: In adults with cancer, multimorbidity was associated with increased odds of having malnutrition and (pre-)frailty but not (probable-)sarcopenia. This highlights that multimorbidity should be considered a risk factor for these conditions and evaluated during nutrition and functional screening and assessment to support risk stratification within clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".