Prevalence of sarcopenia and predictors of body composition among women with early-stage breast cancer.
Bibliographic record
Abstract
160 Background: Body mass index (BMI) does not accurately reflect body composition, particularly among cancer survivors. Sarcopenia (low skeletal muscle mass) and low muscle radio-density (MD; suggesting fat infiltration into muscle, compromising function) increase risk of surgical complications and chemotherapy toxicity and are associated with worse survival in advanced cancer. Little is known about the prevalence or predictors of sarcopenia and low MD in early-stage breast cancer. Methods: We studied 2,914 Kaiser Permanente members diagnosed with Stage I-III breast cancer from 2005-2013. Using computed tomography (CT) scans of the third lumbar vertebra from clinical care, we determined sarcopenia (skeletal muscle index < 41 muscle [cm2]/height [m2]) and low MD ( < 25-Hounsfield Units for non-obese; < 33 for obese) using published cut points. We assessed associations with characteristics including age, race, BMI, age, stage, lifestyle and co-morbidities with logistic regression. We also examined moderate/vigorous physical activity among a subset of 672 women with activity questionnaires. Results: At diagnosis, mean age was 56 years and time to CT was 1 month. Both sarcopenia and low MD were common among early-stage breast cancer survivors (40% and 38% respectively). In multivariate analyses, the odds of sarcopenia and low MD increased with age (per 5 years, Odds Ratio [OR] 95% Confidence Interval [CI] of OR = 1.33; 95%CI: 1.27, 1.39 and OR = 1.41; 95%CI: 1.35, 1.47 respectively). The odds of sarcopenia decreased with greater BMI (OR = 0.80; 95%CI: 0.78, 0.82 per kg/m2), while the odds of low MD increased (OR = 1.03; 95%CI: 1.01, 1.04 per kg/m2). Black race was associated with lower odds of sarcopenia and low MD, while physical activity levels were associated with lower odds of sarcopenia and more favorable MD. Conclusions: Sarcopenia and low MD are highly prevalent among breast cancer survivors. While older age is strongly associated with these conditions, they occur across ages and stages. Differences in body composition by race and age may underlie differences in the association of BMI with cancer outcomes; understanding these may help guide clinical interventions.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".