Body Image, Body Composition, and Aging in Women: Insights into an Older Adult Problem
Bibliographic record
Abstract
Body image is understudied in aging women. The present perspective article reviews body image, body composition, and aging in women. Sarcopenia, a condition of low muscle mass, is associated with aging, and sarcopenia in combination with excessive body fat causes sarcopenic obesity. Findings of improved health in people with a higher body mass index, known as the obesity paradox, are due to misclassifying healthy people as overweight according to height and body weight instead of according to fat mass and fat-free mass. Body fat infiltrates internal organs in aging adults as increasing levels of body fat are redistributed into the trunk, especially in the abdomen, while subcutaneous fat in the appendages decreases. Accuracy of body image perceptions can determine an individual’s control of body weight. Aging women can protect against sarcopenic obesity by increasing fat-free mass with resistance training and by lowering body fat levels with weight management knowledge and skills. Healthy dietary patterns are low in ultra-processed foods that stimulate excessive consumption of calories and increase body fat levels. In combination with the phosphate additives in ultra-processed food which increase sarcopenia and aging, the present article proposes an etiological pathway in which ultra-processed food consumption eventually leads to aging-related sarcopenic obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".