Myosteatosis in the Context of Skeletal Muscle Function Deficit: An Interdisciplinary Workshop at the National Institute on Aging
Bibliographic record
Abstract
Skeletal muscle fat infiltration (known as myosteatosis) is an ectopic fat depot that increases with aging and is recognized to negatively correlate with muscle mass, strength and mobility, and disrupt metabolism (insulin resistance, diabetes). An interdisciplinary workshop convened by the National Institute on Aging Division of Geriatrics and Clinical Gerontology on September 2018, discussed myosteatosis in the context of Skeletal Muscle Function Deficit (SMFD). Its purpose was to gain a better understanding of the roles of myosteatosis in aging muscles and metabolic disease, particularly its potential determinants and clinical consequences, and ways of properly assessing it. Special attention was given to functional status and standardization of measures of body composition (including the value of D3- Creatine dilution method) and imaging approaches (including ways to better use Dual-Energy X-Ray Absorptiometry [DXA] through the Shape and Appearance Modeling) to assess lean mass, sarcopenia, and myosteatosis. The workshop convened innovative new areas of scientific relevance to light such as the effect of circadian rhythms and clock disruption in skeletal muscle structure, function, metabolism, and potential contribution to increased myosteatosis. A muscle-bone interaction perspective compared mechanisms associated with myosteatosis and bone marrow adiposity. Potential preventive and therapeutic approaches highlighted ongoing work on physical activity, myostatin treatment, and calorie restriction. Myosteatosis’ impact on cancer survivors raised new possibilities to identify its role and to engage in cross-disciplinary collaboration. A wide range of research opportunities and challenges in planning for the most appropriate study design, interpretation of findings, and consequent translation into clinical practice were discussed and are presented here.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".