Are Lean Body Mass and Fat-Free Mass the Same or Different Body Components? A Critical Perspective
Bibliographic record
Abstract
The 2-component molecular-level model dividing body mass into fat and fat-free mass (FFM) is a cornerstone of contemporary body composition research across multiple disciplines. Confusion prevails, however, as the term lean body mass (LBM) is frequently used interchangeably with FFM in scientific discourse. Are LBM and FFM the same or different body components? Captain Albert R. Behnke originated the LBM concept in 1942 and he argued that his "physiological" LBM component included "essential" fat or structural lipids whereas FFM is a chemical entity "free" of fat. Classical experimental animal and human studies conducted during Behnke's era laid the foundation for the widely used body density and total body water 2-component molecular-level body composition models. Refined body composition models, organization of lipids into structural and functional groupings, and lipid extraction methods all have advanced since Behnke's era. Our review provides an in-depth analysis of these developments with the aim of clarifying distinctions between the chemical composition of LBM and FFM. Our retrospective analysis reveals that FFM, derived experimentally as the difference between body weight and extracted neutral or nonpolar lipids (mainly triglycerides), includes polar or structural lipids (that is, Behnke's "essential" fat). Accordingly, LBM as originally proposed by Behnke has the same chemical composition as FFM, thus answering a longstanding ambiguity in the body composition literature. Bringing body composition science into the modern era mandates the use of the chemically correct term FFM with the elimination of the duplicative term LBM that today has value primarily in a historical context. Avoiding the use of the term LBM additionally limits confusion surrounding similar widely used body composition terms such as lean mass, lean soft tissue mass, and lean muscle mass.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.014 |
| 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".