A 396-node human cell-lineage tree test finds anatomical compartment identity explains about 75% of metabolic-tier variance while lineage depth explains almost none

The work proposes and tests a computable framework linking cell-lineage tree topology to adult organ bioenergetics, with five layers: lineage as a formal tree grammar, information-theoretic structure of the tree, metabolic switches as runtime operators, compilation to organ-level energetic networks, and execution as organism-level bioenergetics. Cell-type atlases already annotate mitochondrial abundance, dominant fuel pathway, and lineage of origin, but no computable model connected lineage-tree topology to adult organ metabolic rate; this work supplies an explicit five-layer framework for that connection. Based on a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities; the framework is motivated by the reported statistical results.

For predicting a cell's metabolic tier, anatomical compartment identity is far more explanatory than developmental time: lineage depth explains essentially none of the variance (r = 0.11, R2 approximately 1.2%), while compartment identity explains roughly 75%. This directly contrasts two candidate explanations, developmental time versus terminal fate identity, and quantifies compartment identity as dominant rather than leaving it qualitative. Regression analysis on the 396-node lineage tree, reporting correlation and R2.

The mean mitochondrial volume fraction of an organ's constituent terminal cell types is highly correlated with that organ's classical literature-derived SMR, with r = 0.90 across five canonical reference-man organ groups. It links cell-level mitochondrial abundance to the classical physiological SMR measure at organ level, providing an empirical basis for cross-scale inference. Between-group correlation across five canonical reference-man organ groups, r = 0.90.

The study introduces the metabolic commitment-horizon model, the hypothesis that a cell's metabolic tier is fixed at a discrete lineage-commitment event and thereafter held constant rather than accumulated continuously with differentiation time; in 5-fold cross-validation (n = 396), a nearest-commitment-ancestor predictor beat a global-mean null (MAE 0.752 vs 0.809, p = 0.015) but was not significantly better than a lineage-depth regression (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175), so the model is reported as descriptively supported but not yet discriminated predictively. It frames metabolic-tier formation as a testable discrete-commitment hypothesis and explicitly reports that its predictive power is not yet distinguished from a lineage-depth regression. 5-fold cross-validation, n = 396; significant versus the null, not significant versus the lineage-depth regression.

The result is aimed at researchers studying human cell lineages and organ energetics, and applies to modeling settings that take a curated lineage tree and literature-derived SMR as inputs; it treats insulin resistance as an acquired perturbation of the clock and closes with eight testable predictions and falsification conditions, offering a starting point for extending the framework on primary single-cell data and for connecting this within-species result to the classical cross-species rate-of-living hypothesis.

The predictive difference between the commitment-horizon model and the lineage-depth regression was not significant (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175), so the mechanistic hypothesis is currently only descriptively supported; the correlation between mitochondrial volume fraction and SMR rests on five reference organ groups, a limited number of groups; moreover this reading scope is incomplete, lacking the main text, figures, and supplementary materials, so the lineage-tree construction details, the definition of metabolic tier, and the specific cross-validation implementation cannot be checked, which are open questions a reader should keep in mind.