Evidence for protein leverage on Total Energy Intake, but not Body Mass Index, in a large cohort of older adults
Bibliographic record
Abstract
Abstract BACKGROUND Protein leverage (PL), the phenomenon of food consuming until absolute intake of protein meets a target value, regardless of shortfall or overconsuming for other nutrients in the diet and total energy intake (TEI). Evidence for PL was observed in humans, recently in a cohort of youth with obesity. This study aimed to test for PL and the protein leverage hypothesis (PLH) in a cohort of older adults. METHODS We conducted a retrospective analysis of dietary intake in a cohort of 1699 community-dwelling older adults aged 67-84 years from the NuAge cohort. We computed TEI and the energy contribution (EC) from each macronutrient. The strength of leverage of macronutrients was assessed through power functions ( TEI = µ * EC L ). Body mass index (BMI) was calculated, and mixture models were fitted to predict TEI and BMI from macronutrient ECs. RESULTS The mean TEI was 7,673 kJ and macronutrient ECs were 50.4 %, 33.2 % and 16.4 %, respectively for carbohydrates, fat, and protein. High carbohydrate intake was associated with low fat intake. There was a strong negative association ( L = -0.37; p < 0.001) between the protein EC and TEI. Each percent of energy intake from protein reduced TEI by 77 kJ on average, ceteris paribus . BMI was unassociated with TEI in this cohort, so the PLH could not be tested here. CONCLUSIONS Findings indicate clear evidence for PL on TEI, but not on BMI, likely because TEI and BMI become increasingly uncoupled during aging.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".