The relationship between dietary protein intake distribution and lean mass loss in free‐living older adults: effect of sex and total protein intake.
Bibliographic record
Abstract
Background Insufficient dietary protein is a plausible contributing factor to the age‐related loss of lean mass. In addition to quantity, an even protein intake distribution across meals has been shown to enhance 24h muscle protein synthesis in young adults. Whether these short‐term results translate into long‐term preservation of lean mass in older adults remains unknown. Objective To investigate the associations between the quantity and distribution of daily protein intake and lean mass (LM) and appendicular LM (aLM) at baseline (T1) and as a 2 y‐change (T3) in community‐dwelling older adults. Methods A secondary data analysis of the Quebec longitudinal study on nutrition as a determinant of successful aging (NuAge, n=1793, aged 67–84 y at T1) was performed among 351 men and 361 women with available body composition data measured by DXA at T1 and T3. Food intake was assessed from 3 non‐consecutive 24h food recalls at T1. Protein distribution across meals was calculated as the coefficient of variation (CV) of g protein ingested/meal, with lower values reflecting evenness of protein intake. Associations were examined using multivariate regression models adjusted for baseline age, energy intake, physical activity (PASE) questionnaire, smoking, fat mass, diabetes, and total protein intake (when studying protein distribution). Results Over 2 years, men lost 2.5±4.0% LM and 1.5±4.8% aLM, women lost 2.0±3.4% LM ( P <0.05 vs. men) and 1.2±5.3% aLM ( P =ns vs. men). Protein intake distribution at breakfast, lunch and dinner was 20/35/41% in men and 18/38/39% in women (P<0.05 vs. men). After adjustment for potential confounders, energy‐adjusted protein intake was associated with LM ( P <0.05), and aLM ( P <0.05) both at T1 and T3 only in men. However, the link between total protein intake and LM and aLM at T3 was abolished by further adjustment for baseline LM and aLM, indicating their predominant predictive values. The CV of protein intake distribution was negatively associated with T1 LM [β±SE: −3.27±1.31; P <0.05] and aLM [−1.43±0.67; P <0.05] in the fully adjusted model, again only in men; but not with T3 when adjusted for baseline values. Interestingly, in half of the cohort with protein intake below the median (<1.0 g/kg/d), protein distribution was associated with aLM in men and women at both time points, but not when controlling for T1 aLM. This association was not significant in participants with protein intake ≥1.0 g/kg/d. Consistently, 2‐y changes in LM or aLM were not related to the quantity‐ or distribution of protein intake in either sex. Conclusions Greater quantity and even distribution of daily protein intake were independently and cross‐sectionally associated with higher LM and aLM in men, and not related to changes over 2 years in either sex. Protein distribution may have more impact on lean mass when intakes are low, i.e. below 1.0 g/kg/d, in both men and women. These findings could have implications in establishing recommendations, upon confirmation with larger cohorts and longer‐term follow‐up. Support or Funding Information Funded by Dairy Farmers of Canada and Fonds de la recherche en sante‐Quebec.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".