Association between serum choline and betaine concentrations and longitudinal changes of body composition in community-dwelling middle-aged and older Chinese adults
Bibliographic record
Abstract
Previous studies suggest that betaine and choline may be beneficial for body composition. However, no longitudinal study has been conducted to illustrate if choline and betaine have long-term effects on changes in body composition. This study aimed to prospectively investigate the association between serum choline and betaine concentrations and 3-year changes in body composition in community-dwelling Chinese adults. This present analysis used data from 1384 women and 554 men aged 40–75 years. Serum concentrations of betaine and choline at baseline were assessed using high-performance liquid chromatography-tandem mass spectrometry. Body composition parameters, i.e., muscle mass (MM), fat mass (FM), and body fat percentage (FM%) were measured using dual-energy X-ray absorptiometry at the first and the second follow-ups. After adjustment for potential cofounders, higher serum choline concentrations were associated with a lower decrease in MM in men (β = 0.022, P = 0.025) and a lower increase in FM and FM% in women with baseline choline concentrations below 21.5 μmol/L (all P for nonlinearity = 0.007); higher serum betaine concentrations were associated with a lower decline in MM and a lower increase in FM and FM% among men whose betaine concentrations were lower than 55 μmol/L (all P for nonlinearity < 0.05). These findings suggest that higher concentrations of serum choline and betaine may be associated with favorable changes in body composition profiles among men and women who have relatively low concentrations, especially in men. Novelty Higher concentrations of serum choline and betaine were associated with favorable changes in body composition. Such favorable associations were more pronounced in men.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".