Association of meal timing with body composition and cardiometabolic risk factors in young adults
Bibliographic record
Abstract
Abstract Purpose To investigate the association of meal timing with body composition and cardiometabolic risk factors in young adults. Methods In this cross-sectional study participated 118 young adults (82 women; 22 ± 2 years old; BMI: 25.1 ± 4.6 kg/m 2 ). Meal timing was determined via three non-consecutive 24-h dietary recalls. Sleep outcomes were objectively assessed using accelerometry. The eating window (time between first and last caloric intake), caloric midpoint (local time at which ≥ 50% of daily calories are consumed), eating jetlag (variability of the eating midpoint between non-working and working days), time from the midsleep point to first food intake, and time from last food intake to midsleep point were calculated. Body composition was determined by DXA. Blood pressure and fasting cardiometabolic risk factors (i.e., triglycerides, total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, and insulin resistance) were measured. Results Meal timing was not associated with body composition ( p > 0.05). The eating window was negatively related to HOMA-IR and cardiometabolic risk score in men ( R 2 = 0.348, β = − 0.605; R 2 = 0.234, β = − 0.508; all p ≤ 0.003). The time from midsleep point to first food intake was positively related to HOMA-IR and cardiometabolic risk score in men ( R 2 = 0.212, β = 0.485; R 2 = 0.228, β = 0.502; all p = 0.003). These associations remained after adjusting for confounders and multiplicity (all p ≤ 0.011). Conclusions Meal timing seems unrelated to body composition in young adults. However, a longer daily eating window and a shorter time from midsleep point to first food intake (i.e., earlier first food intake in a 24 h cycle) are associated with better cardiometabolic health in young men. Clinical trial registration NCT02365129 ( https://www.clinicaltrials.gov/ct2/show/NCT02365129?term=ACTIBATE&draw=2&rank=1 ).
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".