Effect of the Timing of Weight Cycling During Adulthood on Mortality Risk in Overweight and Obese Postmenopausal Women
Bibliographic record
Abstract
Inconsistent results exist for whether or not weight cycling (WgtC) and weight variability (WgtV) increase mortality risk. The aim of this study was to examine the effect of WgtC and WgtV during adulthood on mortality risk. Data was obtained from the Women's Health Initiative (WHI) observational study (OS) dataset, acquired from the National Heart, Lung and Blood Institute (N = 47,473 overweight and obese women; age 50-79 years). Women were categorized (stable; WgtV: weight-gainer or loser; or WgtC) based on weight changes during early (18-35 years), mid (35-50 years), and late (50 years to current age) adulthood. Those with weight changes of <5% during all three time-periods were classified as being stable-weight. Weight-gainers were those with at least one period of weight-gain (≥5%) without a period of weight-loss (≥5%), and weight-losers were those with at least one period of loss without a period of gain during all time-periods. Those who experienced both a period of weight-gain and loss (≥5%) were categorized as WgtC. Compared to stable-weight individuals, WgtC and WgtV across adulthood were not significantly associated with mortality risk when the age-period of weight change was not considered. However, when considering the age period, increased mortality risk was observed for every 5 kg of weight-gain during early (hazard ratio (HR) = 1.04 (1.00-1.07)) or mid-adulthood (HR = 1.05 (1.02-1.08)), or for every 5 kg of weight-loss since mid (HR = 1.12 (1.01-1.24)) or late-adulthood (HR = 1.12 (1.04-1.20)). In conclusion, merely investigating WgtC and WgtV by weight changes across adulthood may not be sufficient to fully describe mortality risk, and the age at which the weight change occurred might be as important to consider.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".