Life course associations of height, weight, fatness, grip strength, and all‐cause mortality for high socioeconomic status Guatemalans
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to investigate the association between physical growth in preadult life with five outcomes at ages 64 to 76: weight, body mass index (BMI), estimated body fat percentage, hand grip strength, and mortality. METHODS: Super-imposition by translation and rotation (SITAR) growth curves of 40 484 Guatemalan individuals aged 3 to 19 years were modeled for the parameters of size, timing and intensity (peak growth velocity, eg, cm/year) of height, weight, BMI, and grip strength. Associations between the SITAR parameters and old age outcomes were tested using linear and binary logistic regression for a follow-up sample of high socioeconomic status (SES) Guatemalans, of whom 50 were aged 64 to 76 years at re-measurement and 45 died prior to the year 2017. RESULTS: SITAR models explained 69% to 98% of the variance in each outcome, with height the most precise. Individuals in the follow-up sample who had a higher BMI before the age of 20 years had higher estimated body fat (B = 1.4 CI -0.02-2.8) and BMI (B = 1.2, CI 0.2-2.2) at the ages of 64 to 76 years. Those who grew slower in height but faster in weight and BMI before the age of 20 years had higher BMI and body fat later in life. CONCLUSIONS: These findings highlight the importance of a life course perspective on health and mortality risk. Childhood exposures leading to variation in preadult growth may be key to better understanding health and mortality risks in old age.
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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 | 0.001 |
| 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".