Initial Damage and Frailty: Going to Sleep Hungry in Childhood
Bibliographic record
Abstract
Abstract Frailty deficits accumulate as individuals age, whereas resilience (the ability to repair damage) and robustness (the ability to resist damage) underlie the speed of aging from one birthday to the next. Nutritional deprivation reduces the redundancies that allow robustness and resilience. In childhood, nutritional deprivation increases the burden of initial damage. Even though the functional capacity of organs in young adults is high, changes in health are often influenced by social circumstance. Here, we studied the overall impact of childhood hunger on a population’s frailty. We applied a deficit accumulation approach to construct a 47-item frailty index with domains of morbidities, cognition, lifestyle, and disabilities. We analyzed data from 10260 people aged 65+ years, participants in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2005 cohort. They were followed in 2008, 2011, 2014, and 2018 (men=4570, age=84.6±10.6, years of education=3.5±5.6; women=6150, age=89.6±11.6, years of education=0.8±4.5). Most participants (73.0%) responded that they had slept hungry as children. A regression model using Generalized Estimating Equations showed that all predictors (sex, age, education, hungry in childhood) significantly affected the frailty index (p-value < 0.001). We found that children who had not experienced hunger had fewer health deficits (beta coefficient for “not hungry” = -0.010 (95% CI=-0.02∼-0.01) versus “hungry”, adjusted for age, sex, and education. Reflecting better ability to repair, as older adults, they experienced less smooth transitions in aging. In short, children who did not experience hunger on average have a slower speed of aging, with less frailty and greater resilience 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".