Early-Onset of Obesity Model: Impact of Early-Onset Obesity on Comorbidity Risk and Life Expectancy
Bibliographic record
Abstract
Introduction: Early-onset obesity increases the risk of developing comorbidities and decreases life expectancy with many variables such as age of onset, severity, and duration of obesity each having an individual influence. Here, we present findings from a model that aims to assess the impact of early-onset obesity. METHODS: The Early-Onset Obesity Model (EOObesity Model) was built by integrating data from clinical studies with demographic information. It categorizes information into four primary groups: prevalence, morbidity risk, mortality risk, and impact of obesity duration. Type 2 diabetes, cardiovascular events (fatal and nonfatal events, cardiovascular disease, and coronary heart disease), metabolic dysfunction-associated steatotic liver disease, asthma, obstructive sleep apnea, and cancer were evaluated over a range of age and body mass index (BMI) z-scores. RESULTS: The EOObesity Model provides a systematic approach for estimating the impact of early-onset obesity on risk of comorbidities and on life expectancy by considering individual patient weight trajectories. We test different scenarios to illustrate the potential impact of age of onset and severity of obesity on the risk of various comorbidities, on life expectancy, and on disability-adjusted life years. The model indicates that severe early-onset obesity has a high impact on life expectancy with, for example, up to 42 years of life lost if a patient has a BMI z-score of 4 by the age of 4 years. DISCUSSION: The model and these scenarios underscore the predicted substantially detrimental effects of early-onset and prolonged obesity on life expectancy, and the increased risks of obesity-related comorbidities. They suggest that morbidity and mortality risks increase with obesity duration and severity in a nonlinear manner, thereby supporting the view that early weight-loss intervention and obesity prevention strategies may reduce long-term health risks substantially. .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".