Clinical Heterogeneity and Transitions of Obesity in Mexico
Bibliographic record
Abstract
CONTEXT: There is large variation in the individual risk of developing obesity-associated comorbidities. While obesity is highly prevalent in Mexico, data on the extent and heterogeneity of its associated comorbidities are lacking. OBJECTIVE: We estimated the prevalence of different obesity-associated comorbidities, and how they have changed over 15 years. METHODS: We gathered data from different editions of nationally representative health and nutrition surveys (ENSANUT) from 2006 to 2022. The prevalence of obesity and the coexistence with diabetes, dyslipidemia, hypertension, depression, and impaired mobility, which are outcomes used in the Edmonton Obesity Staging System (EOSS), which assesses 3 dimensions (medical, mental, and functional) across 5 incremental severity stages, by sex and age groups, were estimated across all included surveys. Metabolically healthy obesity (MHO) was defined as the absence of diabetes, dyslipidemia, and hypertension. RESULTS: A total of 20 758 participants were analyzed. Mean body mass index (BMI) increased progressively at all ages from 30.2 to 31.0 across survey rounds. Depression and impaired mobility were highly prevalent even among MHO individuals. While most people with obesity had at least one detectable abnormality, there was large heterogeneity in the presented comorbidities. The most prevalent EOSS categories were stage 2 for the medical dimension (90.1%), and stage 1 for the functional and mental dimensions (75.1% and 62.9%, respectively). The prevalence of obesity-related comorbidities increased with age but was similar across all surveys. In both sexes, MHO was less likely as age and BMI increased. CONCLUSION: The prevalence of obesity comorbidities has been stable over time in Mexico but increases with age. The rising prevalence of obesity and the aging of the population will cause additional burdens to the population and the health system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".