Rethinking Obesity Classification: Population-Level Insights from a Small Island States
Bibliographic record
Abstract
Abstract Background Traditional measures such as BMI are commonly used to define obesity, but they may not capture the complexity of obesity-related risk. This study applied newly proposed definitions of pre-obesity and clinical obesity by the Lancet Diabetes & Endocrinology Commission and compared them with standard anthropometric measures in Malta-a population with a high prevalence of obesity. Methods A nationally representative, cross-sectional health examination survey was conducted in Malta between 2014 and 2016 (weighted N = 3,947). Anthropometric, sociodemographic, behavioural, and biochemical data were collected. Obesity was assessed using BMI, waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), and the newly proposed definitions. Metabolic syndrome (MetS) was used as a clinical complication. Binary logistic regression models were used to examine associations with MetS, adjusting for age, sex, education, and locality. Results Obesity prevalence varied by classification: 34.08% (BMI), 34.81% (WC), 68.94% (WHR), 39.17% (WtHR), and 22.40% (clinical obesity). All anthropometric measures showed significant associations with MetS, even after adjustment. Obesity defined by BMI had the strongest association (OR: 18.84, 95% CI: 14.13-25.13), while the newly defined clinical obesity also showed a strong association (OR: 9.89, 95% CI: 8.37-11.67). The pre-obesity category was negatively associated with MetS (OR: 0.16, 95% CI: 0.12-0.22), supporting its distinction as a preclinical state. Conclusions Different anthropometric measures identify different subgroups within the population, yet all are significantly associated with MetS. The findings support the utility of the Lancet Commission's refined definitions, particularly the pre-obesity classification, in guiding early intervention strategies. Key messages • Applying multiple anthropometric measures reveals variation in obesity classification, but all remain significantly associated with metabolic complications, highlighting their clinical value. • The newly defined pre-obesity category shows a protective association with metabolic syndrome, supporting its use as a meaningful stage for early risk identification and intervention.
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 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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".