Body mass index and all-cause mortality in a large Chinese cohort
Bibliographic record
Abstract
BACKGROUND: Obesity is known to be associated with an increased risk of death, but current definitions of obesity are based on data from white populations. We examined the association between body mass index (BMI) and the risk of death in a large population of adult Chinese people. METHODS: We examined the association between body mass index (BMI) and all-cause mortality prospectively among 58,738 men and 65,718 women aged 20 years and older enrolled in 1998-1999 from four national health screening centres in Taiwan. We used Cox proportional hazards regression analyses to estimate the relative risks of all-cause mortality for different BMI categories during a maximum follow-up of 10 years. RESULTS: A total of 3947 participants died during the follow-up period. The lowest risk of death was observed among men and women who had a BMI of 24.0-25.9 (mean 24.9). After adjustment for age, smoking status, alcohol intake, betel-nut chewing, level of physical activity, income level and education level, we observed a U-shaped association between BMI and all-cause mortality. Similar U-shaped associations were observed when we analyzed data by age (20-64 or ≥ 65 years), smoking (never, < 10 pack-years or ≥ 10 pack-years) and presence of a pre-existing chronic disease, and after we excluded deaths that occurred in the first three years of follow-up. INTERPRETATION: BMI and all-cause mortality had a U-shaped association among adult Chinese people in our study. The lowest risk of death was among adults who had a BMI of 24.0-25.9 (mean 24.9). Our findings do not support the use of a lower cutoff value for overweight and obesity in the adult Chinese population.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".