Ethnicity and obesity: why are some people more vulnerable?
Bibliographic record
Abstract
Obesity is a global problem that affects all ethnic groups and managing it is a major challenge. In developing countries obesity coexists with underweight.BMI is the most widely used measure of obesity. World Health Organization cut-off values of BMI =25 or =30 kg/m2 for over weight and obesity, respectively, have been used worldwide for several years to assess the prevalence of obesity of varying degrees. The highest prevalence of overweight and obesity in the world is to be found in the Western Pacific Islands, especially among the populations of Nauru and Tonga, where it reaches 80–90%. Sub-Saharan Africa has the lowest prevalence of obesity. The greatest increase in obesity is occurring in countries with a diverse ethnic population, such as Mauritius and Brazil. An increased percentage of body fat is normally coupled to an increase in body weight. However, there is evidence to show that the association between BMI, percentage and distribution of body fat differs across populations, with Asians having the highest percentage of body fat compared with other populations. Asians also have a higher amount of visceral adipose tissue. The variation in percentage of body fat and body fat distribution relative to BMI across ethnic groups is reflected in ethnic differences in the health risks associated with obesity. For example, populations from the Asia-Pacific region have been found to have substantial risks of cardiovascular disease (CVD) below a BMI of 25 kg/m2. In all populations, cardiovascular risk increases with increasing waist circumference, even though it is influenced by ethnicity. For example, compared with white populations, Inuit and Polynesians have been found to have lower blood pressure, lipids, stimulated glucose and insulin levels for the same levels of waist circumference. The metabolic impact of different levels of obesity differs considerably across populations, especially with regard to diabetes and CVD. Therefore the ‘one-size-fits-all’ approach adopted internationally must be reconsidered and carefully analysed. BMI, waist circumference and waist-hip ratio all have their limitations when it comes to comparing obesity and its risk factors across ethnic groups and populations. The influence of genetics on the association between obesity and health risks remains unresolved. Data on obesity and metabolic risk factors including Inuit living in Greenland and Denmark showed that Inuit in Denmark followed the same patterns as an ethnic Danish reference population with regard to the association between obesity and cardiovascular risk factors. Lifestyle and environmental factors may therefore be more important than genetic factors regarding the influence of obesity on disease risk. Udgivelsesdato: 2008
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".