The relationship between weight and CVD risk factors in a sample population from central Iran (based on IHHP)
Bibliographic record
Abstract
BACKGROUND: Atherosclerosis is one of the leading causes of mortality all around the world. Obesity is an independent risk factor for atherosclerosis and cardiovascular diseases (CVD). In this respect, we decided to examine the effect of the subgroups of weight on cardiovascular risk factors. METHODS: This cross-sectional study was done in 2006 using the data obtained by the Iranian Healthy Heart Program (IHHP) and based on classification of obesity by the World Health Organization (WHO). In this study, the samples were tested based on the Framingham risk score, Metabolic Measuring Score (MMS) and classification of obesity. Chi-square and ANOVA were used for statistical analysis. RESULTS: 12514 people with a mean age of 38 participated in this study. 6.8% of women and 14% of men had university degrees (higher than diploma). Obesity was seen in women more than men: 56.4% of women and 40% of men had a Body Mass Index of (BMI) ≥ 25 Kg/m2. 13% of the subjects had FBS > 110 and13.9% of them were using hypertensive drugs. In this study, we found that all risk factors, except HDL cholesterol in men, increased with an increase in weight. This finding is also confirmed by the Framingham flowchart for men and women. CONCLUSION: One of every two Americans, of any age and sex, has a Body Mass Index of (BMI) ≥ 25 Kg/m2. Obesity associated CVD and other serious diseases. Many studies have been done in different countries to find the relationship between obesity and CVD risk factors. For example, in the U.S.A and Canada they found that emteropiotic parameters, blood presser and lipids increased by age(of both sexes). Moreover, another study done in China, which is a country in Asia like Iran, shows that BMI has an indirect effect on HDL cholesterol, LDL cholesterol and triglyceride. This data is consistent with the results of the current study. However, In China they found that this relationship in men is stronger than women, but our study reveals the opposite. Keywords: Body Mass Index (BMI), Overweight, Cardiovascular Risk Factors, Framingham Risk Score, Metabolic Syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".