Bibliographic record
Abstract
It is well known that there is a greater prevalence of hypertension in obese than among normal weight subjects. However, not every obese individual is hypertensive, indicating that obesity is a heterogeneous condition. Although excess fatness may contribute to high blood pressure (BP) in obese patients, the best indices of adiposity which relate to BP are still unclear. Kotchen and colleagues have investigated the relation between indices of adiposity (body mass index, waist circumference, waist-to-hip and waist-to-height ratios, percentage of body fat derived from skinfold thicknesses) and BP in normotensive and untreated hypertensive African Americans.1 BP–adiposity relationship was also evaluated in normotensive and untreated hypertensive non-Hispanic black and non-Hispanic White participants from National Health and Nutritional Examination Survey. They found that while indices of adiposity were higher in hypertensive subjects, the association between adiposity indices and BP was only seen in normotensive subjects. In a stepwise regression analysis, waist-to-hip ratio was the best adiposity parameter explaining the variation in BP in the whole population while waist circumference was the best correlate of BP in normotensive individuals. Only waist circumference was predictive of BP in untreated hypertensive individuals. The authors also concluded that the BP–adiposity relationship in hypertensive individuals may be modulated by a combination of environmental and genetic factors. The population studied by Kotchen et al. should be considered mostly overweight from a BMI viewpoint since waist circumference was only increased in hypertensive women.1 Percent body fat was low in normotensive and hypertensive men and waist-to-hip ratio was also within normal range in men and women in the Milwaukee cohort, while waist circumference was higher in the National Health and Nutritional Examination Survey cohort. Also, insulin levels or insulin resistance index was not reported. Obesity, insulin resistance, and systemic hypertension are clearly interrelated. Although the pathophysiology linking these variables is not clear, a meta-analysis supported the role of hyperinsulinemia in the pathogenesis of systemic hypertension.2 This role was corroborated in cohorts such as the San Antonio Heart Study,3 the Atherosclerosis Risk in Communities study,4 the Coronary Artery Risk Development in Young Adults study,5 and the eastern Finland cohort. Pathophysiological mechanisms linking insulin to hypertension are numerous (stimulation of the sympathetic nervous system, renal sodium retention, decreased heart rate variability, hemodynamic effects). The presence of abdominal obesity may influence the pathophysiological events linking insulin resistance to hypertension as shown by the association between BP and waist. Nevertheless, the link between insulin resistance and BP suffers from a lack of consistency, and ethnicity may be an important potential confounder. The strength of relationship between insulinemia, insulin resistance, and hypertension varies widely according to ethnic groups, since weak associations has been reported in African Americans compared with white Americans in the Atherosclerosis Risk in Communities4 and the Coronary Artery Risk Development in Young Adults study.5 Although the authors suggested that control of body weight may be more effective in the prevention compared to the treatment of hypertension, weight loss should always be mandatory in individuals with risk factors. Nevertheless, this study evokes that genetic background impact the relationship between BP and obesity. The author declared no conflict of interest.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".