Effects of BMI on Blood Pressure and Urinary Excretion of Sodium, Chloride and Potassium in Young Paraguayans
Bibliographic record
Abstract
Background: Obesity has been associated with an increased risk for coronary disease, type 2 diabetes, hyperlipidemia and mortality. The demographics of Paraguay show that most of the population is of mixed European and Amerindian descent. Most of the studies performed in Hispanics, particularly in the United States fail to identify their country of origin. There is a paucity of data in Paraguay about the relationship between BMI and BP in young adults. Furthermore, there is an increased prevalence of overweight, obesity and hypertension in the country. In this study, we hypothesized that even early increments of BMI are associated with increased blood pressure (BP) readings in young adults in Paraguay. Methods: Cross-sectional study, performed in 72 adult participants, from Asuncion Paraguay. There is no similar study published in this country. BMI, BP readings and urinary excretion of Na, K and Cl were studied. Results: Mean age 25. 2 ± 1.52 years. Mean BMI 24.1 ± 3.66. There was a linear relationship between BMI and SBP, DBP, MAP and urine Na, K and Cl across all levels. A multivariate analysis, adjusting for gender, showed that increased BMI is associated with increased BP readings. A BMI of 23.4 was the best cut-off for a BP equal or higher than 135/85 mmHg (pre- hypertension), (AUC = 0.80). A BMI of 24.2 was the best cut off for a BP equal or higher than 140/90 (AUC = 0.80). After excluding participants with SBP equal or higher than 140 mmHg and DBP equal or higher than 90 mmHg, BMI was still associated with increased SBP and DBP even in normotensive individuals. After excluding obese and overweight patients, we found that even a small increase of BMI was associated with an increase in blood pressure. Conclusions : BMI values of 24.2 and 23.4 were associated with BP of 140/90 and 135/85 respectively. Since the BMI cut-off for obesity was established in different cohorts, we might need to create a more specific set of values for the Paraguayan population, as the values proposed by the American Heart Association are probably rather high for this population. doi:10.4021/wjnu8e
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".