Metabolically Obese Normal-Weight Phenotype as a Risk Factor for High Blood Pressure: A Five-Year Cohort
Bibliographic record
Abstract
Background: The metabolically obese normal-weight (MONW) phenotype has been considered a risk factor for different chronic diseases, but its role in high blood pressure (HBP) is still unclear. The aim of the study is to determine if the MONW phenotype constitutes a risk factor for hypertension in Peruvian adults belonging to a 5-year cohort. Methods: This is a retrospective cohort study. A secondary analysis from the database of the PERU MIGRANT study was carried out from the MONW and non-MONW cohorts; after a 5-year follow-up, the appearance of HBP was evaluated in the subjects of both cohorts. To assess the strength and magnitude of the association, a Poisson regression model (crude and adjusted) with robust variance was used. The measure of association was the relative risk (RR). Results: The incidence of HBP was 11.30%. In the multivariable analysis, subjects with the MONW phenotype had a 2.879-fold risk of presenting HBP in 5 years compared with those who were not MONW at the beginning of the study; this was adjusted for categorized age, sex, group, and state of smoker and alcohol drinker (RR: 2.055; 95% confidence interval (CI): 1.118 - 3.777; P = 0.020). Conclusions: The presence of the MONW phenotype doubled the incidence of HBP, even after adjusting for other covariates. However, studies in this field should continue. If these findings are confirmed, it should be considered that presenting an adequate weight for height should not be interpreted as a condition free of metabolic alterations, so screening for hypertension should be carried out regardless of whether or not the body mass index obtained is considered normal. J Endocrinol Metab. 2023;13(1):13-19 doi: https://doi.org/10.14740/jem855
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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 | 0.000 |
| 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 | 0.001 |
| 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".