ETHNIC DIFFERENCES IN THE ASSOCIATION BETWEEN HANDGRIP STRENGTH AND THE PREVALENCE OF HYPERTENSION
Bibliographic record
Abstract
Abstract Objective: Higher prevalence of hypertension among afro-descendants compared to other ethnic groups has been reported in high-income countries. Genetic, behavioral, and socioeconomic factors could explain these differences, and body composition could contribute. This study aims to assess the association between handgrip strength (HGS) and hypertension in different ethnicities in a middle-income country. We additionally evaluated the role of socioeconomic and behavioral factors to explain ethnic differences. Design and method: We evaluated the association between HGS measured by Jamar Dynamometer and the risk hypertension in 4102 adults aged 35 to 70 years from 3 ethnic groups (593 whites, 3001 mestizos, 508 afro-descendants) enrolled in the prospective population-based cohort study PURE-Colombia. We calculated unadjusted and adjusted odds ratios (95% CI) for the prevalence of hypertension across tertiles of HGS and the association with anthropometric, socioeconomic, and behavioral factors. Results: Results: The overall prevalence of hypertension was 39.2%, being greater in the afro-descendants (46.3%) than in whites (41.5%) and mestizos (37.6%). A higher prevalence of hypertension was found in mestizos and afro-descendants in tertile 1 of HGS (< 21 kg) compared to those in tertile 3 of HGS (> 29.7 kg) (OR = 1.48; 95% CI: 1.20 - 1.84 and OR = 1.70; 95% IC: 1.01 – 2.85, respectively). However, when adjusting by confounders, the association lost statistical significance. The prevalence of hypertension was positively associated with body mass index and waist circumference. There was a higher prevalence amongst individuals with a low educational level compared to those with a high educational level in whites (OR = 1.74; 95% CI: 1.08 - 2.78), mestizos (OR = 1.5 95% CI: 1.13 - 2.01), and afro-descendants (OR = 2.46; 95% CI: 1.11 - 5.45). Behavioral factors, such as alcohol and tobacco intake, protein, and fat consumption, were not associated with a higher prevalence of hypertension. Conclusions: HGS could partially contribute to explaining ethnic differences in the prevalence of hypertension. However, socioeconomic factors such as education level play a key role. Therefore, a greater focus on screening for low HGS, interventions aimed at attenuating age-related declines, and addressing social inequalities could positively impact these differences.
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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 | 0.000 | 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) | 0.002 | 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".