The prevalence of hypertension and metabolic clustering (hyperlipidemia and hyperinsulinemia) in healthy older adults
Bibliographic record
Abstract
Ageing and major cardiovascular risk factors, such as hypertension, hyperlipidemia, and diabetes, often cluster in the same individuals. We examined the prevalence of these metabolic abnormalities in a healthy cohort of older adults. All subjects underwent a maximal treadmill exercise test to exclude silent cardiovascular disease, and also provided a blood specimen for biochemical analyses. Fifty men (75.5±7.6 years) and 62 women (76.1±7.2 years) were evaluated. Hypertension was defined within the present study as a measured systolic pressure of greater than 140 mmHg. Thirty percent of the men and 31% of the women had hypertension. Ten percent of the men and 22% of the women had no risk factors. Thirty-eight percent of the men and 31% of the women had at least two of the three risk factors. Clustering of all three risk factors was found in 10% of this study population. The most prevalent metabolic risk factor of the syndrome was elevated insulin levels in men (60%) and women (45%). Our data suggest the metabolic syndrome is prevalent in healthy older adults. Future study of this cohort over 10 years may provide data regarding the impact on morbidity and mortality.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".