Involvement of AGE and Its Receptors in the Pathogenesis of Hypertension in Elderly People and Its Treatment
Bibliographic record
Abstract
Both systolic and diastolic blood pressures increase with age up to 50 to 60 years of age. After 60 years of age systolic pressure rises to 84 years of age but diastolic pressure remains stable or even decreases. In the oldest age group (85-99 years), the systolic blood pressure (SBP) is high and diastolic pressure (DBP) is the lowest. Seventy percent of people older than 65 years are hypertensive. This paper deals with the role of advanced glycation end products (AGE) and its cell receptor (RAGE) and soluble receptor (sRAGE) in the development of hypertension in the elderly population. Plasma/serum levels of AGE are higher in older people as compared with younger people. Serum levels of AGE are positively correlated with age, arterial stiffness, and hypertension. Low serum levels of sRAGE are associated with arterial stiffness and hypertension. Levels of sRAGE are negatively correlated with age and blood pressure. Levels of sRAGE are lower in patients with arterial stiffness and hypertension than patients with high levels of sRAGE. AGE could induce hypertension through numerous mechanisms including, cross-linking with collagen, reduction of nitric oxide, increased expression of endothelin-1, and transforming growth factor-β (TGF-β). Interaction of AGE with RAGE could produce hypertension through the generation of reactive oxygen species, increased sympathetic activity, activation of nuclear factor-kB, and increased expression of cytokines, cell adhesion molecules, and TGF- β. In conclusion, the AGE-RAGE axis could be involved in hypertension in elderly people. Treatment for hypertension in elderly people should be targeted at reduction of AGE levels in the body, prevention of AGE formation, degradation of AGE in vivo, downregulation of RAGE expression, blockade of AGE-RAGE interaction, upregulation of sRAGE expression, and use of antioxidants.
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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 | 0.000 |
| 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".