CORRELATION OF COGNITIVE STATUS WITH IMPAIRED SKIN MICROVASCULAR REACTIVITY IN NEWLY DIAGNOSED UNTREATED HYPERTENSIVES
Bibliographic record
Abstract
Objective: Untreated hypertension (HT) in the early midlife is strongly correlated with mild cognitive impairment (MCI) later on, in the elderly. HT is also associated with significant microvascular alterations in several vascular beds including skin. Skin microcirculation has been correlated with microvascular alterations in other beds and has been proposed as a model of generalized microvascular dysfunction. We sought to estimate the prevalence of MCI in newly diagnosed untreated hypertensive patients (UHTs) and compare it to normotensive healthy individuals (NTs). We also investigated possible associations between MCI and parameters of skin microvascular reactivity by using a novel laser technique, namely Laser Speckle Contrast Imaging (LSCI). Design and method: We consecutively enrolled 51 individuals including 31 UHTs and 20 NTs. All patients were free from diabetes mellitus or any cardiovascular disease. Diagnosis of HT was based on conventional office BP and 24h-Ambulatory Blood Pressure Measurement (24h-ABPM) criteria, according to standard guidelines. The Montreal Cognitive Assessment test (MoCA test) was used to assess cognitive function with a score equal to or lower than 26 being suggestive of MCI for all participants. The LSCI technique combined with post-occlusive reactive hyperemia (PORH) protocol was used to assess skin microvascular reactivity. Skin microvascular results are expressed as percentage increase of cutaneous perfusion from baseline to peak condition (base to peak flux, %). Results: Base to peak flux was significantly lower in UHTs compared to NTs (211.0 ± 53.1 vs 154.0 ± 53.0 %, p < 0.01). Although the prevalence of MCI did not differ between UHTs and NTs, there was also a significant positive correlation between the MoCA test score and base to peak flux in the UHTs group (r = 0.491, p = 0.02). Conclusions: Although UHTs did not present impaired cognitive status compared to NTs, they do present impaired skin microvascular reactivity which is strongly correlated with their cognitive status from the very early stages of hypertension. Early and effective treatment of essential hypertension may contribute to the prevention of MCI and the improvement of skin microvascular function in hypertensive patients.
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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 | 0.001 | 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".