P147 Association Between Inflammatory Markers of Low Intention and Arterial Stiffness
Bibliographic record
Abstract
Abstract Extracellular and biochemical changes in the vessel contribute to the stiffening, this process is determinant for the increase of the Pulse Wave Velocity (PWV) and subsequent increase of the central and brachial arterial pressures [1,2]. These vascular alterations are denominated of TOD (target organ damage) and represent a point of association between cardiovascular risk factors and cardiovascular events [2,3]. Chronic low-grade inflammation associated with endothelial dysfunction and increased number of biomarkers, such as ultra-sensitive C-reactive protein (hsCRP), cytokines such as interleukins, fibrinogen, platelets, leukocytes and hematocrit [4,5,6]. Methods A cross-sectional exploratory study on a representative population of a community in Salvador-Bahia-Brazil. The data came from a including 301 individuals. 150 were initially assessed from December 2016 to May 2019. PWV measurement for the carotid-femoral by an ATCor SphygmoCor, data not demonstrated in this poster. Blood samples were collected to biochemistry analysis, ADVIA1800 ® (SiemensHealthcare Japan/Canada). The committee for research on human was done. Results The data show a predominance of women (65%). Changes in leukocytes, platelets and hematocrit were more prevalent in men, as observed in Table 1. Mean values of changes in ultra-sensitive CRP values were higher in women (0.43) than in men (0, 25). Conclusion Studies correlate the markers evaluated in this study as positive predictive factors for arterial stiffening. Data from the literature show these preliminary changes present in the male population, as observed in our population. The cytokines IL-1, 6 and 18, the chemokines MCP-1 and 3 and the adhesion molecules VCAM, ICAM are being evaluated to better respond to these findings. Table 1 WBC HEMATOCRIT PLATELETS Column B vs Column A WBC vs WBC Ht WOMAN vs Ht MAN PLAT WOMAN vs PLAT MAN Unpaired t -test p- value 0.0004 0.0246 0.0337 p -value summary *** * * Significantly different ( p < 0.05)? Yes Yes Yes One- or two-tailed p -value? Two-tailed Two-tailed Two-tailed t , df t = 4.840, df = 12 t = 2.980, df = 6 t = 2.397, df = 12 How big is the difference? Mean of column A 8950 −3.567 311.3 Mean of column B 5767 3.167 250.2 Difference between means (B - A) ± SEM −3183 ± 657.7 1.197 −61.09 ± 25.49 95% confidence interval −4616 to −1750 −6.496 to -0.6378 −116.6 to −5.553 R squared (eta squared) 0.6613 0.5968 0.3237
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".