ASSOCIATION OF SUBCLINICAL INFLAMMATION MARKERS WITH PRIMARY HYPERTENSION IN CHILDREN - A SYSTEMATIC REVIEW WITH META-ANALYSIS
Bibliographic record
Abstract
Objective: Primary hypertension (PH) is a growing threat to children's health. The pathogenesis of this phenomenon is not fully known, and subclinical inflammation and activation of the immune system are potential explanations. This systematic review and meta-analysis aimed to systematically determine whether there is an association between low–grade inflammation markers and PH in children. Design and method: The MEDLINE, EMBASE, and Cochrane databases were searched up to March 2023, with no language restrictions, for cohort, cross-sectional, and case-control studies; additional references were obtained from reviewed articles. The included studies needed to investigate an association between any inflammation markers and PH defined by the authors. Participants of the study were children (<18 years old) with PH and healthy controls. The main outcome measured was the concentration of any subclinical inflammation markers in children with PH and in healthy controls. This meta-analysis included 12 studies published between 2005 and 2022, enrolling 1220 patients (689 with PH and 531 healthy controls). The data were analyzed using Review Manager. Pooled mean difference (MD) with 95% confidence interval (95% CI) was used to assess the differences in inflammation markers levels. All analyses were based on the random-effect model. The risk of bias was assessed using the Newcastle-Ottawa Scale. Results: There was a significant difference between hypertensive and control groups in high-sensitivity C-reactive protein (hsRCP) concentration (MD: 0.07 95%CI [0.04,0.09]), intercellular adhesion molecule 1 (ICAM-1) (MD: 85.28 95%CI: [50.57-119.99]), vascular cell adhesion molecule 1 (VCAM-1) (MD: 259.78 95%CI: [22.65-496.91]), neutrophil count (MD: 0.80 95%CI [0.53-1.07]), monocyte count (MD: 0.07 95%CI: [0.02-0.12]), and neutrophil-to-lymphocyte ratio (NLR) (MD: 0.49 95%CI: [0.32-0.65]). There was no difference in terms of interleukin 6 (IL-6), lymphocyte count, platelet count, mean platelet volume (MPV), as well as in platelet-to-lymphocyte (PLR) and monocyte-to-lymphocyte (MLR) ratios. Conclusions: Some easily accessible markers of low-grade inflammation might be used as an additional tool for diagnosis and screening for hypertension in pediatric patients. These promising results should be validated in large and well-conducted studies.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".