Observational studyJournal of thrombosis and thrombolysis2015
Impact of red blood cells count on the relationship between high density lipoproteins and the prevalence and extent of coronary artery disease: a single centre study [corrected].
Observational study in Journal of thrombosis and thrombolysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Triglycerides/High-Density Lipoprotein Ratio and Coronary Artery Disease: Results from a Large Single-Center Study.Journal of clinical medicine · 2025Article
- The Role of Red Blood Cells in Cholesterol Accumulation and Atherosclerotic Plaque Instability: A Perspective on Atherosclerosis.Current cardiology reviews · 2025Review
- Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort.Circulation reports · 2022Article
- Associations of Red Cell Distribution Width With Coronary Artery Calcium in the General Population.Angiology · 2022Article
- Genomics of hypertension: the road to precision medicine.Nature reviews. Cardiology · 2021Review
- Association of peripheral differential leukocyte counts with dyslipidemia risk in Chinese patients with hypertension: insight from the China Stroke Primary Prevention Trial.Journal of lipid research · 2017Article
- The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.Cell · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have hypothesized that high red blood cells (RBC) count can potentially play an atheroprotective role in patients with coronary atherosclerosis. We, therefore, have investigated the relationship between high density lipoproteins cholesterol (HDL-C) and RBC levels in patients undergoing coronary angiography. Coronary artery disease (CAD) is a major cause of mortality. Impaired lipid profile represents a major risk factor for atherosclerosis. High density lipoprotein (HDL) is a key factor in atherosclerosis disease development. RBC can mimic HDL's reverse cholesterol transportation with a potential atheroprotective role. Coronary angiography has been evaluated in 3,534 patients. Fasting samples were collected for haematology and lipids levels assessment. Coronary disease was defined for at least 1 vessel stenosis >50 %. Patients were divided according to HDL-C and RBC tertiles. Lower HDL-C was significantly associated to the prevalence of CAD (84.8 vs 78.5 vs 67.3 %, p ≤ 0.001; adjusted OR [95 % CI] = 1.55 [1.3-1.8], p < 0.001) and severe CAD (30 % vs 30 % vs 24.4 %, p = 0.002; adjusted OR [95 % CI] = 1.08 [1.01-1.16], p = 0.02), this relationship was maintained even dividing our population according to RBC tertiles (p < 0.001).In conclusion, HDL-C levels are directly related to RBC count and inversely to the prevalence and extent of coronary disease. Higher RBC levels can reduce the risk of CAD in patients with lower HDL-C levels, suggesting an important atheroprotective role.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.