Evidence map›Paper›PMID 24599246›Full record

ArticlePloS one2014

Relation of leukocytes and its subsets counts with the severity of stable coronary artery disease in patients with diabetic mellitus.

Li-Feng Hong, Xiao-Lin Li, Song-Hui Luo, Yuan-Lin Guo, Jun Liu, Cheng-Gang Zhu, Ping Qing, Rui-Xia Xu, Na-Qiong Wu, Li-Xin Jiang and 1 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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  11. The Significance of the Relative Lymphocyte Count as an Independent Predictor of Cardiovascular Disease in Patients with Obstructive Sleep Apnea Syndrome.Medical principles and practice : international journal of the Kuwait University, Health Science Centre
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Li-Feng HongDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Division of Cardiology, The Fifth Hospital of Wuhan & Affiliated Guangci Hospital of Wuhan University, Wuhan, China.
Xiao-Lin LiDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Song-Hui LuoDivision of Cardiology, The Fifth Hospital of Wuhan & Affiliated Guangci Hospital of Wuhan University, Wuhan, China.
Yuan-Lin GuoDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Jun LiuDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Cheng-Gang ZhuDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Ping QingDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Rui-Xia XuDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Na-Qiong WuDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Li-Xin JiangDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Jian-Jun LiDivision of Dyslipidemia, State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBoth coronary artery disease (CAD) and diabetes mellitus (DM) are associated with inflammation. However, whether and which leukocytes can predict the presence and extent of CAD in patients with DM has not been investigated. The aim of the present study was to examine the association of leukocyte and its subsets counts with the severity of CAD in patients with DM. METHODS AND

findingsThree hundred and seventy-three diabetic patients who were scheduled for coronary angiography due to typical stable angina pectoris were enrolled in this study. They were classified into the three groups according to tertiles of Gensini score (GS, low group <8, n=143; intermediate group 8~28, n=109; high group >28, n=121). The relationship between the leukocyte and its subsets counts with the severity of CAD were evaluated. The data indicated that there were significant correlations between leukocyte and neutrophil counts with GS (r=0.154 and 0.156, respectively, all P<0.003 for Pearson's correlation). Similarly, area under the receivers operating characteristic curve of leukocyte and neutrophil counts were 0.61 and 0.60 respectively (95%CI: 0.55-0.67, all P=0.001) for predicting high GS. Multivariate logistic regression analysis demonstrated that leukocyte count was an independent predictor for high GS patients with DM (OR=1.20, 95%CI 1.03-1.39, P=0.023) after adjusting for conventional risk factors of CAD.

conclusionsCompared with its subsets, leukocyte count appeared to be an independent predictor for the severity of CAD and the optimal cut-off value for predicting high GS (>28 points) was 5.0 × 10(9) cells/L in diabetic patients.

Indexed as

AdultAgedBiomarkersCoronary Artery DiseaseC-Reactive ProteinDemographyDiabetes ComplicationsFemaleGlycated HemoglobinHumansLeukocyte CountLeukocytesLogistic ModelsMaleMiddle AgedMultivariate AnalysisBiomarkersC-Reactive ProteinGlycated Hemoglobin

Identifiers

PMID24599246
PMCPMC3944194

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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.