Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 30

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Association of erythrocyte parameters with metabolic syndrome in the Pearl River Delta region of China: a cross sectional study

Huang, Ling Ling, Dou, Dong-Mei, Liu, Nan, Wang, Xiao Xiao, Fu, Li-Ying, Wu, Xiao, Wang, Peixi·2018·BMJ Open
DOI 10.1136/bmjopen-2017-019792
Open this paper in CoChat — free account

Abstract

Objective: Increasing studies have reported that erythrocyte parameters, including red blood cells (RBCs), haematocrit (HCT), haemoglobin (Hb) and red blood cell distribution width (RDW), are associated with metabolic syndrome (MetS) in adults worldwide. However, the association, stratified by sex, remains to be elucidated, particularly in the Pearl River Delta region of China. Therefore, our aim was to explore the association of erythrocyte parameters with MetS, stratified by sex, in the Pearl River Delta region of China. Methods: In this cross sectional study, 2161 men and 2511 women were enrolled. MetS was diagnosed using a modified version of the Adult Treatment Panel III criteria. Logistic regression analyses were performed to calculate adjusted ORs of erythrocyte parameters associated with MetS stratified by sex. Results: The prevalence of MetS was higher in women than in men (35.2%vs26.7%). RBC, HCT, Hb and RDW values increased linearly with the number of MetS components from 0 to 5 identified in both men and women. Among men, the ORs of MetS risk increased across the tertiles of Hb (Q2: OR=1.921, 95% CI=1.170 to 3.151; Q3: OR=1.992, 95%CI=1.198 to 3.312). Men in the highest tertiles of RDW had a 2.752-fold increased risk of suffering from MetS compared with those in the reference group. Among women, the ORs of MetS risk also increased across the tertiles of Hb (Q2: OR=1.538, 95%CI=1.008 to 2.348; Q3: OR=1.665, 95%CI=1.075 to 2.578). Women in the highest tertiles of RBC had a 1.718-fold increased risk of experiencing MetS compared with those in the reference group. Conclusions: MetS was more prevalent in women than in men. The association between erythrocyte parameters and MetS differed between the sexes. RBC and Hb were identified as risk factors for MetS in women and Hb and RDW as risk factors in men.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1136/bmjopen-2017-019792

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 46

  1. 10.1161/CIRCULATIONAHA.105.169404 · DOI · Paper page
  2. 10.1161/CIRCULATIONAHA.109.192644 · DOI · Paper page
  3. 10.1016/j.jacc.2010.05.034 · DOI · Paper page
  4. 10.1089/met.2014.0055 · DOI
  5. 10.1177/2047487314549744 · DOI
  6. Lovre, 2015, Trends in prevalence of the metabolic syndrome, JAMA, 314, 950, 10.1001/jama.2015.8625 · DOI
  7. 10.1038/sj.ejcn.1602384 · DOI
  8. 10.2337/dc12-2562 · DOI
  9. Ellinger, 2006, Relation between insulin resistance and hematological parameters in a Brazilian sample, Arq Bras Endocrinol Metabol, 50, 114, 10.1590/S0004-27302006000100016 · DOI
  10. 10.1016/j.diabres.2005.01.005 · DOI
  11. Tabara, 2013, Association of hematological parameters with insulin resistance, insulin sensitivity, and asymptomaticcerebrovascular damage: The J-SHIP Toon Health Study, Clin Hemorheol Microcirc, 55, 297, 10.3233/CH-2012-1634 · DOI
  12. Lippi, 2009, Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients, Arch Pathol Lab Med, 133, 628, 10.5858/133.4.628 · DOI
  13. Kawamoto, 2013, Hematological parameters are associated with metabolic syndrome in Japanese community-dwelling persons, Endocrine, 43, 334, 10.1007/s12020-012-9662-7 · DOI
  14. 10.1186/1471-2458-13-1 · DOI · Paper page
  15. Nebeck, 2012, Hematological parameters and metabolic syndrome: findings from an occupational cohort in Ethiopia, Diabetes Metab Syndr, 6, 22, 10.1016/j.dsx.2012.05.009 · DOI
  16. Hashimoto, 2015, Hemoglobin concentration and incident metabolic syndrome: a population-based large-scale cohort study, Endocrine, 50, 390, 10.1007/s12020-015-0587-9 · DOI
  17. Vayá, 2011, Association between high red blood cell distribution width and metabolic syndrome. Influence of abdominal obesity, Clin Hemorheol Microcirc, 47, 75, 10.3233/CH-2010-1385 · DOI
  18. 10.1186/1471-2458-10-1 · DOI
  19. Kozan, 2007, Prevalence of the metabolic syndrome among Turkish adults, Eur J Clin Nutr, 61, 548, 10.1038/sj.ejcn.1602554 · DOI
  20. Tao, 2014, Association of hematological parameters with metabolic syndrome in Beijing adult population: a longitudinal study, Endocrine, 46, 485, 10.1007/s12020-013-0067-z · DOI
  21. 10.1007/s00038-012-0347-z · DOI
  22. 10.1016/S1056-8727(04)00003-0 · DOI
  23. 10.1016/S0168-8227(03)00059-7 · DOI
  24. Wang, 2016, Study of the relationship between female metabollic syndrome and its related blood indexes in Guangzhou, Medical Innovation of China, 13, 61
  25. 10.1002/stem.5530120309 · DOI
  26. 10.1210/jcem-55-6-1209 · DOI
  27. 10.1046/j.1365-2141.2000.02047.x · DOI
  28. 10.1172/JCI112079 · DOI
  29. 10.2337/dc09-0420 · DOI
  30. 10.2337/diabetes.51.4.1000 · DOI
  31. 10.1186/1476-511X-10-82 · DOI
  32. 10.3945/ajcn.113.061218 · DOI
  33. 10.1016/j.amjcard.2012.04.027 · DOI
  34. Lohsoonthorn, 2007, Association of hematological parameters with clustered components of metabolic syndrome among professional and office workers in Bangkok, Thailand, Diabetes Metab Syndr, 1, 143, 10.1016/j.dsx.2007.05.002 · DOI
  35. 10.1161/HYPERTENSIONAHA.108.110460 · DOI
  36. Zinchuk, 2004, Blood oxygen transport and endothelial dysfunction in patients with arterial hypertension, Clin Physiol Funct Imaging, 24, 205, 10.1111/j.1475-097X.2004.00549.x · DOI
  37. Wei, 2013, The association between metabolic syndrome and vascular endothelial dysfunction in adolescents, Exp Ther Med, 5, 1663, 10.3892/etm.2013.1055 · DOI
  38. Tsuji, 2011, [Vascular endothelial dysfunction as a mechanistic factor for metabolic syndrome], Nihon Rinsho, 69 Suppl 1, 295
  39. Kutlu, 2009, Relationship between hemoglobin and CD40 ligand in prediabetes, Clin Invest Med, 32, 244, 10.25011/cim.v32i6.10659 · DOI
  40. 10.1160/TH09-07-0463 · DOI
  41. Kawamoto, 2011, Hemoglobin is associated with serum high molecular weight adiponectin in Japanese community-dwelling persons, J Atheroscler Thromb, 18, 182, 10.5551/jat.6379 · DOI
  42. Ben Ali, 2012, Adiponectin and metabolic syndrome in a Tunisian population, Inflammation, 35, 828, 10.1007/s10753-011-9383-8 · DOI
  43. Laufer Perl, 2015, High red blood cell distribution width is associated with the metabolic syndrome, Clin Hemorheol Microcirc, 63, 1
  44. Sanchez-Chaparro, 2010, Higher red blood cell distribution width is associated with the metabolic syndrome, Diabetes Care, 33, 10.2337/dc09-1707 · DOI
  45. Perna, 2014, PP249-SUN: The role of red blood cell distribution width in metabolic syndrome. A cross-sectional study in elderly, Clin Nutr, 33, S112, 10.1016/S0261-5614(14)50290-7 · DOI
  46. 10.1191/0267659105pf793oa · DOI

Cited by

Metadata

Authors
Huang, L. L., Dou, D.-M., Liu, N., Wang, X. X., Fu, L.-Y., Wu, X., & Wang, P.
Venue
BMJ Open
Publisher
BMJ
Published
2018-01
Volume
8
Issue
1
Pages
e019792
ISSN
2044-6055
DOI
10.1136/bmjopen-2017-019792
Cited by
30
References
2
Language
en
Funders
  • the Science and Technology Program of Guangzhou
  • the National Science Foundation of China
  • the Guangzhou 121 Talents Program

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.