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Article: Combined use of serum adiponectin and tumor necrosis factor-alpha receptor 2 levels was comparable to 2-hour post-load glucose in diabetes prediction

TitleCombined use of serum adiponectin and tumor necrosis factor-alpha receptor 2 levels was comparable to 2-hour post-load glucose in diabetes prediction
Authors
KeywordsBlood sampling
Diabetes mellitus
Disease association
Dyslipidemia
Family history
Issue Date2012
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
Plos One, 2012, v. 7 n. 5, article no. e36868 How to Cite?
Abstract
Background: Adipose tissue inflammation and dysregulated adipokine secretion are implicated in obesity-related insulin resistance and type 2 diabetes. We evaluated the use of serum adiponectin, an anti-inflammatory adipokine, and several proinflammatory adipokines, as biomarkers of diabetes risk and whether they add to traditional risk factors in diabetes prediction. Methods: We studied 1300 non-diabetic subjects from the prospective Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS). Serum adiponectin, tumor necrosis factor-alpha receptor 2 (TNF-α R2), interleukin-6 (IL-6), adipocyte-fatty acid binding protein (A-FABP) and high-sensitivity C-reactive protein (hsCRP) were measured in baseline samples. Results: Seventy-six participants developed diabetes over 5.3 years (median). All five biomarkers significantly improved the log-likelihood of diabetes in a clinical diabetes prediction (CDP) model including age, sex, family history of diabetes, smoking, physical activity, hypertension, waist circumference, fasting glucose and dyslipidaemia. In ROC curve analysis, "adiponectin + TNF-α R2" improved the area under ROC curve (AUC) of the CDP model from 0.802 to 0.830 (P = 0.03), rendering its performance comparable to the "CDP + 2-hour post-OGTT glucose" model (AUC = 0.852, P = 0.30). A biomarker risk score, derived from the number of biomarkers predictive of diabetes (low adiponectin, high TNF-α R2), had similar performance when added to the CDP model (AUC = 0.829 [95% CI: 0.808-0.849]). Conclusions: The combined use of serum adiponectin and TNF-α R2 as biomarkers provided added value over traditional risk factors for diabetes prediction in Chinese and could be considered as an alternative to the OGTT. © 2012 Woo et al.
Persistent Identifierhttp://hdl.handle.net/10722/159698
ISSN
2013 Impact Factor: 3.534
2013 SCImago Journal Rankings: 1.724
PubMed Central ID
ISI Accession Number ID
References

 

Author Affiliations
  1. Hong Kong Hospital Authority
  2. The University of Hong Kong
DC FieldValueLanguage
dc.contributor.authorWoo, YCen_HK
dc.contributor.authorTso, AWKen_HK
dc.contributor.authorXu, Aen_HK
dc.contributor.authorLaw, LSCen_HK
dc.contributor.authorFong, CHYen_HK
dc.contributor.authorLam, THen_HK
dc.contributor.authorLo, SVen_HK
dc.contributor.authorWat, NMSen_HK
dc.contributor.authorCheung, BMYen_HK
dc.contributor.authorLam, KSLen_HK
dc.date.accessioned2012-08-16T05:54:09Z-
dc.date.available2012-08-16T05:54:09Z-
dc.date.issued2012en_HK
dc.identifier.citationPlos One, 2012, v. 7 n. 5, article no. e36868en_HK
dc.identifier.issn1932-6203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/159698-
dc.description.abstractBackground: Adipose tissue inflammation and dysregulated adipokine secretion are implicated in obesity-related insulin resistance and type 2 diabetes. We evaluated the use of serum adiponectin, an anti-inflammatory adipokine, and several proinflammatory adipokines, as biomarkers of diabetes risk and whether they add to traditional risk factors in diabetes prediction. Methods: We studied 1300 non-diabetic subjects from the prospective Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS). Serum adiponectin, tumor necrosis factor-alpha receptor 2 (TNF-α R2), interleukin-6 (IL-6), adipocyte-fatty acid binding protein (A-FABP) and high-sensitivity C-reactive protein (hsCRP) were measured in baseline samples. Results: Seventy-six participants developed diabetes over 5.3 years (median). All five biomarkers significantly improved the log-likelihood of diabetes in a clinical diabetes prediction (CDP) model including age, sex, family history of diabetes, smoking, physical activity, hypertension, waist circumference, fasting glucose and dyslipidaemia. In ROC curve analysis, "adiponectin + TNF-α R2" improved the area under ROC curve (AUC) of the CDP model from 0.802 to 0.830 (P = 0.03), rendering its performance comparable to the "CDP + 2-hour post-OGTT glucose" model (AUC = 0.852, P = 0.30). A biomarker risk score, derived from the number of biomarkers predictive of diabetes (low adiponectin, high TNF-α R2), had similar performance when added to the CDP model (AUC = 0.829 [95% CI: 0.808-0.849]). Conclusions: The combined use of serum adiponectin and TNF-α R2 as biomarkers provided added value over traditional risk factors for diabetes prediction in Chinese and could be considered as an alternative to the OGTT. © 2012 Woo et al.en_HK
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.actionen_HK
dc.relation.ispartofPLoS ONEen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBlood sampling-
dc.subjectDiabetes mellitus-
dc.subjectDisease association-
dc.subjectDyslipidemia-
dc.subjectFamily history-
dc.titleCombined use of serum adiponectin and tumor necrosis factor-alpha receptor 2 levels was comparable to 2-hour post-load glucose in diabetes predictionen_HK
dc.typeArticleen_HK
dc.identifier.emailTso, AWK: awk.tso@gmail.comen_HK
dc.identifier.emailXu, A: amxu@hkucc.hku.hken_HK
dc.identifier.emailLam, TH: hrmrlth@hkucc.hku.hken_HK
dc.identifier.emailCheung, BMY: mycheung@hku.hken_HK
dc.identifier.emailLam, KSL: ksllam@hku.hken_HK
dc.identifier.authorityTso, AWK=rp00535en_HK
dc.identifier.authorityXu, A=rp00485en_HK
dc.identifier.authorityLam, TH=rp00326en_HK
dc.identifier.authorityCheung, BMY=rp01321en_HK
dc.identifier.authorityLam, KSL=rp00343en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0036868en_HK
dc.identifier.pmid22615828-
dc.identifier.pmcidPMC3353952-
dc.identifier.scopuseid_2-s2.0-84861210925en_HK
dc.identifier.hkuros205202en_US
dc.identifier.hkuros213321-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861210925&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue5en_HK
dc.identifier.isiWOS:000305341300035-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWoo, YC=55222050900en_HK
dc.identifier.scopusauthoridTso, AWK=6701371436en_HK
dc.identifier.scopusauthoridXu, A=7202655409en_HK
dc.identifier.scopusauthoridLaw, LSC=36994511000en_HK
dc.identifier.scopusauthoridFong, CHY=14033917100en_HK
dc.identifier.scopusauthoridLam, TH=7202522876en_HK
dc.identifier.scopusauthoridLo, SV=8426498400en_HK
dc.identifier.scopusauthoridWat, NMS=6602131754en_HK
dc.identifier.scopusauthoridCheung, BMY=7103294806en_HK
dc.identifier.scopusauthoridLam, KSL=8082870600en_HK

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