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Article: Simple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus

TitleSimple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus
Authors
Issue Date2016
PublisherWiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1753-0393
Citation
Journal of Diabetes, 2016, v. 8 n. 3, p. 414-421 How to Cite?
AbstractBackground: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise and uncontrolled blood pressure; the laboratory-based risk algorithm, with score ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P=0.229 and P=0.483, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/209798
ISSN
2015 Impact Factor: 2.5
2015 SCImago Journal Rankings: 0.762

 

DC FieldValueLanguage
dc.contributor.authorWong, CKH-
dc.contributor.authorSiu, SC-
dc.contributor.authorWan, EYF-
dc.contributor.authorJiao, FF-
dc.contributor.authorYu, EYT-
dc.contributor.authorFung, CSC-
dc.contributor.authorWong, KW-
dc.contributor.authorLeung, AYM-
dc.contributor.authorLam, CLK-
dc.date.accessioned2015-05-18T03:24:16Z-
dc.date.available2015-05-18T03:24:16Z-
dc.date.issued2016-
dc.identifier.citationJournal of Diabetes, 2016, v. 8 n. 3, p. 414-421-
dc.identifier.issn1753-0393-
dc.identifier.urihttp://hdl.handle.net/10722/209798-
dc.description.abstractBackground: To develop a simple nomogram which can be used to predict the risk of diabetes mellitus (DM) in asymptomatic non-diabetic general population based on non-laboratory-based and laboratory-based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habit and family history of DM were collected from Chinese non-diabetic subjects aged 18-70. Logistic regression analysis was performed on the data of a random sample of 2518 subjects to construct non-laboratory-based and laboratory-based risk assessment algorithms for the detection of undiagnosed DM; both algorithms were validated on the data of the remaining sample (n=839). Hosmer-Lemeshow χ2 statistic and area under the receiver-operating characteristic curve (AUC) were employed to assess the calibration and discrimination of the different DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose≥7.0mmol/L or 2-hour post-load plasma glucose≥11.1mmol/L after oral glucose tolerance test. The non-laboratory-based risk algorithm, with score ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise and uncontrolled blood pressure; the laboratory-based risk algorithm, with score ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P=0.229 and P=0.483, respectively) and discrimination (AUC: 0.709 and 0.711, respectively) for the detection of undiagnosed DM. The optimal cutoff point on the receiver-operating characteristic curve was 18 for the detection of undiagnosed DM in both algorithms. Conclusions: Simple-to-use nomogram for detecting undiagnosed DM has been developed using the validated non-laboratory-based and laboratory-based risk algorithms.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=1753-0393-
dc.relation.ispartofJournal of Diabetes-
dc.rightsThis is the accepted version of the following article: Journal of Diabetes, 2016, v. 8 n. 3, p. 414-421, which has been published in final form athttp://onlinelibrary.wiley.com/doi/10.1111/1753-0407.12310/abstract-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSimple Non-laboratory- and Laboratory-based Risk Assessment Algorithms and Nomogram for Detecting Undiagnosed Diabetes Mellitus-
dc.typeArticle-
dc.identifier.emailWong, CKH: carlosho@hku.hk-
dc.identifier.emailWan, EYF: yfwan@hku.hk-
dc.identifier.emailYu, EYT: ytyu@hku.hk-
dc.identifier.emailFung, CSC: cfsc@hku.hk-
dc.identifier.emailLeung, AYM: angleung@hku.hk-
dc.identifier.emailLam, CLK: clklam@hku.hk-
dc.identifier.authorityWong, CKH=rp01931-
dc.identifier.authorityYu, EYT=rp01693-
dc.identifier.authorityFung, CSC=rp01330-
dc.identifier.authorityLeung, AYM=rp00405-
dc.identifier.authorityLam, CLK=rp00350-
dc.description.naturepostprint-
dc.identifier.doi10.1111/1753-0407.12310-
dc.identifier.pmid25952330-
dc.identifier.scopuseid_2-s2.0-84933556241-
dc.identifier.hkuros243284-
dc.identifier.volume8-
dc.identifier.issue3-
dc.identifier.spage414-
dc.identifier.epage421-
dc.publisher.placeUnited States-

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