An innovative prognostic model for predicting diabetes risk in the Thai population
- Publication Type:
- Journal Article
- Citation:
- Diabetes Research and Clinical Practice, 2011, 94 (2), pp. 193 - 198
- Issue Date:
- 2011-11-01
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2013005852OK.pdf | 339.5 kB | Adobe PDF |
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Pongchaiyakul, C | en_US |
dc.contributor.author | Kotruchin, P | en_US |
dc.contributor.author | Wanothayaroj, E | en_US |
dc.contributor.author |
Nguyen, TV https://orcid.org/0000-0002-3246-6281 |
en_US |
dc.date.available | 2011-07-14 | en_US |
dc.date.issued | 2011-11-01 | en_US |
dc.identifier.citation | Diabetes Research and Clinical Practice, 2011, 94 (2), pp. 193 - 198 | en_US |
dc.identifier.issn | 0168-8227 | en_US |
dc.identifier.uri | http://hdl.handle.net/10453/28782 | |
dc.description.abstract | Objective: To estimate the prevalence and type 2 diabetes, and to develop a prognostic model for identifying individuals at high risk of undiagnosed type 2 diabetes. Research design and methods: The study was designed as a cross-sectional investigation with 4314 participants of Thai background, aged between 15 and 85. years (mean age: 48). Fasting plasma glucose was initially measured, and repeated if the first measurement was more than 126. mg/dl. Type 2 diabetes was diagnosed using the World Health Organization's criteria. Logistic regression model was used to develop prognostic models for men and women separately. The prognostic performance of the model was assessed by the area under the receiver operating characteristic curve (AUC) and a nomogram was constructed from the logistic regression model. Results: The overall prevalence of type 2 diabetes was 7.4% (n= 125/1693) in men and 3.4% (n= 98/2621) in women. In either gender, the prevalence increased with age and body mass index (BMI). Gender, age, BMI and systolic blood pressure (SBP) were independently associated with type 2 diabetes risk. Based on the estimated parameters of model, a nomogram was constructed for predicting diabetes separated by gender. The AUC for the model with 3 factors was 0.75. Conclusions: These data suggest that the combination of age, BMI and systolic blood pressure could help identify Thai individuals at high risk of undiagnosed diabetes. © 2011. | en_US |
dc.relation.ispartof | Diabetes Research and Clinical Practice | en_US |
dc.relation.isbasedon | 10.1016/j.diabres.2011.07.019 | en_US |
dc.subject.classification | Endocrinology & Metabolism | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Diabetes Mellitus, Type 2 | en_US |
dc.subject.mesh | Blood Glucose | en_US |
dc.subject.mesh | Biological Markers | en_US |
dc.subject.mesh | Body Mass Index | en_US |
dc.subject.mesh | Prognosis | en_US |
dc.subject.mesh | Prevalence | en_US |
dc.subject.mesh | Area Under Curve | en_US |
dc.subject.mesh | Models, Statistical | en_US |
dc.subject.mesh | Logistic Models | en_US |
dc.subject.mesh | Nomograms | en_US |
dc.subject.mesh | Bayes Theorem | en_US |
dc.subject.mesh | Odds Ratio | en_US |
dc.subject.mesh | Risk Assessment | en_US |
dc.subject.mesh | Risk Factors | en_US |
dc.subject.mesh | Cross-Sectional Studies | en_US |
dc.subject.mesh | Age Factors | en_US |
dc.subject.mesh | Blood Pressure | en_US |
dc.subject.mesh | Adolescent | en_US |
dc.subject.mesh | Adult | en_US |
dc.subject.mesh | Aged | en_US |
dc.subject.mesh | Aged, 80 and over | en_US |
dc.subject.mesh | Middle Aged | en_US |
dc.subject.mesh | Asian Continental Ancestry Group | en_US |
dc.subject.mesh | Rural Health | en_US |
dc.subject.mesh | Thailand | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Male | en_US |
dc.subject.mesh | Young Adult | en_US |
dc.subject.mesh | Biomarkers | en_US |
dc.title | An innovative prognostic model for predicting diabetes risk in the Thai population | en_US |
dc.type | Journal Article | |
utslib.citation.volume | 2 | en_US |
utslib.citation.volume | 94 | en_US |
utslib.for | 1103 Clinical Sciences | en_US |
utslib.for | 1117 Public Health and Health Services | en_US |
utslib.for | 1701 Psychology | en_US |
pubs.embargo.period | Not known | en_US |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering | |
pubs.organisational-group | /University of Technology Sydney/Strength - CHT - Health Technologies | |
utslib.copyright.status | closed_access | |
pubs.issue | 2 | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 94 | en_US |
Abstract:
Objective: To estimate the prevalence and type 2 diabetes, and to develop a prognostic model for identifying individuals at high risk of undiagnosed type 2 diabetes. Research design and methods: The study was designed as a cross-sectional investigation with 4314 participants of Thai background, aged between 15 and 85. years (mean age: 48). Fasting plasma glucose was initially measured, and repeated if the first measurement was more than 126. mg/dl. Type 2 diabetes was diagnosed using the World Health Organization's criteria. Logistic regression model was used to develop prognostic models for men and women separately. The prognostic performance of the model was assessed by the area under the receiver operating characteristic curve (AUC) and a nomogram was constructed from the logistic regression model. Results: The overall prevalence of type 2 diabetes was 7.4% (n= 125/1693) in men and 3.4% (n= 98/2621) in women. In either gender, the prevalence increased with age and body mass index (BMI). Gender, age, BMI and systolic blood pressure (SBP) were independently associated with type 2 diabetes risk. Based on the estimated parameters of model, a nomogram was constructed for predicting diabetes separated by gender. The AUC for the model with 3 factors was 0.75. Conclusions: These data suggest that the combination of age, BMI and systolic blood pressure could help identify Thai individuals at high risk of undiagnosed diabetes. © 2011.
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