Literature DB >> 23393174

Development of a screening score for undiagnosed diabetes and its application in estimating absolute risk of future type 2 diabetes in Japan: Toranomon Hospital Health Management Center Study 10 (TOPICS 10).

Yoriko Heianza1, Yasuji Arase, Kazumi Saito, Shiun Dong Hsieh, Hiroshi Tsuji, Satoru Kodama, Shiro Tanaka, Yasuo Ohashi, Hitoshi Shimano, Nobuhiro Yamada, Shigeko Hara, Hirohito Sone.   

Abstract

OBJECTIVE: The objective of the study was to develop a screening score for undiagnosed diabetes by eliciting information on noninvasive clinical markers and to assess its effectiveness for identifying the presence of diabetes and predicting future diabetes. DESIGN, SETTING, AND PARTICIPANTS: A screening score was cross-sectionally developed for 33 335 Japanese individuals aged 18-88 years without known diabetes who underwent a health examination. We validated its utility and compared it with existing screening tools in an independent population (n = 7477). After initial assessment of the instrument, 7332 nondiabetic individuals were followed up for a mean 4.0 years.
RESULTS: Prevalence of undiagnosed diabetes (fasting plasma glucose ≥ 7.0 mmol/L or glycated hemoglobin ≥ 6.5%) was 2.9% (n = 965). Diabetes score included age, sex, family history of diabetes, current smoking habit, body mass index, and hypertension with an area under the receiver-operating characteristics curve of 0.771. Screening with 8 or more points yielded a sensitivity of 72.7% and a specificity of 68.1%. In the validation cohort, the area under the receiver-operating characteristics curve was 0.806. The developed score with 8 or more points had better positive predictive value (9.6%) and positive likelihood ratio (2.52) compared with existing tools (positive predictive value, from 6.9% to 9.4%; positive likelihood ratio, from 1.77 to 2.46) in which each tool's highest combination of sensitivity and specificity was observed. The 4-year cumulative risk of developing diabetes gradually escalated in association with higher screening scores at the initial examination.
CONCLUSIONS: Our algorithm could serve as a self-assessment tool for undiagnosed diabetic patients needing timely medical care and as a prognostic tool for individuals without present diabetes who must be closely followed up to prevent future diabetes.

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Year:  2013        PMID: 23393174     DOI: 10.1210/jc.2012-3092

Source DB:  PubMed          Journal:  J Clin Endocrinol Metab        ISSN: 0021-972X            Impact factor:   5.958


  10 in total

1.  Developing a Screening Algorithm for Type II Diabetes Mellitus in the Resource-Limited Setting of Rural Tanzania.

Authors:  Caroline West; David Ploth; Virginia Fonner; Jessie Mbwambo; Francis Fredrick; Michael Sweat
Journal:  Am J Med Sci       Date:  2016-02-10       Impact factor: 2.378

2.  Factors associated with untreated diabetes: analysis of data from 20,496 participants in the Japanese National Health and Nutrition Survey.

Authors:  Maki Goto; Atsushi Goto; Nayu Ikeda; Hiroyuki Noda; Kenji Shibuya; Mitsuhiko Noda
Journal:  PLoS One       Date:  2015-03-10       Impact factor: 3.240

3.  Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review.

Authors:  Katya L Masconi; Tandi E Matsha; Justin B Echouffo-Tcheugui; Rajiv T Erasmus; Andre P Kengne
Journal:  EPMA J       Date:  2015-03-11       Impact factor: 6.543

4.  Elevated HbA1c levels in individuals not diagnosed with type 2 diabetes in Qatar: a pilot study.

Authors:  Marjonneke J Mook-Kanamori; Mohammed M El-Din Selim; Ahmed H Takiddin; Khoulood A S Al-Mahmoud; Hala Al-Homsi; Cindy McKeon; Wadha A Al Muftah; Sara Abdul Kader; Dennis O Mook-Kanamori; Karsten Suhre
Journal:  Qatar Med J       Date:  2014-12-09

5.  Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study.

Authors:  Chang Ho Ahn; Ji Won Yoon; Seokyung Hahn; Min Kyong Moon; Kyong Soo Park; Young Min Cho
Journal:  PLoS One       Date:  2016-05-23       Impact factor: 3.240

6.  Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study.

Authors:  Alex J Mitchell; Davy Vancampfort; Peter Manu; Christoph U Correll; Martien Wampers; Ruud van Winkel; Weiping Yu; Marc De Hert
Journal:  PLoS One       Date:  2019-09-12       Impact factor: 3.240

7.  Yield, NNS and prevalence of screening for DM and hypertension among pulmonary tuberculosis index cases and contacts through single time screening: A contact tracing-based study.

Authors:  Shengqiong Guo; Virasakdi Chongsuvivatwong; Min Guo; Shiguang Lei; Jinlan Li; Huijuan Chen; Jiangping Zhang; Wen Wang; Cui Cai
Journal:  PLoS One       Date:  2022-01-28       Impact factor: 3.240

8.  Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.

Authors:  Samaneh Asgari; Davood Khalili; Farhad Hosseinpanah; Farzad Hadaegh
Journal:  Int J Endocrinol Metab       Date:  2021-03-22

9.  DS21, a new noninvasive technology, is effective and safe for screening for prediabetes and diabetes in Chinese population.

Authors:  Xiaopeng Zhu; Jing Tang; Huandong Lin; Xinxia Chang; Mingfeng Xia; Liu Wang; Hongmei Yan; Hua Bian; Xin Gao
Journal:  Biomed Eng Online       Date:  2020-10-14       Impact factor: 2.819

10.  A cross-sectional study clarifying profiles of patients with diabetes who discontinued pharmacotherapy: reasons and consequences.

Authors:  Yoshiko Tominaga; Donald E Morisky; Mayumi Mochizuki
Journal:  BMC Endocr Disord       Date:  2021-06-14       Impact factor: 2.763

  10 in total

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