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HAPT2D : high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability

Di Camillo, Barbara; Hakaste, Liisa; Sambo, Francesco; Gabriel, Rafael; Kravic, Jasmina LU ; Isomaa, Bo; Tuomilehto, Jaakko; Alonso, Margarita; Longato, Enrico and Facchinetti, Andrea, et al. (2018) In European journal of endocrinology 178(4). p.331-341
Abstract

OBJECTIVE: Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information.

RESEARCH DESIGN AND METHODS: We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added... (More)

OBJECTIVE: Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information.

RESEARCH DESIGN AND METHODS: We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores.

RESULTS: The three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive.

CONCLUSIONS: Our models provide an estimation of patient's risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.

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Contribution to journal
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published
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European journal of endocrinology
volume
178
issue
4
pages
11 pages
publisher
Society of the European Journal of Endocrinology
external identifiers
  • scopus:85044713975
ISSN
1479-683X
DOI
10.1530/EJE-17-0921
language
English
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yes
id
c8b6d972-8026-4d3e-a9ca-a8deb52bec8f
date added to LUP
2018-04-10 12:10:26
date last changed
2018-10-16 04:17:25
@article{c8b6d972-8026-4d3e-a9ca-a8deb52bec8f,
  abstract     = {<p>OBJECTIVE: Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information.</p><p>RESEARCH DESIGN AND METHODS: We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores.</p><p>RESULTS: The three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive.</p><p>CONCLUSIONS: Our models provide an estimation of patient's risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.</p>},
  author       = {Di Camillo, Barbara and Hakaste, Liisa and Sambo, Francesco and Gabriel, Rafael and Kravic, Jasmina and Isomaa, Bo and Tuomilehto, Jaakko and Alonso, Margarita and Longato, Enrico and Facchinetti, Andrea and Groop, Leif C. and Cobelli, Claudio and Tuomi, Tiinamaija},
  issn         = {1479-683X},
  language     = {eng},
  month        = {04},
  number       = {4},
  pages        = {331--341},
  publisher    = {Society of the European Journal of Endocrinology},
  series       = {European journal of endocrinology},
  title        = {HAPT2D : high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability},
  url          = {http://dx.doi.org/10.1530/EJE-17-0921},
  volume       = {178},
  year         = {2018},
}