Barbara Di Camillo1, Liisa Hakaste2,3, Francesco Sambo1, Rafael Gabriel4,5, Jasmina Kravic6, Bo Isomaa3, Jaakko Tuomilehto5,7,8,9, Margarita Alonso4,5, Enrico Longato1, Andrea Facchinetti1, Leif C Groop6,10, Claudio Cobelli1, Tiinamaija Tuomi11,3,10. 1. Department of Information EngineeringUniversity of Padova, Padova, Italy. 2. EndocrinologyAbdominal Centre, University of Helsinki and Helsinki University Hospital, Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland. 3. Folkhälsan Research CenterHelsinki, Finland. 4. Department of International HealthNational School of Public Health, Instituto de Salud Carlos III, Madrid, Spain. 5. Asociación Española Para el Desarrollo de la Epidemiología Clínica (AEDEC)Madrid, Spain. 6. Lund University Diabetes CentreDepartment of Clinical Sciences Malmö, Lund University, Skåne University Hospital, Malmö, Sweden. 7. Dasman Diabetes InstituteDasman, Kuwait City, Kuwait. 8. Department of Neuroscience and Preventive MedicineDanube-University Krems, Krems, Austria. 9. Saudi Diabetes Research GroupKing Abdulaziz University, Jeddah, Saudi Arabia. 10. Institute for Molecular Medicine Finland (FIMM)University of Helsinki, Helsinki, Finland. 11. EndocrinologyAbdominal Centre, University of Helsinki and Helsinki University Hospital, Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland tiinamaija.tuomi@hus.fi.
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 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.
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.
Authors: Kadri Haljas; Liisa Hakaste; Jari Lahti; Bo Isomaa; Leif Groop; Tiinamaija Tuomi; Katri Räikkönen Journal: Ann Med Date: 2019-02-14 Impact factor: 4.709
Authors: Martina Vettoretti; Enrico Longato; Alessandro Zandonà; Yan Li; José Antonio Pagán; David Siscovick; Mercedes R Carnethon; Alain G Bertoni; Andrea Facchinetti; Barbara Di Camillo Journal: BMJ Open Diabetes Res Care Date: 2020-07
Authors: Niko S Wasenius; Bo A Isomaa; Bjarne Östman; Johan Söderström; Björn Forsén; Kaj Lahti; Liisa Hakaste; Johan G Eriksson; Leif Groop; Ola Hansson; Tiinamaija Tuomi Journal: BMJ Open Diabetes Res Care Date: 2020-11