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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
  • Barbara Di Camillo
  • Liisa Hakaste
  • Francesco Sambo
  • Rafael Gabriel
  • Jasmina Kravic
  • Bo Isomaa
  • Jaakko Tuomilehto
  • Margarita Alonso
  • Enrico Longato
  • Andrea Facchinetti
  • Leif C. Groop
  • Claudio Cobelli
  • Tiinamaija Tuomi
Enheter & grupper
Externa organisationer
  • University of Padova
  • Folkhälsans forskningscentrum
  • King Abdulaziz University
  • University of Helsinki
  • Helsinki University Central Hospital
  • Asociación Española Para el Desarrollo de la Epidemiología Clínica
  • Carlos III Health Institute
  • Dasman Diabetes Institute
  • Danube University Krems
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Endokrinologi och diabetes
Originalspråkengelska
Sidor (från-till)331-341
Antal sidor11
TidskriftEuropean journal of endocrinology
Volym178
Utgivningsnummer4
StatusPublished - 2018 apr 1
PublikationskategoriForskning
Peer review utfördJa