Literature DB >> 8187472

Risk calculation of type 2 diabetes.

T Haas1, S Svacina, J Pav, R Hovorka, P Sucharda, J Sonka.   

Abstract

The paper presents an analysis of the risk of developing Type 2 diabetes according to family history and anthropometric variables. The age of diabetes onset was analysed in 2024 diabetics. We obtained several groups according to family history. In each group taken separately, the data describing the cumulative percentage of diabetes onset was fitted by logistic curve F(x) = p1/(1 + p2*p3((x/10)-p4)). Comparing these curves we see that cumulative age-dependent risk increases from the group of randomly chosen persons through the group of first degree relatives to the children of diabetics. The highest risk of diabetes onset is determined by the curve representing the group of known diabetics. Another analysis was performed in a different group of 390 obese subjects (34 diabetics among them). Male diabetics had significantly higher body mass index (BMI) and weight than male non-diabetics. Female diabetics showed significantly higher weight, body mass index, waist to hip ratio (WHR) and age than female non-diabetics. Elimination of factors with randomization and matching showed a complicated relationship between diabetes, age and anthropometric variables. Using stepwise logistic regression we obtained the model for prediction of diabetes risk based on age, BMI, WHR: probability of diabetes = exp(u)/(1 + exp(u)), where u = -13.9 + 0.05431*age + 6.789*WHR + 0.07881*BMI for obese women, u = -11.84 + 10.01*WHR for obese men. In conclusion, genetic factors are the most important and can be exactly quantified in Type 2 diabetes. The importance of anthropometric variables for prediction of diabetes risk is also presented.

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Year:  1994        PMID: 8187472     DOI: 10.1016/0169-2607(94)90061-2

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  3 in total

1.  The serum C Peptide levels among the offsprings of the people with type 2 diabetes.

Authors:  Gaurav Kumar; Jayballabh Kumar; Farhan Ahmad Khan; Devesh Kumar; Kiran Malik
Journal:  J Clin Diagn Res       Date:  2013-02-15

2.  A classification method of normal and overweight females based on facial features for automated medical applications.

Authors:  Bum Ju Lee; Jun-Hyeong Do; Jong Yeol Kim
Journal:  J Biomed Biotechnol       Date:  2012-08-05

3.  A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study.

Authors:  Bum Ju Lee; Boncho Ku; Jun-Su Jang; Jong Yeol Kim
Journal:  Evid Based Complement Alternat Med       Date:  2013-03-14       Impact factor: 2.629

  3 in total

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