Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards

  1. Yochai Edlitz
  2. Eran Segal  Is a corresponding author
  1. Weizmann Institute of Science, Israel

Abstract

Background: Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation (IDF). Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

Methods: In this study, we analyzed data from 44,709 non-diabetic U.K. Biobank participants aged 40-69, predicting the risk of T2D onset within a selected timeframe (mean of 7.3 years with a standard deviation of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one non-laboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes sub cohorts, and compared the results to the results of the general cohort. We established the non-laboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip Ratio (WHR), and Body-Mass Index (BMI). For the laboratory model, we used age and sex together with four common blood tests: HDL (high-density lipoprotein), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

Results: The non-laboratory scorecard model achieved an Area Under the Receiver Operating Curve (auROC) of 0.81 (0.77-0.84 95% Confidence Interval (CI)) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (5-66 95% CI). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood-test model based on age, sex and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (0.85-0.90 95% CI) and a deciles' OR of x48 (12-109 95% CI). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4%-7%); 3% (2-4%); 10% (8-12%) and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (0.74-0.75 95% CI). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the Anthropometry models.

Conclusions: The four blood tests and anthropometric models outperformed the commonly used non-laboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

Funding: No Funders. The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Data availability

All data that we used to develop the models in this research is available through the UK Biobank database. The external validation cohort is from "Clalit healthcare".The two databases can be accessed upon specific requests and approval as described below.UKBiobank - The UK Biobank data is Available from UK Biobank subject to standard procedures (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). The UK Biobank resource is open to all bona fide researchers at bona fide research institutes to conduct health-related research in the public interest. UK Biobank welcomes applications from academia and commercial institutes.Clalit - The data that support the findings of the external Clalit cohort originate from Clalit Health Services (http://clalitresearch.org/about-us/our-data/). Due to restrictions, these data can be accessed only by request to the authors and/or Clalit Health Services. Requests for access to all or parts of the Clalit datasets should be addressed to Clalit Healthcare Services via the Clalit Research Institute (http://clalitresearch.org/contact/). The Clalit Data Access committee will consider requests given the Clalit data-sharing policy.Source code for analysis is available at https://github.com/yochaiedlitz/T2DM_UKB_predictions

The following previously published data sets were used

Article and author information

Author details

  1. Yochai Edlitz

    Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Yavne, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7733-3995
  2. Eran Segal

    Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Yavne, Israel
    For correspondence
    eran.segal@weizmann.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6859-1164

Funding

Feinberg Graduate School, Weizmann Institute of Science

  • Eran Segal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Nicola Napoli, Campus Bio-Medico University of Rome, Italy

Publication history

  1. Received: July 1, 2021
  2. Accepted: May 26, 2022
  3. Accepted Manuscript published: June 22, 2022 (version 1)

Copyright

© 2022, Edlitz & Segal

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yochai Edlitz
  2. Eran Segal
(2022)
Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards
eLife 11:e71862.
https://doi.org/10.7554/eLife.71862

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