Cargando…
Development and validation of a diabetes risk score among two populations
The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previ...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833146/ https://www.ncbi.nlm.nih.gov/pubmed/33493190 http://dx.doi.org/10.1371/journal.pone.0245716 |
_version_ | 1783641997740867584 |
---|---|
author | Schwatka, Natalie V. Smith, Derek E. Golden, Ashley Tran, Molly Newman, Lee S. Cragle, Donna |
author_facet | Schwatka, Natalie V. Smith, Derek E. Golden, Ashley Tran, Molly Newman, Lee S. Cragle, Donna |
author_sort | Schwatka, Natalie V. |
collection | PubMed |
description | The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previously undetected/incipient chronic illness. Study population 2 (n = 3,293) was part of a Colorado worksite wellness program health risk assessment. We assessed the relationship between a continuous diabetes risk score at baseline and development of diabetes in the future using logistic regression. Receiver operating curves and sensitivity/specificity of the models were calculated. Across both study populations, we observed that participants with diabetes at follow-up had higher diabetes risk scores at baseline than participants who did not have diabetes at follow-up. On average, the odds ratio of developing diabetes in the future was 1.38 (95% CI: 1.26–1.50, p < 0.0001) for study population 1 and 1.68 (95% CI: 1.45–1.95, p-value < 0.0001) for study population 2. These findings indicate that the diabetes risk score may be generalizable to diverse individuals, and thus potentially a population level diabetes screening tool. Minimally-invasive diabetes risk scores can aid in the identification of sub-populations of individuals at risk for diabetes. |
format | Online Article Text |
id | pubmed-7833146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78331462021-01-26 Development and validation of a diabetes risk score among two populations Schwatka, Natalie V. Smith, Derek E. Golden, Ashley Tran, Molly Newman, Lee S. Cragle, Donna PLoS One Research Article The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previously undetected/incipient chronic illness. Study population 2 (n = 3,293) was part of a Colorado worksite wellness program health risk assessment. We assessed the relationship between a continuous diabetes risk score at baseline and development of diabetes in the future using logistic regression. Receiver operating curves and sensitivity/specificity of the models were calculated. Across both study populations, we observed that participants with diabetes at follow-up had higher diabetes risk scores at baseline than participants who did not have diabetes at follow-up. On average, the odds ratio of developing diabetes in the future was 1.38 (95% CI: 1.26–1.50, p < 0.0001) for study population 1 and 1.68 (95% CI: 1.45–1.95, p-value < 0.0001) for study population 2. These findings indicate that the diabetes risk score may be generalizable to diverse individuals, and thus potentially a population level diabetes screening tool. Minimally-invasive diabetes risk scores can aid in the identification of sub-populations of individuals at risk for diabetes. Public Library of Science 2021-01-25 /pmc/articles/PMC7833146/ /pubmed/33493190 http://dx.doi.org/10.1371/journal.pone.0245716 Text en © 2021 Schwatka et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Schwatka, Natalie V. Smith, Derek E. Golden, Ashley Tran, Molly Newman, Lee S. Cragle, Donna Development and validation of a diabetes risk score among two populations |
title | Development and validation of a diabetes risk score among two populations |
title_full | Development and validation of a diabetes risk score among two populations |
title_fullStr | Development and validation of a diabetes risk score among two populations |
title_full_unstemmed | Development and validation of a diabetes risk score among two populations |
title_short | Development and validation of a diabetes risk score among two populations |
title_sort | development and validation of a diabetes risk score among two populations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833146/ https://www.ncbi.nlm.nih.gov/pubmed/33493190 http://dx.doi.org/10.1371/journal.pone.0245716 |
work_keys_str_mv | AT schwatkanataliev developmentandvalidationofadiabetesriskscoreamongtwopopulations AT smithdereke developmentandvalidationofadiabetesriskscoreamongtwopopulations AT goldenashley developmentandvalidationofadiabetesriskscoreamongtwopopulations AT tranmolly developmentandvalidationofadiabetesriskscoreamongtwopopulations AT newmanlees developmentandvalidationofadiabetesriskscoreamongtwopopulations AT cragledonna developmentandvalidationofadiabetesriskscoreamongtwopopulations |