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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0245716 |
| _version_ | 1828919139698212864 |
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| author | Natalie V Schwatka Derek E Smith Ashley Golden Molly Tran Lee S Newman Donna Cragle |
| author_facet | Natalie V Schwatka Derek E Smith Ashley Golden Molly Tran Lee S Newman Donna Cragle |
| author_sort | Natalie V Schwatka |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T21:22:08Z |
| format | Article |
| id | doaj.art-220a2583e8b14255ac15d631daad3b9f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T21:22:08Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-220a2583e8b14255ac15d631daad3b9f2022-12-21T23:31:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01161e024571610.1371/journal.pone.0245716Development and validation of a diabetes risk score among two populations.Natalie V SchwatkaDerek E SmithAshley GoldenMolly TranLee S NewmanDonna CragleThe 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.https://doi.org/10.1371/journal.pone.0245716 |
| spellingShingle | Natalie V Schwatka Derek E Smith Ashley Golden Molly Tran Lee S Newman Donna Cragle Development and validation of a diabetes risk score among two populations. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0245716 |
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