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Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables
Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clus...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918294/ https://www.ncbi.nlm.nih.gov/pubmed/36769457 http://dx.doi.org/10.3390/jcm12030810 |
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author | Ito, Ryoma Mizushiri, Satoru Nishiya, Yuki Ono, Shoma Tamura, Ayumi Hamaura, Kiho Terada, Akihide Tanabe, Jutaro Yanagimachi, Miyuki Wai, Kyi Mar Kudo, Yutaro Ihara, Kazushige Takahashi, Yoshiko Daimon, Makoto |
author_facet | Ito, Ryoma Mizushiri, Satoru Nishiya, Yuki Ono, Shoma Tamura, Ayumi Hamaura, Kiho Terada, Akihide Tanabe, Jutaro Yanagimachi, Miyuki Wai, Kyi Mar Kudo, Yutaro Ihara, Kazushige Takahashi, Yoshiko Daimon, Makoto |
author_sort | Ito, Ryoma |
collection | PubMed |
description | Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015–2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as “obese insulin resistant with sufficient compensatory insulin secretion”, and cluster 2 (n = 136), labeled as “low insulin secretion”, were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to. |
format | Online Article Text |
id | pubmed-9918294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99182942023-02-11 Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables Ito, Ryoma Mizushiri, Satoru Nishiya, Yuki Ono, Shoma Tamura, Ayumi Hamaura, Kiho Terada, Akihide Tanabe, Jutaro Yanagimachi, Miyuki Wai, Kyi Mar Kudo, Yutaro Ihara, Kazushige Takahashi, Yoshiko Daimon, Makoto J Clin Med Article Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015–2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as “obese insulin resistant with sufficient compensatory insulin secretion”, and cluster 2 (n = 136), labeled as “low insulin secretion”, were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to. MDPI 2023-01-19 /pmc/articles/PMC9918294/ /pubmed/36769457 http://dx.doi.org/10.3390/jcm12030810 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ito, Ryoma Mizushiri, Satoru Nishiya, Yuki Ono, Shoma Tamura, Ayumi Hamaura, Kiho Terada, Akihide Tanabe, Jutaro Yanagimachi, Miyuki Wai, Kyi Mar Kudo, Yutaro Ihara, Kazushige Takahashi, Yoshiko Daimon, Makoto Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title | Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title_full | Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title_fullStr | Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title_full_unstemmed | Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title_short | Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables |
title_sort | two distinct groups are shown to be at risk of diabetes by means of a cluster analysis of four variables |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918294/ https://www.ncbi.nlm.nih.gov/pubmed/36769457 http://dx.doi.org/10.3390/jcm12030810 |
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