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Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population

AIMS: This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population. METHODS: Participants aged 25-64 years were recruited from a Shanghai population from July 2019...

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Autores principales: Jin, Shenyi, Chen, Qingguang, Han, Xu, Liu, Yahua, Cai, Mengjie, Yao, Zheng, Lu, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902815/
https://www.ncbi.nlm.nih.gov/pubmed/35273562
http://dx.doi.org/10.3389/fendo.2022.725314
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author Jin, Shenyi
Chen, Qingguang
Han, Xu
Liu, Yahua
Cai, Mengjie
Yao, Zheng
Lu, Hao
author_facet Jin, Shenyi
Chen, Qingguang
Han, Xu
Liu, Yahua
Cai, Mengjie
Yao, Zheng
Lu, Hao
author_sort Jin, Shenyi
collection PubMed
description AIMS: This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population. METHODS: Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b’= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639–0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671–0.899) and 0.731 (95% CI 0.619–0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753–0.964) and 0.823 (95% CI 0.730–0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767–0.844) and 0.830 (95% CI 0.788–0.872), respectively, with seven as the cut-off point. CONCLUSIONS: The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population.
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spelling pubmed-89028152022-03-09 Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population Jin, Shenyi Chen, Qingguang Han, Xu Liu, Yahua Cai, Mengjie Yao, Zheng Lu, Hao Front Endocrinol (Lausanne) Endocrinology AIMS: This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population. METHODS: Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b’= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639–0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671–0.899) and 0.731 (95% CI 0.619–0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753–0.964) and 0.823 (95% CI 0.730–0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767–0.844) and 0.830 (95% CI 0.788–0.872), respectively, with seven as the cut-off point. CONCLUSIONS: The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population. Frontiers Media S.A. 2022-02-22 /pmc/articles/PMC8902815/ /pubmed/35273562 http://dx.doi.org/10.3389/fendo.2022.725314 Text en Copyright © 2022 Jin, Chen, Han, Liu, Cai, Yao and Lu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Jin, Shenyi
Chen, Qingguang
Han, Xu
Liu, Yahua
Cai, Mengjie
Yao, Zheng
Lu, Hao
Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title_full Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title_fullStr Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title_full_unstemmed Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title_short Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population
title_sort comparison of the finnish diabetes risk score model with the metabolic syndrome in a shanghai population
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902815/
https://www.ncbi.nlm.nih.gov/pubmed/35273562
http://dx.doi.org/10.3389/fendo.2022.725314
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