Cargando…

Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes

AIMS/INTRODUCTION: To develop a non‐invasive risk score to identify Saudis having prediabetes or undiagnosed type 2 diabetes. METHODS: Adult Saudis without diabetes were recruited randomly using a stratified two‐stage cluster sampling method. Demographic, dietary, lifestyle variables, personal and f...

Descripción completa

Detalles Bibliográficos
Autores principales: Bahijri, Suhad, Al‐Raddadi, Rajaa, Ajabnoor, Ghada, Jambi, Hanan, Al Ahmadi, Jawaher, Borai, Anwar, Barengo, Noël C, Tuomilehto, Jaakko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378422/
https://www.ncbi.nlm.nih.gov/pubmed/31957345
http://dx.doi.org/10.1111/jdi.13213
_version_ 1783562416912596992
author Bahijri, Suhad
Al‐Raddadi, Rajaa
Ajabnoor, Ghada
Jambi, Hanan
Al Ahmadi, Jawaher
Borai, Anwar
Barengo, Noël C
Tuomilehto, Jaakko
author_facet Bahijri, Suhad
Al‐Raddadi, Rajaa
Ajabnoor, Ghada
Jambi, Hanan
Al Ahmadi, Jawaher
Borai, Anwar
Barengo, Noël C
Tuomilehto, Jaakko
author_sort Bahijri, Suhad
collection PubMed
description AIMS/INTRODUCTION: To develop a non‐invasive risk score to identify Saudis having prediabetes or undiagnosed type 2 diabetes. METHODS: Adult Saudis without diabetes were recruited randomly using a stratified two‐stage cluster sampling method. Demographic, dietary, lifestyle variables, personal and family medical history were collected using a questionnaire. Blood pressure and anthropometric measurements were taken. Body mass index was calculated. The 1‐h oral glucose tolerance test was carried out. Glycated hemoglobin, fasting and 1‐h plasma glucose were measured, and obtained values were used to define prediabetes and type 2 diabetes (dysglycemia). Logistic regression models were used for assessing the association between various factors and dysglycemia, and Hosmer–Lemeshow summary statistics were used to assess the goodness‐of‐fit. RESULTS: A total of 791 men and 612 women were included, of whom 69 were found to have diabetes, and 259 had prediabetes. The prevalence of dysglycemia was 23%, increasing with age, reaching 71% in adults aged ≥65 years. In univariate analysis age, body mass index, waist circumference, use of antihypertensive medication, history of hyperglycemia, low physical activity, short sleep and family history of diabetes were statistically significant. The final model for the Saudi Diabetes Risk Score constituted sex, age, waist circumference, history of hyperglycemia and family history of diabetes, with the score ranging from 0 to 15. Its fit based on assessment using the receiver operating characteristic curve was good, with an area under the curve of 0.76 (95% confidence interval 0.73–0.79). The proposed cut‐point for dysglycemia is 5 or 6, with sensitivity and specificity being approximately 0.7. CONCLUSION: The Saudi Diabetes Risk Score is a simple tool that can effectively distinguish Saudis at high risk of dysglycemia.
format Online
Article
Text
id pubmed-7378422
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-73784222020-07-27 Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes Bahijri, Suhad Al‐Raddadi, Rajaa Ajabnoor, Ghada Jambi, Hanan Al Ahmadi, Jawaher Borai, Anwar Barengo, Noël C Tuomilehto, Jaakko J Diabetes Investig Articles AIMS/INTRODUCTION: To develop a non‐invasive risk score to identify Saudis having prediabetes or undiagnosed type 2 diabetes. METHODS: Adult Saudis without diabetes were recruited randomly using a stratified two‐stage cluster sampling method. Demographic, dietary, lifestyle variables, personal and family medical history were collected using a questionnaire. Blood pressure and anthropometric measurements were taken. Body mass index was calculated. The 1‐h oral glucose tolerance test was carried out. Glycated hemoglobin, fasting and 1‐h plasma glucose were measured, and obtained values were used to define prediabetes and type 2 diabetes (dysglycemia). Logistic regression models were used for assessing the association between various factors and dysglycemia, and Hosmer–Lemeshow summary statistics were used to assess the goodness‐of‐fit. RESULTS: A total of 791 men and 612 women were included, of whom 69 were found to have diabetes, and 259 had prediabetes. The prevalence of dysglycemia was 23%, increasing with age, reaching 71% in adults aged ≥65 years. In univariate analysis age, body mass index, waist circumference, use of antihypertensive medication, history of hyperglycemia, low physical activity, short sleep and family history of diabetes were statistically significant. The final model for the Saudi Diabetes Risk Score constituted sex, age, waist circumference, history of hyperglycemia and family history of diabetes, with the score ranging from 0 to 15. Its fit based on assessment using the receiver operating characteristic curve was good, with an area under the curve of 0.76 (95% confidence interval 0.73–0.79). The proposed cut‐point for dysglycemia is 5 or 6, with sensitivity and specificity being approximately 0.7. CONCLUSION: The Saudi Diabetes Risk Score is a simple tool that can effectively distinguish Saudis at high risk of dysglycemia. John Wiley and Sons Inc. 2020-02-20 2020-07 /pmc/articles/PMC7378422/ /pubmed/31957345 http://dx.doi.org/10.1111/jdi.13213 Text en © 2020 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Articles
Bahijri, Suhad
Al‐Raddadi, Rajaa
Ajabnoor, Ghada
Jambi, Hanan
Al Ahmadi, Jawaher
Borai, Anwar
Barengo, Noël C
Tuomilehto, Jaakko
Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title_full Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title_fullStr Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title_full_unstemmed Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title_short Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes
title_sort dysglycemia risk score in saudi arabia: a tool to identify people at high future risk of developing type 2 diabetes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378422/
https://www.ncbi.nlm.nih.gov/pubmed/31957345
http://dx.doi.org/10.1111/jdi.13213
work_keys_str_mv AT bahijrisuhad dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT alraddadirajaa dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT ajabnoorghada dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT jambihanan dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT alahmadijawaher dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT boraianwar dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT barengonoelc dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes
AT tuomilehtojaakko dysglycemiariskscoreinsaudiarabiaatooltoidentifypeopleathighfutureriskofdevelopingtype2diabetes