Xia Li 1,2,3 , Shuting Yang 1,2,3 , Chuqing Cao 1,2,3 , Xiang Yan 1,2,3 , Lei Zheng 4 , Lanbo Zheng 4 , Jiarui Da 4 , Xiaohan Tang 1,2,3 , Linong Ji 5 , Xilin Yang 6 , Zhiguang Zhou 1,2,3 Show Affiliations »
Abstract
CONTEXT: This study applied the Swedish novel data-driven classification in Chinese newly diagnosed diabetic patients and validated its adoptability. OBJECTIVE: This study aimed to validate the practicality of the Swedish diabetes regrouping scheme in Chinese adults with newly diagnosed diabetes. DESIGN: Patients were classified into 5 subgroups by K-means and Two-Step methods according to 6 clinical parameters. SETTING: Ambulatory care. PATIENTS: A cross-sectional survey of 15 772 patients with adult-onset newly diagnosed diabetes was conducted in China from April 2015 to October 2017. INTERVENTION: None. MAIN OUTCOME MEASURES: Six parameters including glutamate decarboxylase antibodies (GADA), age of onset, body mass index (BMI), glycated hemoglobin A1c (HbA1c), homoeostatic model assessment 2 estimates of β-cell function (HOMA2-B) and insulin resistance (HOMA2-IR) were measured to calculate the patient subgroups. RESULTS: Our patients clustered into 5 subgroups: 6.2% were in the severe autoimmune diabetes (SAID) subgroup, 24.8% were in the severe insulin-deficient diabetes (SIDD) subgroup, 16.6% were in the severe insulin-resistance diabetes (SIRD) subgroup, 21.6% were in the mild obesity-related diabetes (MOD) subgroup and 30.9% were in the mild age-related diabetes (MARD) subgroup. When compared with the Swedish population, the proportion of SIDD subgroup was higher. In general, Chinese patients had younger age, lower BMI, higher HbA1c, lower HOMA2-B and HOMA2-IR, and higher insulin use but lower metformin usage than the Swedish patients. CONCLUSION: The Swedish diabetes regrouping scheme is applicable to adult-onset diabetes in China, with a high proportion of patients with the severe insulin deficient diabetes. Further validations of long-term diabetes complications remain warranted in future studies. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
CONTEXT: This study applied the Swedish novel data-driven classification in Chinese newly diagnosed diabetic patients and validated its adoptability. OBJECTIVE: This study aimed to validate the practicality of the Swedish diabetes regrouping scheme in Chinese adults with newly diagnosed diabetes . DESIGN: Patients were classified into 5 subgroups by K-means and Two-Step methods according to 6 clinical parameters. SETTING: Ambulatory care. PATIENTS : A cross-sectional survey of 15 772 patients with adult-onset newly diagnosed diabetes was conducted in China from April 2015 to October 2017. INTERVENTION: None. MAIN OUTCOME MEASURES: Six parameters including glutamate decarboxylase antibodies (GADA), age of onset, body mass index (BMI), glycated hemoglobin A1c (HbA1c), homoeostatic model assessment 2 estimates of β-cell function (HOMA2-B) and insulin resistance (HOMA2-IR) were measured to calculate the patient subgroups. RESULTS: Our patients clustered into 5 subgroups: 6.2% were in the severe autoimmune diabetes (SAID) subgroup, 24.8% were in the severe insulin -deficient diabetes (SIDD) subgroup, 16.6% were in the severe insulin -resistance diabetes (SIRD) subgroup, 21.6% were in the mild obesity-related diabetes (MOD) subgroup and 30.9% were in the mild age-related diabetes (MARD) subgroup. When compared with the Swedish population, the proportion of SIDD subgroup was higher. In general, Chinese patients had younger age, lower BMI, higher HbA1c, lower HOMA2-B and HOMA2-IR, and higher insulin use but lower metformin usage than the Swedish patients . CONCLUSION: The Swedish diabetes regrouping scheme is applicable to adult-onset diabetes in China, with a high proportion of patients with the severe insulin deficient diabetes . Further validations of long-term diabetes complications remain warranted in future studies. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Entities: Chemical
Disease
Gene
Species
Keywords:
clustering; diabetes; novel classification; precision medicine; validation
Year: 2020
PMID： 32808015 DOI： 10.1210/clinem/dgaa524
Source DB: PubMed Journal: J Clin Endocrinol Metab ISSN： 0021-972X Impact factor: 5.958