胰岛素抵抗和胰岛素分泌受损可预测糖尿病的发生:对两个韩国全国范围内具有人口代表性队列的统计匹配应用。

IF 3.9 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Hyemin Jo, Soyeon Ahn, Jung Hun Ohn, Cheol Min Shin, Eunjeong Ji, Donggil Kim, Sung Jae Jung, Joongyub Lee
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引用次数: 0

摘要

背景:利用全国人口代表性数据评估胰岛素抵抗和胰岛素分泌受损是否是韩国人糖尿病发病的有效预测因素:利用全国人口代表性数据,评估胰岛素抵抗和胰岛素分泌受损是否是韩国人糖尿病发病的有效预测因素,以提高数据的私密性:本研究分析了 2007-2010 年和 2015 年韩国国民健康和营养调查(KNHANES)以及国民健康保险服务-国民健康检查队列(NHIS-HEALS)中年龄大于 40 岁的无糖尿病患者的数据。出于隐私考虑,这些数据库无法使用直接标识符进行链接。因此,我们生成了 10 个合成数据集,然后与 NHIS-HEALS 进行统计匹配。胰岛素抵抗的稳态模型评估(HOMA-IR)和β细胞功能的稳态模型评估(HOMA-β)分别作为胰岛素抵抗和胰岛素分泌功能的指标,NHIS-HEALS中记录了糖尿病的发病情况:在对 10 个合成的 KNHANES 和 NHIS-HEALS 数据集进行统计匹配后,中位数为 4580(范围为 4463 至 4761)名成人被纳入分析。在平均 5.8 年的随访期间,中位数为 4.7%(4.3% 至 5.0%)的参与者患上了糖尿病。与参考的低 HOMA-IR/high-HOMA-β 组相比,高 HOMA-IR/low- HOMA-β 组患糖尿病的风险最高,其次是高 HOMA-IR/high-HOMA-β 组和低 HOMA-IR/low- HOMA-β 组(调整后危险比[范围]中位数:3.36 [1.86 至 5.0]):结论:结论:在韩国人群中,胰岛素抵抗和胰岛素分泌受损是糖尿病的可靠预测因素。通过统计匹配将横断面合成数据与纵向索赔队列数据相结合而构建的回顾性队列可能是研究糖尿病自然史的可靠资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insulin Resistance and Impaired Insulin Secretion Predict Incident Diabetes: A Statistical Matching Application to the Two Korean Nationwide, Population-Representative Cohorts.

Background: To evaluate whether insulin resistance and impaired insulin secretion are useful predictors of incident diabetes in Koreans using nationwide population-representative data to enhance data privacy.

Methods: This study analyzed the data of individuals without diabetes aged >40 years from the Korea National Health and Nutrition Examination Survey (KNHANES) 2007-2010 and 2015 and the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Owing to privacy concerns, these databases cannot be linked using direct identifiers. Therefore, we generated 10 synthetic datasets, followed by statistical matching with the NHIS-HEALS. Homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β-cell function (HOMA-β) were used as indicators of insulin resistance and insulin secretory function, respectively, and diabetes onset was captured in NHIS-HEALS.

Results: A median of 4,580 (range, 4,463 to 4,761) adults were included in the analyses after statistical matching of 10 synthetic KNHANES and NHIS-HEALS datasets. During a mean follow-up duration of 5.8 years, a median of 4.7% (range, 4.3% to 5.0%) of the participants developed diabetes. Compared to the reference low-HOMA-IR/high-HOMA-β group, the high-HOMA-IR/low- HOMA-β group had the highest risk of diabetes, followed by high-HOMA-IR/high-HOMA-β group and low-HOMA-IR/low- HOMA-β group (median adjusted hazard ratio [ranges]: 3.36 [1.86 to 6.05], 1.81 [1.01 to 3.22], and 1.68 [0.93 to 3.04], respectively).

Conclusion: Insulin resistance and impaired insulin secretion are robust predictors of diabetes in the Korean population. A retrospective cohort constructed by combining cross-sectional synthetic and longitudinal claims-based cohort data through statistical matching may be a reliable resource for studying the natural history of diabetes.

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来源期刊
Endocrinology and Metabolism
Endocrinology and Metabolism Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
6.60
自引率
5.90%
发文量
145
审稿时长
24 weeks
期刊介绍: The aim of this journal is to set high standards of medical care by providing a forum for discussion for basic, clinical, and translational researchers and clinicians on new findings in the fields of endocrinology and metabolism. Endocrinology and Metabolism reports new findings and developments in all aspects of endocrinology and metabolism. The topics covered by this journal include bone and mineral metabolism, cytokines, developmental endocrinology, diagnostic endocrinology, endocrine research, dyslipidemia, endocrine regulation, genetic endocrinology, growth factors, hormone receptors, hormone action and regulation, management of endocrine diseases, clinical trials, epidemiology, molecular endocrinology, neuroendocrinology, neuropeptides, neurotransmitters, obesity, pediatric endocrinology, reproductive endocrinology, signal transduction, the anatomy and physiology of endocrine organs (i.e., the pituitary, thyroid, parathyroid, and adrenal glands, and the gonads), and endocrine diseases (diabetes, nutrition, osteoporosis, etc.).
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