文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

AUSDRISK:一种基于人口统计学、生活方式和简单人体测量学指标的澳大利亚 2 型糖尿病风险评估工具。

AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.

机构信息

Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.

出版信息

Med J Aust. 2010 Feb 15;192(4):197-202. doi: 10.5694/j.1326-5377.2010.tb03507.x.


DOI:10.5694/j.1326-5377.2010.tb03507.x
PMID:20170456
Abstract

OBJECTIVE: To develop and validate a diabetes risk assessment tool for Australia based on demographic, lifestyle and simple anthropometric measures. DESIGN AND SETTING: 5-year follow-up (2004-2005) of the Australian Diabetes, Obesity and Lifestyle study (AusDiab, 1999-2000). PARTICIPANTS: 6060 AusDiab participants aged 25 years or older who did not have diagnosed diabetes at baseline. MAIN OUTCOME MEASURES: Incident diabetes at follow-up was defined by treatment with insulin or oral hypoglycaemic agents or by fasting plasma glucose level > or = 7.0 mmol/L or 2-hour plasma glucose level in an oral glucose tolerance test > or = 11.1 mmol/L. The risk prediction model was developed using logistic regression and converted to a simple score, which was then validated in two independent Australian cohorts (the Blue Mountains Eye Study and the North West Adelaide Health Study) using the area under the receiver operating characteristic curve (AROC) and the Hosmer-Lemeshow (HL) chi(2) statistic. RESULTS: 362 people developed diabetes. Age, sex, ethnicity, parental history of diabetes, history of high blood glucose level, use of antihypertensive medications, smoking, physical inactivity and waist circumference were included in the final prediction model. The AROC of the diabetes risk tool was 0.78 (95% CI, 0.76-0.81) and HL chi(2) statistic was 4.1 (P = 0.85). Using a score > or = 12 (maximum, 35), the sensitivity, specificity and positive predictive value for identifying incident diabetes were 74.0%, 67.7% and 12.7%, respectively. The AROC and HL chi(2) statistic in the two independent validation cohorts were 0.66 (95% CI, 0.60-0.71) and 9.2 (P = 0.32), and 0.79 (95% CI, 0.72-0.86) and 29.4 (P < 0.001), respectively. CONCLUSIONS: This diabetes risk assessment tool provides a simple, non-invasive method to identify Australian adults at high risk of type 2 diabetes who might benefit from interventions to prevent or delay its onset.

摘要

目的:基于人口统计学、生活方式和简单人体测量学指标,为澳大利亚开发并验证一种糖尿病风险评估工具。

设计和设置:澳大利亚糖尿病、肥胖和生活方式研究(AusDiab,1999-2000 年)的 5 年随访(2004-2005 年)。

参与者:6060 名年龄在 25 岁及以上、基线时未被诊断患有糖尿病的 AusDiab 参与者。

主要观察指标:随访时新发糖尿病的定义为接受胰岛素或口服降糖药治疗,或空腹血糖水平≥7.0mmol/L 或口服葡萄糖耐量试验 2 小时血糖水平≥11.1mmol/L。使用逻辑回归建立风险预测模型,并将其转换为简单评分,然后使用接受者操作特征曲线(ROC)下面积(AUC)和 Hosmer-Lemeshow(HL)χ²检验在两个独立的澳大利亚队列(蓝山眼科研究和北阿德莱德健康研究)中进行验证。

结果:362 人发生糖尿病。最终预测模型纳入年龄、性别、种族、父母糖尿病史、高血糖史、降压药物使用、吸烟、身体活动不足和腰围。糖尿病风险工具的 AUC 为 0.78(95%CI,0.76-0.81),HL χ²检验为 4.1(P=0.85)。使用评分>或=12(最高 35),识别新发糖尿病的敏感性、特异性和阳性预测值分别为 74.0%、67.7%和 12.7%。在两个独立验证队列中,AUC 和 HL χ²检验分别为 0.66(95%CI,0.60-0.71)和 9.2(P=0.32),0.79(95%CI,0.72-0.86)和 29.4(P<0.001)。

结论:该糖尿病风险评估工具提供了一种简单、非侵入性的方法,可用于识别澳大利亚成年人患 2 型糖尿病的风险,这些人可能受益于预防或延迟其发病的干预措施。

相似文献

[1]
AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.

Med J Aust. 2010-2-15

[2]
Predictive value of serum testosterone for type 2 diabetes risk assessment in men.

BMC Endocr Disord. 2016-5-27

[3]
External Validation of a Tool Predicting 7-Year Risk of Developing Cardiovascular Disease, Type 2 Diabetes or Chronic Kidney Disease.

J Gen Intern Med. 2017-12-4

[4]
Low serum 25-hydroxyvitamin D is associated with increased risk of the development of the metabolic syndrome at five years: results from a national, population-based prospective study (The Australian Diabetes, Obesity and Lifestyle Study: AusDiab).

J Clin Endocrinol Metab. 2012-3-22

[5]
Characteristics of men classified at high-risk for type 2 diabetes mellitus using the AUSDRISK screening tool.

Diabetes Res Clin Pract. 2015-4

[6]
Anthropometric measures and absolute cardiovascular risk estimates in the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study.

Eur J Cardiovasc Prev Rehabil. 2007-12

[7]
Non-invasive Risk Prediction Models in Identifying Undiagnosed Type 2 Diabetes or Predicting Future Incident Cases in the Iranian Population.

Arch Iran Med. 2019-3-1

[8]
Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes.

J Diabetes Investig. 2020-7

[9]
Assessment of risks of "lifestyle" diseases including cardiovascular disease and type 2 diabetes by anthropometry in remote Australian Aborigines.

Asia Pac J Clin Nutr. 2007

[10]
Area-level socioeconomic status and incidence of abnormal glucose metabolism: the Australian Diabetes, Obesity and Lifestyle (AusDiab) study.

Diabetes Care. 2012-5-22

引用本文的文献

[1]
Utility of long-term systolic blood pressure variability for predicting the development of type 2 diabetes mellitus.

Nagoya J Med Sci. 2025-5

[2]
Adaptation of finnish diabetes risk score for screening undiagnosed diabetes and hyperglycemia in Chinese adults.

PLoS One. 2025-7-7

[3]
Genomic risk prediction for type 2 diabetes in Australian individuals aged 70 years and older.

Diabetes Obes Metab. 2025-9

[4]
Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review.

PLoS One. 2025-6-11

[5]
M120 Risk Score Improves Identification of Children at High Risk of Developing Clinical Type 1 Diabetes and Reports Short-term Response to Preventive Immunotherapy.

Diabetes Care. 2025-8-1

[6]
Usefulness of Indian Diabetes Risk Score in Predicting Treatment-Induced Hyperglycemia in Women Undergoing Adjuvant Chemotherapy for Breast Cancer.

South Asian J Cancer. 2023-10-13

[7]
Diabetes Screening in the Emergency Department: Development of a Predictive Model for Elevated Hemoglobin A1c.

J Diabetes Res. 2025-3-12

[8]
The association between chemosensitivity and the 10-year risk of type 2 diabetes in male patients with obstructive sleep apnea.

Sleep Breath. 2024-11-29

[9]
Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus.

J Diabetes Investig. 2025-2

[10]
A single risk assessment for the most common diseases of ageing, developed and validated on 10 cohort studies.

BMC Med. 2024-10-31

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索