Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China.
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
BMJ Open. 2022 May 24;12(5):e059430. doi: 10.1136/bmjopen-2021-059430.
Diabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care.
A cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong's Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated.
Ethics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal.
US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.
糖尿病(DM)是一种发病率不断上升的主要非传染性疾病。未确诊的 DM 并不少见,可导致严重并发症和死亡。在疾病早期识别高危个体,即糖尿病前期(Pre-DM),对于延缓疾病进展至关重要。现有的风险模型主要依赖于不可改变的因素来预测 DM 风险,而且很少适用于中国人。本研究旨在开发和验证一种风险预测功能,该功能纳入了可改变的生活方式因素,以检测中国成年人在初级保健中的 DM 和 Pre-DM。
一项横断面研究,使用香港人口健康调查(PHS)2014/2015 年的数据来开发 DM/Pre-DM 风险预测函数,并进行 12 个月的前瞻性研究以验证该函数在 DM/Pre-DM 个体中的病例发现。将从 PHS 2014/2015 中提取 1857 名无自我报告 DM/Pre-DM 史的中国成年人的数据,使用逻辑回归和机器学习方法建立 DM/Pre-DM 风险模型。将从香港公立和私立基层医疗诊所招募 1014 名无已知 DM/Pre-DM 史的中国成年人。他们将在招募时完成一份关于相关危险因素的问卷,并进行口服葡萄糖耐量试验(OGTT)和血红蛋白 A1c(HbA1c)的血液检测,如果第一次血液检测为阴性,则在 12 个月时再次进行检测。任何一次血液检测中 OGTT 或 HbA1c 阳性定义为 DM/Pre-DM 阳性病例。将计算模型在检测 DM/Pre-DM 中的曲线下面积、敏感性、特异性、阳性预测值和阴性预测值。
香港大学/香港医院管理局香港西联网(UW19-831)和香港医院管理局九龙中/东联网(REC(KC/KE)-21-0042/ER-3)已批准该研究。研究结果将提交给同行评议的期刊发表。
美国临床试验.gov:NCT04881383;香港大学临床试验注册处:HKUCTR-2808;预结果。