Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04881383 |
| Other study ID # |
UW19-831 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
April 1, 2020 |
| Est. completion date |
June 30, 2023 |
Study information
| Verified date |
May 2022 |
| Source |
The University of Hong Kong |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Many DM and pre-DM remain undiagnosed. The aim is to develop and validate a risk prediction
function to detect DM and pre-DM in Chinese adults aged 18-84 in primary care (PC). The
objectives are to:
1. Develop a risk prediction function using non-laboratory parameters to predict DM and
pre-DM from the data of the HK Population Health Survey 2014/2015
2. Develop a risk scoring algorithm and determine the cut-off score
3. Validate the risk prediction function and determine its sensitivity in predicting DM and
pre-DM in PC
Hypothesis to be tested:
The prediction function developed from the Population Health Survey (PHS) 2014/2015 is valid
and sensitive in PC.
Design and subjects:
We will develop a risk prediction function for DM and pre-DM using data of 1,857 subjects
from the PHS 2014/2015. We will recruit 1014 Chinese adults aged 18-84 from PC clinics to
validate the risk prediction function. Each subject will complete an assessment on the
relevant risk factors and have a blood test on OGTT and HbA1c on recruitment and at 12
months.
Main outcome measures:
The area under the Receiver operating characteristic (ROC) curve, sensitivity and specificity
of the prediction function.
Data analysis and expected results:
Machine learning and Logistic regressions will be used to develop the best model. ROC curve
will be used to determine the cut-off score. Sensitivity and specificity will be determined
by descriptive statistics. A new HK Chinese general population specific risk prediction
function will enable early case finding and intervention to prevent DM and DM complications
in PC.
Description:
Diabetes Mellitus (DM) is the second most common chronic non-communicable disease (NCD) and a
major public health issue. In 2017, it was estimated that 451 million adults worldwide had
DM, a number that is anticipated to rise to 693 million by 2045. In terms of economic burden,
it was estimated that the global cost of DM in 2015 was 1·31 trillion US dollars, which
accounted for 1.8% of global gross domestic product. In China, the prevalence of DM has
increased rapidly, from less than 1% in 1980 to 10.9% in 2013, with approximately 109.6
million Chinese adults (25.8% of all cases worldwide) currently living with the condition.
Among the Chinese population, Hong Kong has one of the highest prevalence of DM. The
Population Health Survey (PHS) 2014/2015 conducted by Department of Health found a prevalence
of 8.4% of DM among persons aged 15-84 in Hong Kong, more than half (4.5%) of which were
previously unknown. Data (unpublished) from the Population Health Survey 2014/2015 showed a
further 9.5% of persons aged 15-84 had hyperglycaemia (pre-diabetes) but were unaware of the
problem before the survey DM can result in severe complications, which lead to disabling
morbidity and premature mortality. A number of randomised controlled trials (RCTs) have found
that lifestyle interventions (e.g., diet, exercise) and pharmacological treatments are
effective in preventing DM and its complications. However, it has been reported that 224
million adults (49.7% of all cases) world-wide are unaware that they have the condition,
similar to the finding of the Hong Kong PHS 2014/2015. DM can be present for 9-12 years prior
to a diagnosis and is often only detected when patients present with complications. Hence,
there is an urgent need for earlier detection of DM so that appropriate interventions can be
provided to prevent and/or delay progression to complications. It would be even more
effective if individuals could be identified at the pre-diabetes (pre-DM) stage when there
may still be an opportunity to revert to normoglycaemia by life-style modifications. While DM
satisfies all Wilson and Jungner's (1968) criteria of screening studies have shown that
general population screening is not effective and the current recommendation is case finding
targeting at high-risk individuals. The Hong Kong Reference Framework for Diabetes Care for
Adults in Primary Care Settings recommends periodic screening for DM among persons aged >=45
years old or having DM risk factors. The recommended methods for screening include 75-g oral
glucose tolerance tests (OGTT), fasting plasma glucose (FPG) tests or HbA1c tests. Indeed, a
cost-effectiveness analysis reported that screening for DM and prediabetes was cost-saving
among patients identified as being at high risk (e.g., body mass index (BMI) > 35 kg/m(2),
systolic blood pressure ≥ 130mmHg or > 55 years of age) when compared with no screening. In
order to identify high risk individuals more accurately, multivariate risk prediction models
have been developed and incorporated into DM prevention programs in a number of Western
countries. Such models have included sociodemographic factors (e.g., age, sex), clinical
factors (e.g., family history of DM, gestational DM) or biomarkers (e.g., BMI, blood
pressure). However, the majority of these models were developed primarily in Caucasian
populations and have not performed as well among Chinese populations. For example, the
Prospective Cardiovascular Münster, Cambridge, San Antonia and Framingham models were found
to have inferior discrimination in a cohort of Chinese people. This can be due to ethnic
differences as well as lifestyle and socioeconomic factors, calling for the need of
population-specific risk prediction models. Since 2009, a number of risk prediction models
and scoring algorithms have been developed specifically for Chinese populations, mostly from
Mainland China, only two of which were developed and validated on Hong Kong Chinese people.
The first used simple self-reported factors and laboratory measurements to develop a scoring
algorithm. However, the generalisability of the model to primary care patients may be limited
as 70% of the subjects of the development and validation samples had known risk factors for
DM. The second risk prediction model for Hong Kong Chinese was previously developed by
members of the investigators' team with data from 3,357 asymptomatic non-diabetic
professional drivers. Non-laboratory risk factors included age, BMI, family history of DM,
regular physical activity (PA), and high blood pressure. Triglyceride was added to the
laboratory-based algorithm. The application of this risk predication model is limited because
the sample was predominately male (92.7%) professional drivers and the accuracy was modest.
It is noted that the majority of factors included in previous models are non-modifiable
(e.g., family history of DM, gestational DM, age), and there is a call for future research to
incorporate more lifestyle factors in order to improve the predictive validity and impact of
risk prediction models. Lifestyle factors that may be associated with DM and pre-DM include
physical activity (PA) level, dietary factors (e.g., fibre, sugar or fat intake), alcohol
consumption, smoking and sleep. This proposed study aims to develop a new DM and pre-DM risk
prediction model specific for the Hong Kong general Chinese population that incorporates
traditional and modifiable life style factors. The investigators will apply the novel method
of machine learning as well as the traditional logistic regression in model development to
improve predictive power. The investigators hope the results will enable the implementation
of effective case finding of DM and pre-DM in primary care, and prevent mortality and
morbidity from this common but silent NCD for the people in Hong Kong.
