Clinical evaluation of the reference intervals for diabetes in Chinese geriatric population: a cross-sectional cohort
Chenglong Zhou1,2, Jun Li1,2, Xiaochu Wu2
1The Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Insights
This study establishes diagnostic reference intervals for Haemoglobin A1c (HbA1c) and glycated albumin (GA) in older Chinese adults. It aims to find optimal cut-off values for diagnosing diabetes mellitus (DM) in this population.
Area of Science:
- Gerontology
- Endocrinology
- Clinical Diagnostics
Background:
- Diabetes mellitus (DM) presents a significant health challenge for China's large and growing elderly population.
- Current diagnostic criteria like Haemoglobin A1c (HbA1c) can be influenced by factors such as age, pregnancy, race, and anemia.
- Glycated albumin (GA) offers a potential alternative diagnostic marker, as it is less affected by these confounding factors.
Purpose of the Study:
- To determine reference intervals (RIs) for HbA1c and GA specifically for diagnosing diabetes in older adults (60-89 years) in China.
- To assess the optimal cut-off values for HbA1c and GA from a health economic perspective for this demographic.
- To address the diagnostic challenges of DM in China's aging population.
Main Methods:
- A cross-sectional survey involving 1278 community-dwelling older adults in Chengdu City.
- Data collection included questionnaires, physical examinations, and laboratory blood sample analysis.
- Statistical analyses utilized chi-squared tests, Fisher's exact test, and receiver operating characteristic (ROC) curves to identify optimal diagnostic thresholds.
Main Results:
- Reference intervals for HbA1c and GA will be established for older Chinese adults.
- Optimal cut-off values for diagnosing diabetes using HbA1c and GA will be identified.
- The study will provide data for health economic assessments related to diabetes diagnosis in this population.
Conclusions:
- Establishing specific reference intervals and optimal cut-off values for HbA1c and GA is crucial for accurate diabetes diagnosis in China's aging population.
- This research will inform healthcare providers and policymakers on effective diagnostic strategies for elderly individuals with diabetes.
- The findings aim to improve the health economic efficiency of diabetes management in older adults.
Introduction:
Diabetes mellitus (DM) is an important health issue that affects the ageing population. China has the largest geriatric population and the largest number of diabetes cases in the world. This poses a significant challenge for healthcare providers and policymakers. Haemoglobin A1C (HbA1c), which is one of the diagnostic criteria for diabetes, is affected by many factors such as pregnancy, age, race and anaemia. Glycated albumin (GA) is not influenced by factors that affect HbA1c concentrations, although it has been used in the diagnosis of diabetes in a few people. The aim of this study protocol is to determine reference intervals (RIs) of HbA1c and GA for the diagnosis of older adults with diabetes in China and to assess the optimal cut-off values for these parameters from a health economic perspective.
Methods And Analysis:
This cross-sectional survey study will recruit 1278 community-dwelling older adults aged 60-89 in Chengdu City. The data collection process will involve a questionnaire survey, a comprehensive physical examination and the collection of blood samples for laboratory testing. Data analyses will be conducted on the pooled sample and stratified by gender, age or other demographic features if necessary. Rates will be compared using the χ2 test or Fisher test and receiver operating characteristic (ROC) curves will be used to identify the most effective threshold values for HbA1c and GA for diagnosing diabetes among older adults in China.
Ethics And Dissemination:
The study protocol was approved by the ethics review board of the Bioethics Subcommittee of West China Hospital, Sichuan University (Approval No. 1705 in 2022). The study's results will be disseminated through peer-reviewed journals and scientific conferences.
Trial Registration Number:
ChiCTR2300070831.
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