Evaluation of non-invasive screening measures to identify individuals with prediabetes
Introduction
The Centers for Disease Control and Prevention (CDC) currently estimate that 25.8 million people in the United States (US) have diabetes, and an additional 79 million people are at high-risk with prediabetes, identified by impaired fasting glucose, impaired glucose tolerance or hemoglobin A1c [1]. The prevalence of the metabolic syndrome, a constellation of risk factors that increase the risk for diabetes has also been persistently increasing during the past decade [2]. It is estimated that nearly one third of the US population will have diabetes by 2050 due to increases in diabetes incidence and low mortality rates [3].
The US Diabetes Prevention Program (DPP) demonstrated that type 2 diabetes could be prevented or delayed through intensive lifestyle intervention, with the goals of moderate weight loss and increased physical activity levels [4]. The success of the DPP has led to a variety of translations of its lifestyle intervention that have been conducted in urban and rural areas, within the health care setting, through community groups, and at worksites and academic institutions across the US. Each has demonstrated some level of success in regard to reducing weight, increasing physical activity levels and even improving risk factors for diabetes and cardiovascular disease [11], [12], [13], [14], [15], [16].
Accurate identification of high-risk individuals who will benefit the most from taking part in these diabetes prevention translation efforts is essential. In the DPP, eligibility criteria included age ≥25 years, Body mass index (BMI) ≥24 kg/m2 or ≥22 kg/m2 for Asian Americans, and impaired glucose tolerance diagnosed by a single 75-g oral glucose tolerance test (OGTT) [17]. However, the criteria used to identify high-risk participants who meet program eligibility among community translations of the DPP lifestyle intervention has varied considerably (Table 1) [18]. A common theme among these translation efforts was use of a BMI cut point (≥24 kg/m2 or ≥25 kg/m2), combined with at least one of the previously mentioned measures of diabetes risk listed in Table 1, including having the metabolic syndrome to determine eligibility [11], [12], [13], [14], [15], [16], [19], [20], [21], [22], [23], [24], [25], [26].
Given the high numbers of people at risk for diabetes, a simple, inexpensive method, such as a paper risk test or anthropometric measurement, is needed to facilitate the identification of individuals with prediabetes who could subsequently benefit from lifestyle intervention. Published DPP translation efforts that employed a paper risk test have reported using the 7 question American Diabetes Association (ADA) paper risk assessment developed by Herman et al. [11], [14], [27]; however the ADA paper risk assessment was created to identify individuals at risk for undiagnosed diabetes, not those with prediabetes. The CDC National Diabetes Prevention Recognition Program (DPRP) guidelines focus on blood-based screening for identification of those with prediabetes for inclusion in diabetes prevention programs. However, while the DPRP standards and operating procedures require that at least half of those enrolled in diabetes prevention programs have documented prediabetes, the guidelines also include use of the ADA paper risk test [28] as an alternative screening method for up to half of enrolled individuals, likely due to a lack of viable, non-invasive screening methods [29]. To date, the ADA risk test has not been evaluated for its ability to identify those with prediabetes in the context of a diabetes prevention translation study.
In other efforts, anthropometric measurements such as BMI [30], [31], [32], [33], waist circumference [30], [31], [33], and waist to height ratio [30], [31], [33], [34] have been investigated for their ability to provide details about an individuals’ future risk for type 2 diabetes. However, to the authors’ knowledge, none of these anthropometric measurements have been evaluated for their ability to identify high risk participants in the context of a lifestyle intervention for the prevention of type 2 diabetes.
Therefore, the aims of this paper are to evaluate the ability of the ADA paper risk assessment test incorporated by the CDC DPRP as well as other non-invasive body composition measures to identify individuals with prediabetes. In addition the utility of these alternate screening methods to ascertain the presence of the metabolic syndrome is assessed. It is anticipated that the availability of alternative accurate non-invasive methods to identify those at high risk for diabetes will facilitate enrollment of these individuals in community diabetes prevention efforts.
Section snippets
Participant recruitment and eligibility
Participants were recruited from a local worksite and three community centers in Allegheny County, Pennsylvania to take part in a National Institutes of Health funded effort evaluating delivery of a DPP-based lifestyle intervention translation, the Group Lifestyle Balance (GLB) program, in a variety of community settings. In the GLB program, which retains the same weight loss and physical activity goals as the DPP, participants are encouraged to self-monitor diet, physical activity and weight,
Results
A total of 364 participants were screened onsite, 64% (233) were female, mean age was 55.8 ± 12.5 years, mean BMI was 33.4 ± 6.2 kg/m2 and mean weight was 93.3 ± 20.1 kg (Table 2). Forty-seven percent of participants reported a family history of diabetes, 45% reported a family history of heart disease, and 22% reported both a family history of diabetes and heart disease (Table 2). Participants were predominately white (92.5%), 89% had at least some college or technical school at screening and 93% were
Discussion
This is the first study to report on the sensitivity and specificity of the ADA risk test and other non-invasive measures in the identification of prediabetes confirmed by fasting blood glucose or hemoglobin A1c in diabetes prevention translation. The ADA risk test score of ≥9, BMI cut-point of ≥27 kg/m2 and waist to height ratio of ≥0.5 all effectively identified participants who had prediabetes and, but they all suffered from low specificity. In the current study, if any non-invasive method
Conflict of interest statement
The authors declare that they have no conflict of interest.
Acknowledgments
The authors would like to acknowledge and thank the study participants as well as the community sites. The present research was supported by the National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases DK092985-01. ClinicalTrials.gov Identifier: NCT01050205.
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