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Background
The prevalence of type 2 diabetes mellitus (DM) and pre–diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data.
Objective
We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research.
Methods
Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes.
Results
We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×10-6). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND.
Conclusions
Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.
youth prediabetes and diabetespublic data setNHANESweb portalepidemiologybiostatisticsmachine learningNational Health and Nutrition Examination SurveyIntroduction
Type 2 diabetes mellitus (DM) is a complex disease influenced by several biological and epidemiological factors [1,2], such as obesity [3], family history [4], diet [1,5], physical activity level [1,6-8], and socioeconomic status [9-11]. Prediabetes, characterized by elevated blood glucose levels below the diabetes threshold, is a precursor condition to DM [12]. There has been an alarming increasing trend in the prevalence of youth with pre–diabetes mellitus (pre-DM) and DM both in the United States [13-19] and worldwide [20,21], and the numbers of newly diagnosed youth living with pre-DM/DM are also expected to increase [14,20,22]. The latest estimate based on nationally representative data showed that the prevalence of pre-DM among youth increased from 11.6% in 1999-2002 to 28.2% in 2015-2018 in the United States [13]. This growth is particularly concerning because pre-DM/DM disproportionately affects racial and ethnic minority groups and those with low socioeconomic status [9-11,22-24], leading to significant health disparities. Having pre-DM/DM at a younger age also confers a higher health and economic burden resulting from living with the condition for more years and a higher risk of developing other cardiometabolic diseases [25-30]. This serious challenge calls for increased translational research into factors associated with pre-DM/DM among youth and how they can collectively affect disease risk and inform prevention strategies.
In particular, the most critically needed research in this direction is exploring the collective impact of various risk factors across multiple health-related domains. While clinical factors, such as obesity, have been mechanistically linked to insulin resistance [31], it is important to consider the broader perspective. There is an increasing recognition that social determinants of health (SDoH) play a significant role in amplifying the risk of pre-DM/DM and their related disparities. For example, factors such as limited access to health care, food and housing insecurity, and the neighborhood-built environment have been identified as influential contributors [9-11,32]. However, to gain a comprehensive understanding, it is essential to delve into other less studied variables, such as screen time, acculturation, or frequency of eating out, and examine how they interact to increase the risk of pre-DM/DM among youth [2].
One of the major challenges that has limited translational research into youth pre-DM/DM risk factors is that there are not publicly available, easily accessible data comprehensively profiling interrelated epidemiological factors for young individuals [2]. Specifically, most available public diabetes data portals focus on providing aggregated descriptive trends, such as pre-DM/DM prevalence for the entire population or subgroups stratified by race and ethnicity [33-36], which does not allow in-depth examination of the relationships between multiple risk factors and pre-DM/DM risk using individual-level data. While there do exist a few individual-level public diabetes data sets [37-41], they include mainly clinical measurements, while other important risk factors such as those related to diet, physical activity, and SDoH are limited. In addition, these data sets are not available for youth populations, as they focus exclusively on adult populations and not on youth specifically [37,39-41]. Furthermore, these data sets are not accompanied by any user-friendly web-based portals that can help explore or analyze these data to reveal interesting knowledge about youth pre-DM/DM. This shows that there is a lack of a comprehensive data set that includes multiple epidemiological variables to study youth pre-DM/DM and easily usable functionalities to explore and analyze data.
To directly address this data gap, we turned to the National Health and Nutrition Examination Survey (NHANES), which offers a promising path for examining pre-DM/DM among the US youth population by providing a rich source of individual- and household-level epidemiological factors. As a result, NHANES has been a prominent data source for studying youth pre-DM/DM trends and associated factors [18,42-45]. However, the use of NHANES data requires extensive data processing that is laborious and time-intensive [46]. This represents a major challenge for the widespread use of these high-quality and extensive data for studying youth pre-DM/DM.
In this work, we directly addressed the above challenges by processing NHANES data from 1999 to 2018 into a large-scale, youth diabetes–focused data set that covers a variety of relevant variable domains, namely, sociodemographic factors, health status indicators, diet, and other lifestyle behaviors. We also provided public access to this high-quality comprehensive youth pre-DM/DM data set, as well as functionalities to explore and analyze it, through the user-friendly Prediabetes/diabetes in youth Online Dashboard (POND) [47]. We demonstrated the data set’s use and potential through 2 case studies that used statistical analyses and machine learning (ML) approaches, respectively, to identify important epidemiological factors that are associated with youth pre-DM/DM.
Through this work, we aim to advance youth diabetes research by providing the most comprehensive epidemiological data set available through a public web portal and illustrating the value of these resources through our example case studies based on statistical analyses and ML. Our overarching goal is to enable researchers to investigate the multifactorial variables associated with youth pre-DM/DM, which may drive translational advances in prevention and management strategies.
MethodsOverview
Figure 1 [48] shows the overall study design and workflow. In the following subsections, we detail the components of the workflow.
Study design and workflow. We processed data from 10 survey cycles (1999-2018) from the National Health and Nutrition Examination Survey (NHANES), which yielded 15,149 youths with known pre-DM/DM status. We extracted 95 variables that were relevant to pre-DM/DM and organized them into 4 domains: sociodemographic, health status, diet, and other lifestyle behaviors. We made the data set easily accessible to the public through the user-friendly POND (Prediabetes/diabetes in youth Online Dashboard) web portal, enabling users to navigate, visualize, and download the data. In addition, we conducted 2 case studies with complementary statistical and machine learning methods that are designed to illustrate the translation potential of our data set and point. Both analyses identified predictive variables associated with youth diabetes, and the results can be explored in POND (some images in this figure were obtained from an open-source collection). DM: diabetes mellitus.
Data Source and Study Population
We built the youth pre-DM/DM data set based on publicly available NHANES data [49] spanning the years from 1999 to 2018. Developed by the Centers for Disease Control and Prevention, NHANES is a serial cross-sectional survey that gathers comprehensive health-related information from nationally representative samples of the noninstitutionalized population in the United States. The survey uses a multistage probability sampling method and collects data through questionnaires, physical examinations, and biomarker analysis. Each year, approximately 5000 individuals are included in the survey, and the data are publicly released in 2-year cycles.
Figure 2 details the process used to define our study population. Briefly, of the total 101,316 participants in 1999-2018 NHANES, we excluded individuals who (1) were not within the 12-19 years age range, (2) did not have either of the biomarkers used to define pre-DM/DM status, and (3) answered “Yes” to “Have you ever been told by a doctor or health professional that you have diabetes?” The youth pre-DM/DM outcome of this work was derived as follows: youth were considered at risk of pre-DM/DM if their fasting plasma glucose (FPG) was at or greater than 100 mg/dL, or their glycated hemoglobin (HbA1c) was at or greater than 5.7%, according to the current American Diabetes Association (ADA) pediatric clinical guidelines [2].
Flow chart showing the inclusion and exclusion criteria applied to 1999-2018 NHANES participants that yielded the study population included in our youth pre-DM/DM data set. Pre-DM/DM status was defined by the current American Diabetes Association (ADA) biomarker criteria, that is, elevated levels of 1 of 2 pre-DM/DM biomarkers (FPG ≥100 mg/dL or HbA1c ≥5.7%). DM: diabetes mellitus; FPG: fasting plasma glucose; HbA1c: glycated hemoglobin; NHANES: National Health and Nutrition Examination Survey.
Validation of the Study Population
We estimated pre-DM/DM prevalence across the 10 survey cycles (1999-2018) by incorporating the NHANES design elements in the analysis and compared the general trend with those reported in the literature [18,19]. We also specifically applied the analytical methods reported in a recent study [13] based on NHANES data to our study population to replicate the trends in pre-DM among youth in the United States from 1999 to 2018 reported in that analysis. Specifically, that study selected a youth population from 12-19 years of age with positive sampling weight from the fasting subsample (ie, nonzero and nonmissing Fasting Subsample 2 Year Mobile Examination Centers Weight [“WTSAF2YR”]; personal communication) without a self-reported physician-diagnosed DM. In addition, that study focused only on pre-DM, which was defined as an HbA1c level between 5.7% and 6.4% or an FPG level between 100 mg/dL and 125 mg/dL [13].
Development of Youth Pre-DM/DM Data Set
Based on the most recent ADA standard of care recommendations including factors related to pre-DM/DM risk and management [2], we selected 27 potentially relevant NHANES questionnaires and grouped them into 4 domains: sociodemographic, health status, diet, and other lifestyle behaviors. For example, under the health status domain, BMI was included as a potential risk factor for youth pre-DM/DM [2]. Similarly, lifestyle and behavioral variables included factors, such as diet and physical activity, that have been shown to be critical for pre-DM/DM prevention in both observational studies and randomized clinical trials [50-52]. Our sociodemographic domain included demographic, socioeconomic, and SDoH variables (eg, age, gender, poverty status, and food security). Except for commonly available clinical measurements, such as blood pressure and total cholesterol, we did not include laboratory data (eg, triglycerides, transferrin, C-reactive protein, interleukin-6, and white blood cells), since these measurements were not collected for all NHANES participants and were not commonly accessible for the general population.
From the selected questionnaires, we identified a list of 95 variables based on the aforementioned methodology. The complete list of variables is provided in Table S1 in Section S1 of Multimedia Appendix 1 [13,49,53-62] and on our POND web portal [47]. All the code developed, processed data, and detailed description of variables are also available on the web portal [47]. The process of extracting these variables involved extensive examination of the questions that were asked, consultation of the literature, and discussions to reach consensus within the study team. The details of this process are provided in Figure S1 and Section S2 of Multimedia Appendix 1. We used SAS (version 9.4; SAS Institute) and R (version 4.2.2; R Core Team, 2022) in R Studio (version 4.2.2; R Core Team, 2022) for data processing and data set development.
Building the POND
To facilitate other researchers’ use of our youth pre-DM/DM data set and make our methodology transparent and reproducible, we developed POND to share our processed data set and enable users to understand and explore the data on their own. The web portal was developed using R markdown and the flexdashboard package [63] and was published as a Shiny application [64]. Table S2 and Section S3 in Multimedia Appendix 1 provide details of all the R packages used to develop POND, and the related code is available on the portal’s do