背景:近几十年来,美国年轻人中2型糖尿病(DM)和前驱糖尿病(preDM)的患病率一直在增加,促使迫切需要了解和确定其相关的风险因素。这种努力,然而,由于缺乏易于获取的青年前DM/DM数据而受到阻碍。
目标:我们的目标是首先建立一个高质量的,综合流行病学数据集集中于青年前DM/DM。随后,我们的目标是通过创建一个用户友好的门户网站来共享这些数据和相应的代码,从而使这些数据可以访问。通过这个,我们希望解决这一重大差距,并促进青年preDM/DM研究。
方法:基于1999年至2018年国家健康和营养检查调查(NHANES)的数据,我们清理并协调了数百个与12-19岁青年(n=15,149)的前DM/DM(空腹血糖水平≥100mg/dL和/或HbA1C≥5.7%)相关的变量。我们使用双变量统计分析确定了与preDM/DM风险相关的单个因素,并使用我们的多领域机器学习集成(EI)框架预测了preDM/DM状态。然后,我们开发了一个用户友好的门户网站,名为青少年糖尿病前期/糖尿病在线仪表板(POND),以共享数据和代码。
结果:我们提取了95个与DM/DM风险潜在相关的变量,这些变量被组织成4个领域(社会人口统计学,健康状况,饮食,和其他生活方式行为)。双变量分析确定了preDM/DM的27个显著相关(P≤0.0005,Bonferroni调整),包括种族/民族,健康保险,BMI,添加糖的摄入量,屏幕时间。这些因素中的16个也是根据EI方法确定的(Fisher重叠的P=7.06x10^-6)。除了那些,EI方法确定了11个额外的预测变量,包括一些已知的(例如,肉类和水果摄入量和家庭收入)以及不太认可的因素(例如,家庭中的房间数量)。在两个分析中确定的因素跨越所提到的所有4个领域。这些数据和结果,以及其他探索工具,可以在POND上访问(https://rstudio-connect。hpc.mssm.edu/POND/)。
结论:使用NHANES数据,我们建立了一个最大的公共流行病学数据集,用于研究青年前DM/DM,并使用补充分析方法确定了潜在的危险因素.我们的结果与preDM/DM的多因素性质一致,并具有多个领域的相关性。此外,我们的数据共享平台,庞德,促进广泛的应用,为未来的青年预DM/DM研究提供信息。
背景:
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=7.06×106). 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.