Hitt Jennifer McGuire, Velasquez-Mieyer Pedro, Neira Claudia, Cowan Patricia
University of Tennessee Health Science Center, Memphis, TN.
LifeDoc Diabetes and Obesity Clinic, Memphis, TN.
J Pediatr Nurs. 2016 Sep-Oct;31(5):511-8. doi: 10.1016/j.pedn.2016.03.020. Epub 2016 Apr 25.
We sought to examine the correlation between variables and A1C levels to determine if prediction modeling could be used in the screening and diagnosis of diabetes and prediabetes in youth. We also sought to test relationships between A1C levels to insulin sensitivity indices and β-cell function indices.
We performed a retrospective review of 904 medical records from youth deemed at-risk for the disease. We performed Pearson correlation, multiple regression, and simple regression testing to determine the relationship between variables and A1C levels. In addition, we performed Pearson correlation testing on insulin sensitivity indices and β-cell function indices to determine the strength of correlation to A1C levels.
Statistical analysis did not show a strong relationship between the variables tested and the A1C. When racial and ethnic groups were tested together, the results from African American participants resulted in bias estimates, and as a result, a statistical model for the entire sample could not be performed. Results indicate that A1C is correlated with all β-cell function proxy measurements and correlated to the corrected insulin level at 30minutes, but not the fasting insulin or insulinogenic index.
The results from this study underline the multi-dimensional causes of diabetes and prediabetes and further stress the difficulties in predicting the diseases. The causes of diabetes and prediabetes are multifaceted, often individualized, and often difficult to ascertain.
Clinicians should continue to examine a variety of variables prior to determining the need for diabetes diagnostic testing.
我们试图检验各变量与糖化血红蛋白（A1C）水平之间的相关性，以确定预测模型是否可用于青少年糖尿病和糖尿病前期的筛查与诊断。我们还试图检验A1C水平与胰岛素敏感性指标和β细胞功能指标之间的关系。
我们对904份来自被认为有患该疾病风险的青少年的病历进行了回顾性研究。我们进行了Pearson相关性分析、多元回归分析和简单回归分析，以确定变量与A1C水平之间的关系。此外，我们对胰岛素敏感性指标和β细胞功能指标进行了Pearson相关性分析，以确定与A1C水平的相关强度。
统计分析未显示所测试的变量与A1C之间有很强的关系。当对种族和族裔群体一起进行测试时，非裔美国参与者的结果导致了偏差估计，因此无法对整个样本进行统计模型分析。结果表明，A1C与所有β细胞功能替代测量指标相关，与30分钟时的校正胰岛素水平相关，但与空腹胰岛素或胰岛素生成指数无关。
本研究结果强调了糖尿病和糖尿病前期的多维度病因，并进一步凸显了预测这些疾病的困难。糖尿病和糖尿病前期的病因是多方面的，往往因人而异，且常常难以确定。
临床医生在确定是否需要进行糖尿病诊断测试之前，应继续检查各种变量。
