ACCURACY OF CARBOHYDRATE COUNTING IN TYPE 1 DIABETES MELLITUS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 饮食行为影响1型糖尿病患者的健康结局。目的：探讨准确计算碳水化合物对1型糖尿病儿童血糖控制及生长发育模式的影响。100名2~12岁1型糖尿病儿童报告以计算碳水化合物为饮食方法的横断面分析。患者将在马萨诸塞州波士顿儿童医院的常规门诊期间登记。相关调查和问卷将在入学时完成。对于每个参与研究的患者，24小时的饮食召回将通过电话采访进行3次不同的场合。家长对碳水化合物含量的评估将与24小时饮食召回的计算机分析进行比较。预计招生期限为6个月。主要观察指标：家长对碳水化合物含量的评估和24次膳食回忆、糖化血红蛋白水平、人体测量学数据、胰岛素方案和严重低血糖反应及相关人口学数据的计算机分析。结果：作为一个整体，父母对孩子碳水化合物摄入量的估计将与计算机对相同膳食的分析进行比较，以百分比表示(主要目标)。如果估计值在计算机分析的范围内或偏离计算机分析的25%以上，则将被归类为“准确”或“不准确”。临床变量和人口统计变量将被合并到Logistic回归模型中，以确定碳水化合物计数准确性的预测因素。最后，准确和不准确的父母将根据他们患有1型糖尿病的孩子的临床相关结果进行比较：HbA1c(血糖控制指标)、严重低血糖发生率和体重指数(二级目标)。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Dietary behaviors impact health outcomes in patients with type 1 diabetes mellitus. OBJECTIVE: To determine the impact of accurately of counting carbohydrates on glycemic control and growth patterns in children with type 1 diabetes mellitus. Cross-sectional analysis of 100 children with type 1 diabetes mellitus aged 2 to 12 years who report carbohydrate counting as their dietary method. Patients will be enrolled during routine outpatient visits at Children's Hospital, Boston, Massachusetts. Relevant surveys and questionnaires will be completed at the time of enrollment. 24-hour dietary recalls will be performed by phone interview on 3 separate occasions for each patient enrolled in the study. Parent's assessments of carbohydrate content will be compared to computer analysis of the 24-hour dietary recall. The anticipated enrollment period will be 6 months. MAIN OUTCOME MEASURES: Assessment of carbohydrate content by parent and computer analysis of 24-dietary recall, hemoglobin A1c level, anthropometric data, insulin regimen and severe hypoglycemic reactions and relevant demographic data. RESULTS: As a group, parents' estimation of their child's carbohydrate intake will be compared to computer analysis of the same meals, expressed as a percentage (primary aim). Estimations will be categorized as "accurate" or "inaccurate" if they are within or deviate by greater than 25% of the computer analysis, respectively. Clinical variables and demographic variables will be incorporated into a logistic regression model to determine predictors of accuracy in carbohydrate counting. Finally, parents who are accurate and inaccurate will be compared on the basis of clinically relevant outcomes for their children with type 1 diabetes: HbA1c (measure of glycemic control), rates of severe hypoglycemia and body mass index (secondary aims).