Zarkoob Hadi, Lewinsky Sarah, Almgren Peter, Melander Olle, Fakhrai-Rad Hossein
BaseHealth Inc., Sunnyvale, California, United States of America.
Department of Clinical Sciences, Lund University, Malmö, Sweden.
PLoS One. 2017 Jul 12;12(7):e0180180. doi: 10.1371/journal.pone.0180180. eCollection 2017.
The aim of this study was to measure the impact of genetic data in improving the prediction of type 2 diabetes (T2D) in the Malmö Diet and Cancer Study cohort. The current study was performed in 3,426 Swedish individuals and utilizes of a set of genetic and environmental risk data. We first validated our environmental risk model by comparing it to both the Finnish Diabetes Risk Score and the T2D risk model derived from the Framingham Offspring Study. The area under the curve (AUC) for our environmental model was 0.72 [95% CI, 0.69-0.74], which was significantly better than both the Finnish (0.64 [95% CI, 0.61-0.66], p-value < 1 x 10-4) and Framingham (0.69 [95% CI, 0.66-0.71], p-value = 0.0017) risk scores. We then verified that the genetic data has a statistically significant positive correlation with incidence of T2D in the studied population. We also verified that adding genetic data slightly but statistically increased the AUC of a model based only on environmental risk factors (RFs, AUC shift +1.0% from 0.72 to 0.73, p-value = 0.042). To study the dependence of the results on the environmental RFs, we divided the population into two equally sized risk groups based only on their environmental risk and repeated the same analysis within each subpopulation. While there is a statistically significant positive correlation between the genetic data and incidence of T2D in both environmental risk categories, the positive shift in the AUC remains statistically significant only in the category with the lower environmental risk. These results demonstrate that genetic data can be used to increase the accuracy of T2D prediction. Also, the data suggests that genetic data is more valuable in improving T2D prediction in populations with lower environmental risk. This suggests that the impact of genetic data depends on the environmental risk of the studied population and thus genetic association studies should be performed in light of the underlying environmental risk of the population.
本研究的目的是在马尔默饮食与癌症研究队列中，衡量基因数据对改善2型糖尿病（T2D）预测的影响。当前的研究在3426名瑞典个体中进行，并利用了一组基因和环境风险数据。我们首先通过将我们的环境风险模型与芬兰糖尿病风险评分以及从弗雷明汉后代研究得出的T2D风险模型进行比较，对其进行了验证。我们环境模型的曲线下面积（AUC）为0.72 [95%置信区间，0.69 - 0.74]，这显著优于芬兰模型（0.64 [95%置信区间，0.61 - 0.66]，p值 < 1×10⁻⁴）和弗雷明汉模型（0.69 [95%置信区间，0.66 - 0.71]，p值 = 0.0017）的风险评分。然后我们验证了基因数据与所研究人群中T2D的发病率具有统计学上显著的正相关性。我们还验证了添加基因数据虽微小但在统计学上增加了仅基于环境风险因素的模型的AUC（风险因素，AUC从0.72变为0.73，偏移 +1.0%，p值 = 0.042）。为了研究结果对环境风险因素的依赖性，我们仅根据环境风险将人群分为两个规模相等的风险组，并在每个亚组内重复相同的分析。虽然在两个环境风险类别中基因数据与T2D发病率之间均存在统计学上显著的正相关性，但AUC的正向偏移仅在环境风险较低的类别中在统计学上仍然显著。这些结果表明基因数据可用于提高T2D预测的准确性。此外，数据表明基因数据在改善环境风险较低人群的T2D预测方面更有价值。这表明基因数据的影响取决于所研究人群的环境风险，因此基因关联研究应根据人群潜在的环境风险来进行。
