The Affiliated Nanhua Hospital, Department of Neurology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
PeerJ. 2023 Oct 12;11:e16240. doi: 10.7717/peerj.16240. eCollection 2023.
To construct a comprehensive nomogram model for predicting the risk of post-stroke depression (PSD) by using clinical data that are easily collected in the early stages, and the level of DNA methylation, so as to help doctors and patients prevent the occurrence of PSD as soon as possible.
We continuously recruited 226 patients with a history of acute ischemic stroke and followed up for three months. Socio-demographic indicators, vascular-risk factors, and clinical data were collected at admission, and the outcome of depression was evaluated at the third month after stroke. At the same time, a DNA-methylation-related sequencing test was performed on the fasting peripheral blood of the hospitalized patients which was taken the morning after admission.
A total of 206 samples were randomly divided into training dataset and validation set according to the ratio of 7:3. We screened 24 potentially-predictive factors by Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analysis, and 10 of the factors were found to have predictive ability in the training set. The PSD nomogram model was established based on seven significant variables in multivariate logistic regression. The consistency statistic (C-index) was as high as 0.937, and the area under curve (AUC) in the ROC analysis was 0.933. Replication analysis results in the validation set suggest the C-index was 0.953 and AUC was 0.926. This shows that the model has excellent calibration and differentiating abilities.
Gender, Rankin score, history of hyperlipidemia, time from onset to hospitalization, location of stroke, National Institutes of Health Stroke scale (NIHSS) score, and the methylation level of the cg02550950 site are all related to the occurrence of PSD. Using this information, we developed a prediction model based on methylation characteristics.
利用易于在早期收集的临床数据和 DNA 甲基化水平构建综合列线图模型，以预测脑卒中后抑郁（PSD）的风险，帮助医生和患者尽早预防 PSD 的发生。
连续招募 226 例有急性缺血性脑卒中病史的患者，进行 3 个月的随访。入院时收集社会人口学指标、血管危险因素和临床数据，在脑卒中后第 3 个月评估抑郁结局。同时对入院次日清晨住院患者的空腹外周血进行 DNA 甲基化相关测序检测。
根据 7：3 的比例，将 206 个样本随机分为训练数据集和验证数据集。通过单因素逻辑回归和最小绝对收缩和选择算子（LASSO）回归分析筛选出 24 个潜在预测因子，在训练集中发现其中 10 个具有预测能力。基于多因素逻辑回归的 7 个显著变量建立 PSD 列线图模型。一致性统计量（C 指数）高达 0.937，ROC 分析中的曲线下面积（AUC）为 0.933。验证集中的复制分析结果表明 C 指数为 0.953，AUC 为 0.926。这表明该模型具有良好的校准和区分能力。
性别、Rankin 评分、高脂血症史、发病至住院时间、卒中部位、美国国立卫生研究院卒中量表（NIHSS）评分和 cg02550950 位点的甲基化水平与 PSD 的发生有关。利用这些信息，我们基于甲基化特征构建了预测模型。
