Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Department of Nutritional Sciences, The Pennsylvania State University, University Park, United States of America.
PLoS One. 2018 Sep 6;13(9):e0202665. doi: 10.1371/journal.pone.0202665. eCollection 2018.
The high prevalence of metabolic syndrome (MetS) and cardiovascular diseases (CVD) is observed among Kazakhs in Xinjiang. Because MetS may significantly predict the occurrence of CVD, the inclusion of CVD-related indicators in metabolic network may improve the predictive ability for a CVD-risk model for Kazakhs in Xinjiang.
The study included 2,644 subjects who were followed for 5 years or longer. CVD cases were identified via medical records of the local hospitals from April 2016 to August 2017. Factor analysis was performed in 706 subjects (267 men and 439 women) with MetS to extract CVD-related potential factors from 18 biomarkers tested in a routine health check-up, served as a synthetic predictor (SP). We evaluated the predictive ability of the CVD-risk model using age and SP, logistic regression discrimination for internal validation (n = 384; men = 164, women = 220) and external validation (n = 219; men = 89, women = 130), calculated the probability of CVD for each participant, and receiver operating characteristic curves.
According to the diagnostic criteria of JIS, the prevalence of MetS in Kazakh was 30.9%. Seven potential factors with a similar pattern were obtained from men and women and comprised the CVD predictors. When predicting CVD in the internal validation, the area under the curve (AUC) were 0.857 (95%CI 0.807-0.898) for men and 0.852 (95%CI 0.809-0.889) for women, respectively. In the external validation, the AUC to predict CVD were 0.914 (95%CI 0.832-0.963) for men and 0.848 (95%CI 0.774-0.905) for women. It is suggested that SP might serve as a useful tool in identifying CVD with in Kazakhs, especially for Kazakhs men.
Among 7 potential factors were extracted from 18 biomarkrs in Kazakhs with MetS, and SP may be used for CVD risk assessment.
代谢综合征（MetS）和心血管疾病（CVD）在新疆的哈萨克族中患病率较高。由于代谢综合征可能显著预测 CVD 的发生，因此将 CVD 相关指标纳入代谢网络可能会提高针对新疆哈萨克族 CVD 风险模型的预测能力。
本研究纳入了 2644 名随访时间超过 5 年的受试者。2016 年 4 月至 2017 年 8 月，通过当地医院的病历记录确定 CVD 病例。对 706 名（267 名男性和 439 名女性）患有 MetS 的受试者进行因子分析，从常规体检中检测的 18 种生物标志物中提取与 CVD 相关的潜在因素作为综合预测因子（SP）。我们使用年龄和 SP 评估 CVD 风险模型的预测能力，通过逻辑回归进行内部验证（n = 384；男性 164 人，女性 220 人）和外部验证（n = 219；男性 89 人，女性 130 人），计算每位参与者的 CVD 概率，并绘制接收者操作特征曲线。
根据 JIS 的诊断标准，哈萨克族的 MetS 患病率为 30.9%。从男性和女性中获得了 7 个具有相似模式的潜在因素，构成了 CVD 预测因子。在内部验证中，男性和女性的曲线下面积（AUC）分别为 0.857（95%CI 0.807-0.898）和 0.852（95%CI 0.809-0.889）。在外部验证中，男性和女性预测 CVD 的 AUC 分别为 0.914（95%CI 0.832-0.963）和 0.848（95%CI 0.774-0.905）。这表明 SP 可能是识别哈萨克族人群 CVD 的有用工具，尤其是对于哈萨克族男性。
从患有 MetS 的哈萨克族中提取的 18 种生物标志物中提取了 7 个潜在因素，SP 可用于 CVD 风险评估。
