Seyednasrollah Fatemeh, Mäkelä Johanna, Pitkänen Niina, Juonala Markus, Hutri-Kähönen Nina, Lehtimäki Terho, Viikari Jorma, Kelly Tanika, Li Changwei, Bazzano Lydia, Elo Laura L, Raitakari Olli T
From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.).
Circ Cardiovasc Genet. 2017 Jun;10(3). doi: 10.1161/CIRCGENETICS.116.001554.
Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods.
A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, <0.0001) and validation data (AUC=0.769 versus AUC=0.747, =0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, <0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, =0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index.
WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.
肥胖是心血管疾病的已知危险因素。肥胖的早期预测对于预防至关重要。本研究的目的是评估使用机器学习方法，利用儿童临床因素和遗传危险因素预测成年期肥胖的情况。
来自芬兰青年心血管风险研究（YFS）的2262名参与者从儿童期（3至18岁）到成年期进行了31年的随访。数据分为训练集（n = 1625）和验证集（n = 637）。使用增强机器学习技术，将已知遗传危险因素（97个单核苷酸多态性）的效应作为所有97个单核苷酸多态性的加权遗传风险评分（WGRS97）或19个最显著单核苷酸多态性的子集（WGRS19）进行研究。使用来自博加卢萨心脏研究（BHS）的外部数据（n = 369）对WGRS97和WGRS19进行验证。WGRS19提高了训练中预测成年期肥胖的准确性（曲线下面积[AUC]=0.787对AUC = 0.744，P<0.0001）以及验证数据中的准确性（AUC = 0.769对AUC = 0.747，P = 0.026）。WGRS97提高了训练中的准确性（AUC = 0.782对AUC = 0.744，P<0.0001），但在验证数据中未提高（AUC = 0.749对AUC = 0.747，P = 0.785）。较高的WGRS19与9岁时较高的体重指数相关，WGRS97与6岁时较高的体重指数相关。在BHS中的重复研究证实了我们的发现，即WGRS19和WGRS97与体重指数相关。
WGRS19提高了对成年期肥胖的预测能力。预测准确性在幼儿（3至6岁）中最高，而在大龄儿童（9至18岁）中，可以使用儿童临床因素识别风险。该模型有助于筛查有肥胖高风险的儿童。
