Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application.
Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application.
复制标题
至少绝对收缩和选择算子类型的方法，用于鉴定超重和肥胖的血清生物标志物：仿真和应用。
DOI：
10.1186/s12874-016-0254-8
复制
发表时间：
2016-11-14
影响因子：
4
通讯作者：
Guerra S
中科院分区：
文献类型：
--
作者：
Vasquez MM;Hu C;Roe DJ;Chen Z;Halonen M;Guerra S
由于通过多重技术测量这些生物标志物的进展，循环生物标志物及其与疾病结局的相关性的研究变得越来越复杂。最小绝对收缩和选择算子（LASSO）是可以用于在这些高维数据中选择生物标志物的数据分析方法。然而，目前尚不清楚在考虑血清生物标志物研究中可能存在的数据场景时，哪种LASSO类型的方法是优选的，例如生物标志物之间的高度相关性，与结果的弱关联，以及真实信号的稀疏数量。本研究的目的是比较LASSO的五个LASSO类型的方法，这些情况下。进行了一项模拟研究，以比较LASSO，自适应LASSO，弹性网络，迭代LASSO，Bootstrap增强LASSO和加权融合的二元逻辑回归模型。模拟研究旨在反映基于人群的图森气道阻塞性疾病流行病学研究（TESAOD）的数据结构，特别是样本量（总人群N = 1000，子分析N = 500），生物标志物的相关性（0.20，0.50，0.80），超重（40%）和肥胖（12%）的患病率结果，以及结果与标准化血清生物标志物浓度的相关性（对数比值比= 0.05 - 1.75）。然后将每种LASSO类型的方法应用于306名超重，66名肥胖和463名正常体重受试者的TESAOD数据，其中包括86种血清生物标志物。基于模拟研究，没有一种方法具有总体上级性能。加权融合正确地识别了更多的真实信号，但错误地包含了更多的噪声变量。LASSO和弹性网络正确识别了许多真实信号，并排除了更多的噪声变量。在应用研究中，通过所有方法选择的超重和肥胖的生物标志物为脂联素、载脂蛋白H、降钙素、CD14、补体3、C反应蛋白、铁蛋白、生长激素、免疫球蛋白M、白细胞介素-18、瘦素、单核细胞趋化蛋白-1、肌红蛋白、性激素结合球蛋白、表面活性蛋白D和YKL-40。对于所研究的数据情景，最佳LASSO型方法的选择是依赖于数据结构的，应该以研究目标为指导。LASSO型方法鉴定了已知与肥胖和肥胖相关病症相关的生物标志物。
The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios. A simulation study was performed to compare the LASSO, Adaptive LASSO, Elastic Net, Iterated LASSO, Bootstrap-Enhanced LASSO, and Weighted Fusion for the binary logistic regression model. The simulation study was designed to reflect the data structure of the population-based Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD), specifically the sample size (N = 1000 for total population, 500 for sub-analyses), correlation of biomarkers (0.20, 0.50, 0.80), prevalence of overweight (40%) and obese (12%) outcomes, and the association of outcomes with standardized serum biomarker concentrations (log-odds ratio = 0.05–1.75). Each LASSO-type method was then applied to the TESAOD data of 306 overweight, 66 obese, and 463 normal-weight subjects with a panel of 86 serum biomarkers. Based on the simulation study, no method had an overall superior performance. The Weighted Fusion correctly identified more true signals, but incorrectly included more noise variables. The LASSO and Elastic Net correctly identified many true signals and excluded more noise variables. In the application study, biomarkers of overweight and obesity selected by all methods were Adiponectin, Apolipoprotein H, Calcitonin, CD14, Complement 3, C-reactive protein, Ferritin, Growth Hormone, Immunoglobulin M, Interleukin-18, Leptin, Monocyte Chemotactic Protein-1, Myoglobin, Sex Hormone Binding Globulin, Surfactant Protein D, and YKL-40. For the data scenarios examined, choice of optimal LASSO-type method was data structure dependent and should be guided by the research objective. The LASSO-type methods identified biomarkers that have known associations with obesity and obesity related conditions.
登录
查看更多内容
影响因子：
5.8
作者：
Esposito, K;Pontillo, A;Giugliano, D
通讯作者：
Giugliano, D
影响因子：
5
作者：
LEBOWITZ, MD;KNUDSON, RJ;BURROWS, B
通讯作者：
BURROWS, B
影响因子：
2.6
作者：
Avalos, Marta;Orriols, Ludivine;Lagarde, Emmanuel
通讯作者：
Lagarde, Emmanuel
影响因子：
4.5
作者：
Fan, Jianqing;Fan, Yingying
通讯作者：
Fan, Yingying
影响因子：
3.2
作者：
KOPELMAN, PG;PILKINGTON, TRE;JEFFCOATE, SL
通讯作者：
JEFFCOATE, SL