Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Department of Biochemistry, Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
Metabolomics. 2023 Aug 9;19(8):72. doi: 10.1007/s11306-023-02035-5.
Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.
Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.
Insulin Resistance Atherosclerosis Family Study (IRASFS).
Community based.
Mexican Americans (MA) and African Americans (AA).
Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR.
Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r = 0.77) and AA (r = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results.
We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.
胰岛素抵抗与多种复杂疾病有关；然而，精确测量胰岛素抵抗的方法具有侵入性、昂贵且耗时。
从代谢组学数据中开发胰岛素抵抗测量指标（包括胰岛素敏感指数 [SI] 和稳态模型评估的胰岛素抵抗 [HOMA-IR]）的估算模型。
胰岛素抵抗动脉粥样硬化家族研究（IRASFS）。
基于社区。
墨西哥裔美国人（MA）和非裔美国人（AA）。
胰岛素抵抗测量指标（即 SI 和 HOMA-IR）的估算模型。
最小绝对收缩和选择算子（LASSO）和弹性网络回归用于从 1274 种代谢物结合临床数据（例如年龄、性别、体重指数 [BMI]）构建胰岛素抵抗估算模型。代谢物数据使用三种方法进行转换，即反正态转换、标准化和 Box-Cox 转换。分析在一个 MA 招募地点（科罗拉多州圣路易斯谷（SLV）；N=450）进行，并在另一个 MA 招募地点（德克萨斯州圣安东尼奥（SA）；N=473）进行测试。此外，将这两个 MA 招募地点合并，并在 AA 招募样本（加利福尼亚州洛杉矶；N=495）中测试估算模型。在 SA（r=0.77）和 AA（r=0.74）测试数据集中，估计的和经验的 SI 相关。此外，估计的和经验的 SI 与 BMI、低密度脂蛋白胆固醇（LDL）和甘油三酯一致相关。我们应用类似的方法来估计 HOMA-IR，结果类似。
我们已经开发出一种使用代谢组学数据估算胰岛素抵抗的方法，该方法有可能应用于广泛的生物医学研究和疾病。
