Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study.
Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study.
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DOI：
10.1002/jcsm.12733
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Thorand B
中科院分区：
文献类型：
--
作者：
Huemer MT;Bauer A;Petrera A;Scholz M;Hauck SM;Drey M;Peters A;Thorand B
低肌肉质量和高脂肪质量的共存，这两种相互关联的条件与健康状况下降密切相关，其特征在于只有少数蛋白质生物标志物。高通量蛋白质组学能够同时测量多种蛋白质，促进潜在的新生物标志物的发现。数据来源于前瞻性基于人群的奥格斯堡地区合作健康研究S4/FF 4队列研究（中位随访时间：13.5年），在横断面分析中纳入了1478名年龄在55-74岁之间的受试者（756名男性和722名女性），在纵向分析中纳入了608名受试者（315名男性和293名女性）。通过基线和随访时的生物电阻抗分析确定了肩胛骨骨骼肌质量（ASMM）和体脂质量指数（BFMI）。在基线时，使用邻近延伸测定法测量233种血浆蛋白。我们实施了具有稳定性选择的增强，以实现假阳性控制变量选择，以识别低肌肉质量，高脂肪质量及其组合的新蛋白质生物标志物。我们通过交叉验证的曲线下面积（AUC）评估了基于组最小绝对收缩和选择算子（lasso）开发的预测模型，以研究蛋白质是否在经典风险因素的基础上提高预测准确性。在横断面分析中，我们确定了激肽释放酶-6、C-C基序趋化因子28（CCL 28）和组织因子途径抑制剂是以前未知的肌肉质量生物标志物[与低ASMM相关：log 2标准化蛋白表达值每增加1-SD的比值比（OR）]。（95%置信区间（CI））：分别为1.63（1.37-1.95）、1.31（1.14-1.51）、1.24（1.06-1.45）]和丝氨酸蛋白酶27 [与高BFMI相关：OR（95% CI）：0.73（0.61-0.86）]。CCL 28和金属蛋白酶抑制剂4（TIMP 4）构成了低肌肉和高脂肪质量组合的新生物标志物[与低ASMM组合高BFMI的相关性：OR（95%CI）分别为：1.32（1.08-1.61），1.28（1.03-1.59）]。在经典风险因素之上，纳入在≥90%的组lasso bootstrap迭代中选择的蛋白质生物标志物改善了预测低ASMM、高BFMI及其组合的模型的性能[Δ AUC（95% CI）：分别为0.16（0.13-0.20）、0.22（0.18-0.25）、0.12（0.08-0.17）]。在纵向分析中，N-末端脑钠肽原（NT-proBNP）是14年内ASMM丢失和ASMM丢失合并BFMI增加的唯一蛋白质[OR（95% CI）：分别为1.40（1.10-1.77），1.60（1.15-2.24）]。蛋白质组学分析显示，CCL 28和TIMP 4是低肌肉质量结合高脂肪质量的新生物标志物，NT‐proBNP是肌肉质量损失结合脂肪质量增加的关键生物标志物。蛋白质组学使我们能够加速肌肉研究中的生物标志物发现。
The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High‐throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers. Data derived from the prospective population‐based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow‐up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55–74 years in the cross‐sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow‐up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives‐controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross‐validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. In the cross‐sectional analysis, we identified kallikrein‐6, C‐C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1‐SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37–1.95), 1.31 (1.14–1.51), 1.24 (1.06–1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61–0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08–1.61), 1.28 (1.03–1.59), respectively]. Including protein biomarkers selected in ≥90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13–0.20), 0.22 (0.18–0.25), 0.12 (0.08–0.17), respectively]. In the longitudinal analysis, N‐terminal prohormone brain natriuretic peptide (NT‐proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10–1.77), 1.60 (1.15–2.24), respectively]. Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT‐proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.
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影响因子：
24
作者：
Pang Y;Kartsonaki C;Lv J;Fairhurst-Hunter Z;Millwood IY;Yu C;Guo Y;Chen Y;Bian Z;Yang L;Chen J;Clarke R;Walters RG;Holmes MV;Li L;Chen Z
通讯作者：
Chen Z
DOI：
10.1016/j.clnu.2016.03.028
发表时间：
2016-12
期刊：
Clinical nutrition (Edinburgh, Scotland)
影响因子：
--
作者：
Batsis JA;Mackenzie TA;Jones JD;Lopez-Jimenez F;Bartels SJ
通讯作者：
Bartels SJ
影响因子：
3
作者：
Hofner B;Boccuto L;Göker M
通讯作者：
Göker M
影响因子：
2.1
作者：
Chowdary, Pratima
通讯作者：
Chowdary, Pratima
影响因子：
3.6
作者：
Krysztofiak H;Wleklik M;Migaj J;Dudek M;Uchmanowicz I;Lisiak M;Kubielas G;Straburzyńska-Migaj E;Lesiak M;Kałużna-Oleksy M
通讯作者：
Kałużna-Oleksy M