Université de Lorraine, Centre d'Investigation Clinique- Plurithématique Inserm CIC-P 1433, and Inserm U1116, CHRU Nancy Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France.
Department of Physiology, University of Porto, Porto, Portugal.
Clin Res Cardiol. 2020 Jan;109(1):22-33. doi: 10.1007/s00392-019-01480-4. Epub 2019 May 6.
Hypertension, obesity and diabetes are major and potentially modifiable "risk factors" for cardiovascular diseases. Identification of biomarkers specific to these risk factors may help understanding the underlying pathophysiological pathways, and developing individual treatment.
The FIBRO-TARGETS (targeting cardiac fibrosis for heart failure treatment) consortium has merged data from 12 patient cohorts in 1 common database of > 12,000 patients. Three mutually exclusive main phenotypic groups were identified ("cases"): (1) "hypertensive"; (2) "obese"; and (3) "diabetic"; age-sex matched in a 1:2 proportion with "healthy controls" without any of these phenotypes. Proteomic associations were studied using a biostatistical method based on LASSO and confronted with machine-learning and complex network approaches.
The case:control distribution by each cardiovascular phenotype was hypertension (50:100), obesity (50:98), and diabetes (36:72). Of the 86 studied proteins, 4 were found to be independently associated with hypertension: GDF-15, LEP, SORT-1 and FABP-2; 3 with obesity: CEACAM-8, LEP and PRELP; and 4 with diabetes: GDF-15, REN, CXCL-1 and SCF. GDF-15 (hypertension + diabetes) and LEP (hypertension + obesity) are shared by 2 different phenotypes. A machine-learning approach confirmed GDF-15, LEP and SORT-1 as discriminant biomarkers for the hypertension group, and LEP plus PRELP for the obesity group. Complex network analyses provided insight on the mechanisms underlying these disease phenotypes where fibrosis may play a central role.
Patients with "mutually exclusive" phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses. Patients with "mutually exclusive" phenotypes (blue: obesity, hypertension and diabetes) display distinct protein bioprofiles (green: decreased expression; red: increased expression) that might underpin different biological pathways (orange arrow), potentially leading to fibrosis.
高血压、肥胖和糖尿病是心血管疾病的主要且潜在可改变的“风险因素”。鉴定出这些风险因素特有的生物标志物，可能有助于了解潜在的病理生理途径，并制定个体化的治疗方案。
FIBRO-TARGETS（针对心力衰竭治疗的心脏纤维化）联盟合并了来自 12 个患者队列的数据，这些队列在一个包含超过 12000 名患者的公共数据库中。三个相互排斥的主要表型组被确定为“病例”：（1）“高血压”；（2）“肥胖”；和（3）“糖尿病”；按年龄和性别以 1:2 的比例与没有任何这些表型的“健康对照”相匹配。使用基于 LASSO 的生物统计学方法研究蛋白质组学关联，并与机器学习和复杂网络方法进行对比。
每种心血管表型的病例：对照分布情况为高血压（50:100）、肥胖（50:98）和糖尿病（36:72）。在研究的 86 种蛋白质中，有 4 种被发现与高血压独立相关：GDF-15、LEP、SORT-1 和 FABP-2；3 种与肥胖相关：CEACAM-8、LEP 和 PRELP；4 种与糖尿病相关：GDF-15、REN、CXCL-1 和 SCF。GDF-15（高血压+糖尿病）和 LEP（高血压+肥胖）是两种不同表型共有的。一种机器学习方法证实 GDF-15、LEP 和 SORT-1 是高血压组的鉴别生物标志物，而 LEP 加 PRELP 是肥胖组的鉴别生物标志物。复杂网络分析提供了对这些疾病表型潜在机制的深入了解，其中纤维化可能起着核心作用。
“相互排斥”表型的患者表现出不同的生物特征，这些特征可能是不同生物学途径的基础，可能导致纤维化。与相互排斥的心血管表型相关的血浆蛋白生物标志物及其关联：FIBRO-TARGETS 病例对照分析。具有“相互排斥”表型的患者（蓝色：肥胖、高血压和糖尿病）表现出不同的蛋白质生物特征（绿色：表达降低；红色：表达增加），这些特征可能是不同生物学途径的基础（橙色箭头），可能导致纤维化。
