Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Finnish Cardiovascular Research Centre, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Finnish Cardiovascular Research Centre, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland.
Bone. 2020 Feb;131:115160. doi: 10.1016/j.bone.2019.115160. Epub 2019 Nov 21.
Studies have shown that osteoporosis and atherosclerosis are comorbid conditions sharing common risk factors and pathophysiological mechanisms. Understanding these is crucial in order to develop shared methods for risk stratification, prevention, diagnosis and treatment. The aim of this study was to apply a system-level bioinformatics approach to lipidome-wide data in order to pinpoint the lipidomic architecture jointly associated with surrogate markers of these complex comorbid diseases.
The study was based on the Cardiovascular Risk in Young Finns Study cohort from the 2007 follow-up (n = 1494, aged 30-45 years, women: 57%). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyse the serum lipidome, involving 437 molecular lipid species. The subclinical osteoporotic markers included indices of bone mineral density and content, measured using peripheral quantitative computer tomography from the distal and shaft sites of both the tibia and the radius. The subclinical atherosclerotic markers included carotid and bulbus intima media thickness measured with high-resolution ultrasound. Weighted co-expression network analysis was performed to identify networks of densely interconnected lipid species (i.e. lipid modules) associated with subclinical markers of both osteoporosis and atherosclerosis. The levels of lipid species (lipid profiles) of each of the lipid modules were summarized by the first principal component termed as module eigenlipid. Then, Pearson's correlation (r) was calculated between the module eigenlipids and the markers. Lipid modules that were significantly and jointly correlated with subclinical markers of both osteoporosis and atherosclerosis were considered to be related to the comorbidities. The hypothesis that the eigenlipids and profiles of the constituent lipid species in the modules have joint effects on the markers was tested with multivariate analysis of variance (MANOVA).
Among twelve studied molecular lipid modules, we identified one module with 105 lipid species significantly and jointly associated with both subclinical markers of both osteoporosis (r = 0.24, p-value = 2 × 10) and atherosclerosis (r = 0.16, p-value = 2 × 10). The majority of the lipid species in this module belonged to the glycerolipid (n = 60), glycerophospholipid (n = 13) and sphingolipid (n = 29) classes. The module was also enriched with ceramides (n = 20), confirming their significance in cardiovascular outcomes and suggesting their joint role in the comorbidities. The top three of the 37 statistically significant (adjusted p-value < 0.05) lipid species jointly associated with subclinical markers of both osteoporosis and atherosclerosis within the module were all triacylglycerols (TAGs) - TAG(18:0/18:0/18:1) with an adjusted p-value of 8.6 × 10, TAG(18:0/18:1/18:1) with an adjusted p-value of 3.7 × 10, and TAG(16:0/18:0/18:1) with an adjusted p-value of 8.5 × 10.
This study identified a novel lipid module associated with both surrogate markers of both subclinical osteoporosis and subclinical atherosclerosis. Alterations in the metabolism of the identified lipid module and, more specifically, the TAG related molecular lipids within the module may provide potential new biomarkers for testing the comorbidities, opening avenues for the emergence of dual-purpose prevention measures.
研究表明,骨质疏松症和动脉粥样硬化是共病状态,具有共同的风险因素和病理生理机制。为了开发共同的风险分层、预防、诊断和治疗方法,了解这些因素至关重要。本研究旨在应用系统水平的生物信息学方法对脂质组学数据进行分析,以确定与这些复杂共病的替代标志物共同相关的脂质组学结构。
该研究基于 2007 年随访的年轻芬兰人研究队列(n=1494,年龄 30-45 岁,女性:57%)。采用液相色谱-串联质谱法(LC-MS/MS)分析血清脂质组,涉及 437 种分子脂质。亚临床骨质疏松标志物包括使用胫骨和桡骨远端和骨干的外周定量计算机断层扫描测量的骨矿物质密度和含量指数。亚临床动脉粥样硬化标志物包括使用高分辨率超声测量的颈动脉和球部内中膜厚度。进行加权共表达网络分析,以识别与亚临床骨质疏松和动脉粥样硬化标志物共同相关的密集相互连接的脂质物种(即脂质模块)网络。通过称为模块特征脂质的第一主成分来总结每个脂质模块的脂质物种(脂质谱)。然后,计算模块特征脂质与标志物之间的 Pearson 相关系数(r)。与亚临床骨质疏松和动脉粥样硬化标志物显著且共同相关的脂质模块被认为与共病有关。通过多变量方差分析(MANOVA)检验模块中组成脂质物种的特征脂质和谱对标志物的联合效应的假设。
在研究的 12 个分子脂质模块中,我们确定了一个包含 105 种脂质物种的模块,这些脂质物种与亚临床骨质疏松(r=0.24,p 值=2×10)和动脉粥样硬化标志物(r=0.16,p 值=2×10)均显著相关。该模块中的大多数脂质物种属于甘油磷脂(n=60)、甘油磷脂(n=13)和鞘脂(n=29)类。该模块还富含神经酰胺(n=20),证实了它们在心血管结局中的重要性,并表明它们在共病中的共同作用。该模块中与亚临床骨质疏松和动脉粥样硬化标志物均显著相关的 37 种统计学上显著(调整后 p 值<0.05)脂质物种中排名前三位的均为三酰基甘油(TAG)-TAG(18:0/18:0/18:1),调整后 p 值为 8.6×10,TAG(18:0/18:1/18:1),调整后 p 值为 3.7×10,TAG(16:0/18:0/18:1),调整后 p 值为 8.5×10。
本研究确定了一个与亚临床骨质疏松和亚临床动脉粥样硬化替代标志物均相关的新型脂质模块。所鉴定的脂质模块代谢的改变,特别是模块内与 TAG 相关的分子脂质的改变,可能为共病的检测提供潜在的新生物标志物,为双重用途预防措施的出现开辟了途径。