Pitti Erica, Vanni Domitilla, Viceconte Nicola, Lembo Angelo, Tanzilli Gaetano, Raparelli Valeria, Petrella Greta, Cicero Daniel O
Department of Chemical Science and Technology, University of Rome "Tor Vergata," 00133 Rome, Italy.
Department of Cardiovascular, Respiratory, Nephrological, Anesthesiologic and Geriatric Sciences, Sapienza University of Rome, Policlinic Umberto I, 00161 Rome, Italy.
J Endocr Soc. 2024 Sep 11;8(10):bvae152. doi: 10.1210/jendso/bvae152. eCollection 2024 Aug 27.
Metabolomics is becoming increasingly popular for detecting markers that indicate the presence of a specific disease. However, it is usually applied to studying individual ailments, yielding results that may not be directly relevant to people with multiple health conditions.
Our study proposes a different approach to explore metabolic crosstalk between various disease states.
We conducted a study on subjects at medium to high risk of developing coronary artery disease. We measured the plasma levels of 83 metabolites using nuclear magnetic resonance and analyzed the connections between these metabolites and various risk factors such as diabetes, hypertension, and dyslipidemia. Linear regression and multivariate analysis were combined for this purpose.
Inspection of the metabolic maps created by our analysis helped us efficiently compare profiles. In this way, it was possible to discover opposing metabolic features among single conditions and their combination. Furthermore, we found compensating metabolic effects between diabetes, hypertension, and dyslipidemia involving mainly ketone body metabolism and fatty acid β-oxidation.
Our study introduces a novel approach to investigating how metabolism reacts to the simultaneous presence of multiple health conditions. This has allowed the detection of potential compensatory effects between diabetes, hypertension, and dyslipidemia, highlighting the complexity of metabolic crosstalk in patients with comorbidities. A better understanding of metabolic crosstalk like this could aid in developing focused treatments, resulting in improved therapeutic results.
代谢组学在检测指示特定疾病存在的标志物方面越来越受欢迎。然而，它通常用于研究个体疾病，得出的结果可能与患有多种健康问题的人没有直接关联。
我们的研究提出了一种不同的方法来探索各种疾病状态之间的代谢相互作用。
设计、地点和患者：我们对有中度至高度冠心病发病风险的受试者进行了一项研究。我们使用核磁共振测量了83种代谢物的血浆水平，并分析了这些代谢物与糖尿病、高血压和血脂异常等各种风险因素之间的联系。为此，我们结合了线性回归和多变量分析。
检查我们分析创建的代谢图谱有助于我们有效地比较概况。通过这种方式，有可能发现单一病症及其组合之间相反的代谢特征。此外，我们发现糖尿病、高血压和血脂异常之间存在代偿性代谢效应，主要涉及酮体代谢和脂肪酸β氧化。
我们的研究引入了一种新方法来研究代谢如何对多种健康状况同时存在做出反应。这使得能够检测糖尿病、高血压和血脂异常之间潜在的代偿效应，突出了合并症患者代谢相互作用的复杂性。像这样更好地理解代谢相互作用有助于开发针对性的治疗方法，从而提高治疗效果。
