Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Provincial Clinical Research Center for Rheumatic and Immunologic Diseases, Xiangya Hospital, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Department of Rheumatology and Immunology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Metabolism. 2023 Jul;144:155587. doi: 10.1016/j.metabol.2023.155587. Epub 2023 May 6.
Systemic sclerosis (SSc) is a chronic and systemic autoimmune disease marked by the skin and visceral fibrosis. Metabolic alterations have been found in SSc patients; however, serum metabolomic profiling has not been thoroughly conducted. Our study aimed to identify alterations in the metabolic profile in both SSc patients before and during treatment, as well as in mouse models of fibrosis. Furthermore, the associations between metabolites and clinical parameters and disease progression were explored.
High-performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS was performed in the serum of 326 human samples and 33 mouse samples. Human samples were collected from 142 healthy controls (HC), 127 newly diagnosed SSc patients without treatment (SSc baseline), and 57 treated SSc patients (SSc treatment). Mouse serum samples were collected from 11 control mice (NaCl), 11 mice with bleomycin (BLM)-induced fibrosis and 11 mice with hypochlorous acid (HOCl)-induced fibrosis. Both univariate analysis and multivariate analysis (orthogonal partial least-squares discriminate analysis (OPLS-DA)) were conducted to unravel differently expressed metabolites. KEGG pathway enrichment analysis was performed to characterize the dysregulated metabolic pathways in SSc. Associations between metabolites and clinical parameters of SSc patients were identified by Pearson's or Spearman's correlation analysis. Machine learning (ML) algorithms were applied to identify the important metabolites that have the potential to predict the progression of skin fibrosis.
The newly diagnosed SSc patients without treatment showed a unique serum metabolic profile compared to HC. Treatment partially corrected the metabolic changes in SSc. Some metabolites (phloretin 2'-O-glucuronide, retinoyl b-glucuronide, all-trans-retinoic acid, and betaine) and metabolic pathways (starch and sucrose metabolism, proline metabolism, androgen and estrogen metabolism, and tryptophan metabolism) were dysregulated in new-onset SSc, but restored upon treatment. Some metabolic changes were associated with treatment response in SSc patients. Metabolic changes observed in SSc patients were mimicked in murine models of SSc, indicating that they may reflect general metabolic changes associated with fibrotic tissue remodeling. Several metabolic changes were associated with SSc clinical parameters. The levels of allysine and all-trans-retinoic acid were negatively correlated, while D-glucuronic acid and hexanoyl carnitine were positively correlated with modified Rodnan skin score (mRSS). In addition, a panel of metabolites including proline betaine, phloretin 2'-O-glucuronide, gamma-linolenic acid and L-cystathionine were associated with the presence of interstitial lung disease (ILD) in SSc. Specific metabolites identified by ML algorithms, such as medicagenic acid 3-O-b-D-glucuronide, 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide, valproic acid glucuronide, have the potential to predict the progression of skin fibrosis.
Serum of SSc patients demonstrates profound metabolic changes. Treatment partially restored the metabolic changes in SSc. Moreover, certain metabolic changes were associated with clinical manifestations such as skin fibrosis and ILD, and could predict the progression of skin fibrosis.
系统性硬化症（SSc）是一种以皮肤和内脏纤维化为特征的慢性系统性自身免疫性疾病。已在 SSc 患者中发现代谢改变；然而，血清代谢组学分析尚未得到彻底开展。我们的研究旨在鉴定治疗前和治疗期间 SSc 患者以及纤维化小鼠模型中代谢谱的改变，此外，还探讨了代谢物与临床参数和疾病进展之间的关系。
采用高效液相色谱四极杆飞行时间质谱（HPLC-Q-TOF-MS）/MS 对 326 个人类样本和 33 个小鼠样本的血清进行分析。人类样本采集自 142 名健康对照（HC）、127 名未经治疗的新发 SSc 患者（SSc 基线）和 57 名接受治疗的 SSc 患者（SSc 治疗）。从 11 只对照小鼠（NaCl）、11 只博来霉素（BLM）诱导纤维化的小鼠和 11 只次氯酸（HOCl）诱导纤维化的小鼠中采集小鼠血清样本。采用单变量分析和多变量分析（正交偏最小二乘判别分析（OPLS-DA））揭示差异表达的代谢物。KEGG 通路富集分析用于描述 SSc 中失调的代谢途径。采用 Pearson 或 Spearman 相关性分析鉴定 SSc 患者代谢物与临床参数之间的关系。应用机器学习（ML）算法鉴定具有预测皮肤纤维化进展潜力的重要代谢物。
未经治疗的新发 SSc 患者与 HC 相比表现出独特的血清代谢谱。治疗部分纠正了 SSc 中的代谢变化。一些代谢物（根皮苷 2'-O-葡萄糖醛酸苷、视黄醇酰基β-葡萄糖醛酸、全反式视黄酸和甜菜碱）和代谢途径（淀粉和蔗糖代谢、脯氨酸代谢、雄激素和雌激素代谢以及色氨酸代谢）在新发病 SSc 中失调，但在治疗后得到恢复。一些代谢变化与 SSc 患者的治疗反应相关。在 SSc 患者中观察到的代谢变化在 SSc 小鼠模型中得到了模拟，表明它们可能反映与纤维化组织重塑相关的一般代谢变化。一些代谢变化与 SSc 临床参数相关。丙氨酸和全反式视黄酸的水平呈负相关，而 D-葡萄糖醛酸和己酰肉碱呈正相关与改良罗达恩皮肤评分（mRSS）相关。此外，包括脯氨酸甜菜碱、根皮苷 2'-O-葡萄糖醛酸苷、γ-亚麻酸和 L-胱硫醚在内的一组代谢物与 SSc 患者的间质性肺病（ILD）相关。ML 算法鉴定的特定代谢物，如马桑酸 3-O-β-D-葡萄糖醛酸、4'-O-甲基-(-)-表儿茶素-3'-O-β-葡萄糖醛酸、丙戊酸葡萄糖醛酸，具有预测皮肤纤维化进展的潜力。
SSc 患者的血清显示出明显的代谢变化。治疗部分恢复了 SSc 中的代谢变化。此外，某些代谢变化与皮肤纤维化和 ILD 等临床表现相关，并可预测皮肤纤维化的进展。
