Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Cells. 2024 Jul 24;13(15):1240. doi: 10.3390/cells13151240.
Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as "extracellular lipid droplets", which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.
脂肪组织是一个具有深远影响的动态调节器官,对患者的整体健康有着重要的影响。然而,人类脂肪组织存在广泛而复杂的不一致性,包括细胞大小、脂质含量、炎症、细胞外基质成分、力学和细胞因子分泌等方面的巨大差异。鉴于人类的高度变异性,并且由于我们对脂肪组织的了解大多来自动物模型,我们试图建立人类脂肪组织的生物学、力学和流行病学特性之间的相关性和模式。为此,我们对 20 名患者的 26 个独立变量进行了编目,其中包括患者人口统计学特征和导致健康、肥胖和纤维化的因素。使用混合数据的因子分析(FAMD)来分析数据集(BMI>25)中的模式,使用相关矩阵来识别定量变量之间的相互作用。血管内皮生长因子 A(VEGFA)和肌动蛋白α 2,平滑肌(ACTA2)基因表达是 FAMD 的前两个维度的最高负荷。脂肪细胞的数量也是患者相关差异的关键驱动因素,脂肪细胞密度的降低与衰老有关。衰老还与皮下组织总脂质百分比的降低有关,脂质沉积有利于细胞外,转化生长因子-β1(TGFβ1)增加,M1 巨噬细胞极化增加。一个重要的发现是,自我认定的种族是导致本研究中患者之间差异的一个因素,黑人患者的 TGFβ1 和 ACTA2 基因表达水平明显较低。这一发现支持了在生物医学研究中迫切需要考虑患者的祖先,以便为所有患者制定更好的治疗策略。另一个重要的发现是,转化生长因子-β诱导因子同源盒 1(TGIF1),一种研究较少的信号分子,与瘦素信号高度相关,与代谢炎症相关。此外,这项研究引起了我们对“细胞外脂质滴”的关注,这些脂质滴在评估的肥胖脂肪组织的富含胶原蛋白的区域中始终存在。TGIF1 水平降低与细胞外脂质滴数量增加以及抑制脂肪组织纤维化改变的能力降低有关。最后,这项研究表明,BMI>25 的患者中,M1 和 M2 巨噬细胞标志物与瘦素相互关联。这一发现支持了越来越多的证据,即肥胖症中巨噬细胞极化涉及一个复杂的、相互关联的网络系统,而不是随着体重的增加,从 M2 向 M1 的完全激活模式转变。总的来说,这项研究强化了动物研究中的关键发现,并确定了未来研究的重要领域,其中人类和动物研究存在分歧。了解人类患者变异性的关键驱动因素对于揭示独特患者的复杂代谢健康状况是必要的。